Casanovo

algorithm · Transformer (AR)

Casanovo: algorithm · Transformer (AR). First Transformer

First Transformer

Kind	algorithm
Deep learning	yes
Acquisition	DDA
Family	Transformer (AR)

Code

https://github.com/Noble-Lab/casanovo

Live stars, open issues and last-push figures are on the Code activity chart.

Checkpoints

Version	Trained on	Host	Licence	Size	Checked
nine-species	—	Zenodo archival	CC-BY-4.0	5.1 GB	live 2026-10-02
data set and weights	—	Zenodo archival	Apache-2.0	3.9 GB	live 2026-10-02
MassIVE-KB splits	—	institutional	not stated	—	live 2026-10-02
v4.0.0	MassIVE-KB v1	GitHub release	Apache-2.0	—	live 2026-10-02
v4.2.0	~2M PSMs from MassIVE-KB v1 + v2.0.15	GitHub release	Apache-2.0	—	live 2026-10-02
v5.0.0	—	GitHub release	Apache-2.0	—	live 2026-10-02
v5.2.0	the default –model orbitrap selector from v5.2.0 onward	GitHub release	Apache-2.0	—	live 2026-10-02
v5.2.0	the –model timstof selector from v5.2.0 onward	GitHub release	Apache-2.0	575 MB	live 2026-10-06

A host marked archival has a DOI and keeps what it is given. The others can move or disappear, which is why they are checked rather than merely listed. verified means the bytes were fetched and hashed on the date shown; live means only that the host answered when last asked.

Data

What the papers describing this method trained it on and reported its results on, as each paper states it. A version is named only where the paper names one.

Benchmarks

denovo_benchmarks: median peptide-level average precision 0.777 over 86 datasets, median rank 7 of 15 (version 5.0.0).
ProteoBench, on the nine-species benchmark, ProteoBench selection: peptide-level AUC 0.901; precision 0.650 at 100% coverage; amino-acid AUC 0.951 (version 4.0, beam search, submitted 2026-08-07).

Both are mass-based matches on the tool’s most recent run. What these numbers mean.

Reported comparison

The comparison table this method’s own papers print, standardised: every value on a 0-1 scale, methods down the side, the measure and then the species across. These are numbers papers report about themselves and their baselines. They are not a leaderboard, and they do not compare across tables: each was produced by a different group, on the dataset named in its corner, with each baseline either retrained, run from released weights or quoted from another paper. Where the paper says which, it follows the method’s name (hover it for the sentence); most papers do not say. Bold is the best value in a column and underline the runner-up, our ranking rather than the paper’s own marks.

Table2

De novo mass spectrometry peptide sequencing with a transformer model, page 6: Empirical comparison of Casanovo, DeepNovo and PointNovo. The table lists the peptide-level and amino acid-level precision of three competing models and coverage of Casanovo with precursor m/z filtering on all nine benchmark cross-validation folds. Each fold’s test set contains spectra from a single species, with nearly disjoint sets of peptides between species. For cross-validation folds corresponding to mouse and human, five models were trained with different random initializations. For these species, we report standard deviation of the performance measures.

Nine-species benchmark	Peptide precision
Nine-species benchmark	Mus musculus	Homo sapiens	Saccharomyces cerevisiae	Methanosarcina mazei	Apis mellifera	Solanum lycopersicum	Vigna mungo	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes
DeepNovo · released	0.286	0.293	0.462	0.422	0.330	0.454	0.436	0.449	0.253
PointNovo · quoted	0.355	0.351	0.534	0.478	0.396	0.513	0.511	0.518	0.298
Casanovo	0.665	0.683	0.824	0.771	0.732	0.771	0.798	0.805	0.695

Nine-species benchmark	Peptide coverage
Nine-species benchmark	Mus musculus	Homo sapiens	Saccharomyces cerevisiae	Methanosarcina mazei	Apis mellifera	Solanum lycopersicum	Vigna mungo	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes
Casanovo	0.666	0.537	0.681	0.630	0.557	0.557	0.547	0.671	0.534

Nine-species benchmark	Peptide precision at coverage 1
Nine-species benchmark	Mus musculus	Homo sapiens	Saccharomyces cerevisiae	Methanosarcina mazei	Apis mellifera	Solanum lycopersicum	Vigna mungo	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes
Casanovo	0.443	0.367	0.561	0.486	0.408	0.460	0.437	0.540	0.371

Nine-species benchmark	Amino acid precision
Nine-species benchmark	Mus musculus	Homo sapiens	Saccharomyces cerevisiae	Methanosarcina mazei	Apis mellifera	Solanum lycopersicum	Vigna mungo	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes
DeepNovo · released	0.623	0.610	0.750	0.694	0.630	0.731	0.679	0.742	0.602
PointNovo · quoted	0.626	0.606	0.779	0.712	0.644	0.733	0.730	0.768	0.589
Casanovo	0.899	0.898	0.952	0.935	0.920	0.929	0.920	0.943	0.908

Nine-species benchmark	Amino acid precision at coverage 1
Nine-species benchmark	Mus musculus	Homo sapiens	Saccharomyces cerevisiae	Methanosarcina mazei	Apis mellifera	Solanum lycopersicum	Vigna mungo	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes
Casanovo	0.562	0.424	0.591	0.518	0.461	0.471	0.442	0.573	0.405

Compared against in other papers (24)

95 tables from 24 papers that report this method as a baseline, newest first. The caveats of the comparisons above hold twice over: each paper ran, retrained or quoted this method its own way, so its number here is that paper’s claim, not a measurement of the method. Where a paper says which it did, the word follows the method’s name.

ScopeNovo: a De Novo Peptide Sequencing Method Using Spectrum-Spectrum Similarity to Refine the Prediction Accuracy (2026) · 2 tables

Table 1 (Nine-species · revised (build not stated))

ScopeNovo: a De Novo Peptide Sequencing Method Using Spectrum-Spectrum Similarity to Refine the Prediction Accuracy, page 5: Comparison of de novo peptide sequencing methods on 9-species-V1 and 9-species-V2 benchmarks. For ScopeNovo we report two operating regimes: Setting 1 (External Library, spectrum-consistency reranking against a curated external spectral library), which characterizes the upper-bound performance attainable when high-quality external spectral resources are available; and Setting 2 (Self-Constructed Library), in which the reference library is bootstrapped entirely from high-confidence in-batch de novo predictions on the test data itself, placing ScopeNovo under the same resource assumptions as the baselines and yielding a direct comparison. In each column, the best value among the baselines and ScopeNovo (Setting 2) is in bold; ScopeNovo (Setting 1) is reported separately for reference and excluded from the bold comparison.

Nine-species benchmark revised (build not stated)	Amino acid precision
Nine-species benchmark revised (build not stated)	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Apis mellifera	Homo sapiens	Methanosarcina mazei	Mus musculus	Vigna mungo	Solanum lycopersicum	Saccharomyces cerevisiae	Average
Casanovo	0.923	0.787	0.776	0.856	0.828	0.806	0.854	0.874	0.865	0.881
Casanovo V2	0.843	0.799	0.788	0.865	0.836	0.813	0.867	0.882	0.875	0.865
InstaNovo	0.852	0.751	0.799	0.847	0.848	0.634	0.894	0.864	0.877	0.850
ContraNovo	0.863	0.802	0.817	0.909	0.864	0.830	0.903	0.881	0.883	0.865
AdaNovo	0.792	0.816	0.756	0.800	0.804	0.757	0.759	0.831	0.801	0.794
RankNovo	0.836	0.836	0.891	0.836	0.879	0.920	0.896	0.893	0.879	0.880
π-PrimeNovo	0.874	0.806	0.824	0.870	0.869	0.808	0.921	0.885	0.891	0.866
ScopeNovo Setting 1	0.885	0.822	0.839	0.884	0.882	0.841	0.931	0.897	0.900	0.885
ScopeNovo Setting 2	0.877	0.814	0.829	0.879	0.874	0.821	0.926	0.889	0.892	0.868

Nine-species benchmark revised (build not stated)	Peptide accuracy
Nine-species benchmark revised (build not stated)	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Apis mellifera	Homo sapiens	Methanosarcina mazei	Mus musculus	Vigna mungo	Solanum lycopersicum	Saccharomyces cerevisiae	Average
Casanovo	0.687	0.522	0.579	0.662	0.656	0.522	0.645	0.730	0.738	0.682
Casanovo V2	0.693	0.532	0.587	0.670	0.661	0.531	0.654	0.737	0.743	0.683
InstaNovo	0.704	0.512	0.625	0.670	0.666	0.504	0.679	0.721	0.758	0.698
ContraNovo	0.709	0.533	0.615	0.759	0.698	0.567	0.722	0.729	0.725	0.701
AdaNovo	0.636	0.624	0.597	0.590	0.641	0.472	0.538	0.693	0.653	0.635
RankNovo	0.713	0.603	0.617	0.717	0.712	0.559	0.744	0.724	0.680	0.682
π-PrimeNovo	0.712	0.572	0.612	0.710	0.714	0.548	0.766	0.719	0.684	0.697
ScopeNovo Setting 1	0.748	0.664	0.669	0.762	0.772	0.664	0.816	0.771	0.702	0.737
ScopeNovo Setting 2	0.728	0.619	0.641	0.750	0.743	0.611	0.788	0.745	0.691	0.716

The paper bolds the best of the baselines and ScopeNovo (Setting 2) only: 'ScopeNovo (Setting 1) is reported separately for reference and excluded from the bold comparison.' Bold and underline here rank every row, Setting 1 included; the paper's own marks were checked against its rule, and match it.

Table 1 (Nine-species · original (DeepNovo, 2017))

Nine-species benchmark original (DeepNovo, 2017)	Amino acid precision
Nine-species benchmark original (DeepNovo, 2017)	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Apis mellifera	Homo sapiens	Methanosarcina mazei	Mus musculus	Vigna mungo	Solanum lycopersicum	Saccharomyces cerevisiae	Average
Casanovo	0.788	0.676	0.708	0.676	0.754	0.765	0.761	0.792	0.756	0.743
Casanovo V2	0.801	0.687	0.721	0.687	0.767	0.774	0.768	0.802	0.768	0.754
InstaNovo	0.802	0.656	0.718	0.691	0.756	0.742	0.794	0.774	0.767	0.745
ContraNovo	0.828	0.700	0.764	0.778	0.798	0.803	0.804	0.812	0.787	0.785
AdaNovo	0.858	0.798	0.767	0.690	0.804	0.778	0.878	0.818	0.791	0.798
RankNovo	0.807	0.771	0.880	0.784	0.838	0.823	0.876	0.846	0.828	0.827
π-PrimeNovo	0.840	0.715	0.767	0.725	0.799	0.797	0.823	0.822	0.795	0.789
ScopeNovo Setting 1	0.849	0.715	0.778	0.729	0.811	0.809	0.816	0.825	0.796	0.797
ScopeNovo Setting 2	0.844	0.722	0.774	0.732	0.806	0.809	0.829	0.828	0.798	0.795

Nine-species benchmark original (DeepNovo, 2017)	Peptide accuracy
Nine-species benchmark original (DeepNovo, 2017)	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Apis mellifera	Homo sapiens	Methanosarcina mazei	Mus musculus	Vigna mungo	Solanum lycopersicum	Saccharomyces cerevisiae	Average
Casanovo	0.621	0.437	0.494	0.446	0.568	0.485	0.593	0.622	0.601	0.551
Casanovo V2	0.630	0.445	0.504	0.455	0.579	0.493	0.600	0.629	0.607	0.559
InstaNovo	0.652	0.459	0.535	0.515	0.595	0.525	0.635	0.635	0.618	0.582
ContraNovo	0.688	0.486	0.580	0.625	0.633	0.574	0.678	0.674	0.673	0.628
AdaNovo	0.753	0.638	0.620	0.494	0.671	0.546	0.797	0.684	0.660	0.660
RankNovo	0.765	0.592	0.642	0.621	0.688	0.595	0.765	0.720	0.726	0.687
π-PrimeNovo	0.725	0.532	0.602	0.575	0.654	0.572	0.701	0.701	0.703	0.650
ScopeNovo Setting 1	0.789	0.610	0.665	0.628	0.731	0.660	0.755	0.752	0.744	0.711
ScopeNovo Setting 2	0.749	0.572	0.643	0.624	0.690	0.637	0.733	0.732	0.721	0.684

MassNet: billion-scale AI-ready mass spectral corpus empowering scalable deep learning in proteomics (2026) · 4 tables

Fig. 4b

MassNet: billion-scale AI-ready mass spectral corpus empowering scalable deep learning in proteomics, page 7: Comparison of XuanjiNovo and existing de novo peptide sequencing models on the 15-species benchmark dataset. b, Cross-species peptide recall aggregated by species, illustrating overall performance differences across taxa.

15-species benchmark Nature Methods (307 files)	Peptide recall
15-species benchmark Nature Methods (307 files)	Acinetobacter calcoaceticus	Aedes aegypti	Arabidopsis thaliana	Aspergillus fumigatus	Bacteroides thetaiotaomicron	Drosophila melanogaster	Escherichia coli	Homo sapiens	Mus musculus	Pyrococcus furiosus	Saccharomyces cerevisiae	Toxoplasma gondii
π-HelixNovo · released	0.41	0.403	0.183	0.286	0.413	0.188	0.114	0.184	0.307	0.398	0.386	0.186
Casanovo V2 · released	0.6	0.57	0.266	0.395	0.652	0.319	0.329	0.224	0.458	0.516	0.598	0.262
InstaNovo+ · released	0.632	0.606	0.262	0.412	0.661	0.346	0.345	0.259	0.524	0.545	0.6	0.241
ContraNovo	0.654	0.615	0.283	0.436	0.678	0.373	0.368	0.299	0.54	0.544	0.626	0.277
XuanjiNovo	0.779	0.757	0.333	0.582	0.793	0.444	0.476	0.449	0.676	0.668	0.691	0.355
π-PrimeNovo	0.733	0.686	0.323	0.51	0.78	0.429	0.466	0.324	0.604	0.616	0.692	0.308

15-species benchmark Nature Methods (307 files)	Peptide recall
15-species benchmark Nature Methods (307 files)	Trypanosoma brucei	Zea mays	Solanum lycopersicum
π-HelixNovo · released	0.348	0.443	0.001
Casanovo V2 · released	0.588	0.557	0.011
InstaNovo+ · released	0.623	0.649	0.004
ContraNovo	0.641	0.653	0.062
XuanjiNovo	0.762	0.799	0.252
π-PrimeNovo	0.738	0.731	0.06

Read from the paper's source data: every value is a cell of 'Source Data.xlsx', sheet Figure4B, behind a figure that prints no values. Nothing was estimated from bar heights. Values from the paper's Source Data file (sheet Figure4B); the figure prints none. XuanjiNovo is XuanjiNovo-100M, per the caption. THE TEXT AND THE SHEET DISAGREE once: p. 5 says the other models on S. lycopersicum ranged 'from as low as 0.01 (π-HelixNovo)', the sheet gives 0.001.

Supplementary Fig. 3c

MassNet: billion-scale AI-ready mass spectral corpus empowering scalable deep learning in proteomics, page 5: Evaluation of de novo peptide sequencing performance on other benchmark datasets. (A) Peptide recall across nine species using seven models: InstaNovo+, π-HelixNovo, ContraNovo, CasaNovo V2, π-PrimeNovo, XuanjiNovo-100M, and XuanjiNovo-130M. (B) Peptide recall on the HCC dataset. (C) Peptide recall on the PT dataset. The π-HelixNovo model is publicly available and was trained on datasets derived from eight species, excluding human. XuanjiNovo-100M: base model; XuanjiNovo-130M: fine-tuned on 30M PSMs from MassIVE.

ProteomeTools	Peptide recall
ProteomeTools	PT
Casanovo V2	0.459
InstaNovo+	0.479
ContraNovo	0.574
π-PrimeNovo	0.586
XuanjiNovo	0.605

Read from the paper's source data: every value is a cell of 'Supplementary Data 1.xlsx', sheet SFigure3C, behind a figure that prints no values. Nothing was estimated from bar heights. Values from Supplementary Data 1 (sheet SFigure3C); the figure prints none. The figure draws six bars and the sheet has five: π-HelixNovo (about 0.26 by its bar) has no row, so it is not recorded. THE TEXT AND THE SHEET DISAGREE: p. 8 says XuanjiNovo-100M 'achieved a recall of 0.62' on PT, the sheet gives 0.605.

Supplementary Fig. 3b

Proteomics identifies new therapeutic targets of early-stage hepatocellular carcinoma as deposited	Peptide recall
	HCC
Casanovo V2	0.168
InstaNovo+	0.087
ContraNovo	0.373
π-PrimeNovo	0.382
XuanjiNovo	0.676

Read from the paper's source data: every value is a cell of 'Supplementary Data 1.xlsx', sheet SFigure3B, behind a figure that prints no values. Nothing was estimated from bar heights. Values from Supplementary Data 1 (sheet SFigure3B); the figure prints none. The figure draws six bars and the sheet has five: π-HelixNovo, whose bar is barely above zero, has no row, so it is not recorded rather than read off the bar. XuanjiNovo is XuanjiNovo-100M, per the caption of Fig. 4.

Supplementary Fig. 3a

Nine-species benchmark original (DeepNovo, 2017)	Peptide recall
Nine-species benchmark original (DeepNovo, 2017)	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Apis mellifera	Homo sapiens	Methanosarcina mazei	Mus musculus	Vigna mungo	Solanum lycopersicum	Saccharomyces cerevisiae
π-HelixNovo · released	0.596	0.388	0.473	0.392	0.56	0.483	0.623	0.56	0.568
InstaNovo+	0.576	0.417	0.467	0.456	0.528	0.471	0.565	0.585	0.56
Casanovo V2	0.622	0.446	0.499	0.455	0.565	0.486	0.594	0.624	0.598
ContraNovo	0.688	0.487	0.576	0.622	0.63	0.566	0.677	0.672	0.674
π-PrimeNovo	0.721	0.531	0.603	0.574	0.65	0.567	0.702	0.697	0.697
XuanjiNovo 100M	0.43	0.393	0.435	0.589	0.468	0.502	0.499	0.536	0.407
XuanjiNovo 130M	0.689	0.503	0.574	0.6	0.628	0.682	0.595	0.681	0.67

Read from the paper's source data: every value is a cell of 'Supplementary Data 1.xlsx', sheet SFigure3A, behind a figure that prints no values. Nothing was estimated from bar heights. Values from the paper's Supplementary Data 1 (sheet SFigure3A); the figure prints none. The sheet names InstaNovo where the legend draws InstaNovo+. THE TEXT AND THE SHEET DISAGREE once: p. 8 puts XuanjiNovo-130M on M. musculus at 'approximately 0.67', the sheet at 0.682.

dIon: Fragmentation-Based Invariance for Self-Supervised Learning of Tandem Mass Spectra (2026) · 4 tables

Table 26

dIon: Fragmentation-Based Invariance for Self-Supervised Learning of Tandem Mass Spectra, page 33: Complete MSKB-final de novo results, including amino-acid metrics and the dIoninitialized models fine-tuned with a 1,000-peak cap. Within each training regime, scratch and dIon initialization use an identical end-to-end training protocol.

Casanovo digestion enzyme bias data as deposited	Amino acid precision	Amino acid recall	Peptide precision at coverage 1
dIon from scratch, end-to-end; trained on MSKB-final	0.895	0.895	0.747
dIon init., end-to-end; trained on MSKB-final	0.927	0.926	0.802
dIon init., end-to-end, 1,000 peaks; trained on MSKB-final	0.935	0.934	0.822
dIon from scratch, end-to-end; trained on DNL-v1	0.933	0.934	0.819
dIon init., end-to-end; trained on DNL-v1	0.945	0.945	0.842
dIon init., end-to-end, 1,000 peaks; trained on DNL-v1	0.949	0.949	0.856
Casanovo 5.2.1 · released	0.911	0.912	0.783
InstaNovo 1.2.2 · released	0.961	0.961	0.916

MSKB-final, rows grouped by training corpus as in Table 1. InstaNovo 1.2.2 is printed in grey with an asterisk because it 'is documented as training on MassIVE-KB v1, the source corpus of this test set', so its numbers are not a held-out evaluation.
In the paper: column amino acid precision: the original table bolded dIon init., end-to-end, 1,000 peaks; trained on DNL-v1 (0.949).
In the paper: column peptide precision at coverage 1: the original table bolded dIon init., end-to-end, 1,000 peaks; trained on DNL-v1 (0.856).
In the paper: column amino acid recall: the original table bolded dIon init., end-to-end, 1,000 peaks; trained on DNL-v1 (0.949).

Table25

dIon: Fragmentation-Based Invariance for Self-Supervised Learning of Tandem Mass Spectra, page 32: Per-species peptide precision at full coverage on the Kingdoms de novo test set (deterministic run-disjoint split, at most 100k spectra per species).

The Proteome Landscape of the Kingdoms of Life	Peptide precision at coverage 1
The Proteome Landscape of the Kingdoms of Life	Akkermansia muciniphila	Arabidopsis thaliana (callus)	Arabidopsis thaliana (root)	Arabidopsis thaliana (sprout)	Bacillus subtilis	Bacteroides fragilis	Bacteroides thetaiotaomicron	Bacteroides uniformis	Bacteroides vulgatus	Bifidobacterium adolescentis	Bifidobacterium longum subsp. longum	Blautia obeum
dIon init.; DNL-v1, 200 peaks	0.584	0.617	0.620	0.652	0.559	0.631	0.607	0.632	0.612	0.563	0.525	0.606
dIon from scratch; DNL-v1, 200 peaks	0.535	0.573	0.574	0.605	0.527	0.583	0.561	0.585	0.562	0.519	0.487	0.554
Casanovo 5.2.1 · released	0.487	0.528	0.510	0.537	0.506	0.505	0.493	0.512	0.491	0.471	0.451	0.502
InstaNovo 1.2.2 · released	0.546	0.586	0.581	0.620	0.546	0.574	0.554	0.576	0.554	0.537	0.503	0.572

The Proteome Landscape of the Kingdoms of Life	Peptide precision at coverage 1
The Proteome Landscape of the Kingdoms of Life	Bos taurus	Caenorhabditis elegans	Canis lupus	Chlamydomonas reinhardtii	Chromera velia	Clostridium bolteae	Clostridium perfringens	Clostridium saccharolyticum	Collinsella aerofaciens	Coprococcus comes	Cricetulus griseus	Danio rerio
dIon init.; DNL-v1, 200 peaks	0.672	0.633	0.631	0.537	0.535	0.587	0.642	0.608	0.570	0.612	0.551	0.603
dIon from scratch; DNL-v1, 200 peaks	0.617	0.584	0.576	0.504	0.492	0.534	0.594	0.556	0.525	0.560	0.500	0.554
Casanovo 5.2.1 · released	0.540	0.519	0.504	0.476	0.450	0.485	0.538	0.502	0.475	0.500	0.451	0.485
InstaNovo 1.2.2 · released	0.649	0.600	0.607	0.520	0.519	0.553	0.600	0.570	0.538	0.569	0.545	0.575

The Proteome Landscape of the Kingdoms of Life	Peptide precision at coverage 1
The Proteome Landscape of the Kingdoms of Life	Dictyostelium discoideum	Didelphis didelphinae	Dorea formicigenerans	Drosophila melanogaster	Eggerthella lenta	Emiliania huxleyi	Escherichia coli	Eubacterium rectale	Fusarium oxysporum	Fusobacterium nucleatum subsp. nucleatum	Gallus gallus	Glycine max
dIon init.; DNL-v1, 200 peaks	0.622	0.645	0.555	0.640	0.519	0.560	0.619	0.564	0.494	0.641	0.659	0.608
dIon from scratch; DNL-v1, 200 peaks	0.566	0.589	0.514	0.586	0.462	0.515	0.567	0.516	0.457	0.594	0.607	0.561
Casanovo 5.2.1 · released	0.476	0.508	0.485	0.501	0.408	0.449	0.495	0.467	0.438	0.538	0.528	0.494
InstaNovo 1.2.2 · released	0.569	0.611	0.530	0.596	0.482	0.530	0.573	0.521	0.476	0.599	0.622	0.573

The Proteome Landscape of the Kingdoms of Life	Peptide precision at coverage 1
The Proteome Landscape of the Kingdoms of Life	Gossypium hirsutum	Guillardia theta	Haloarcula marismortui	Halobacterium sp.	Homo sapiens	Lactobacillus paracasei	Mus musculus	Mycoplasma intracellular	Neurospora crassa	Odoribacter splanchnicus	Oryctolagus cuniculus	Oryzias melastigma
dIon init.; DNL-v1, 200 peaks	0.607	0.579	0.652	0.556	0.679	0.586	0.611	0.761	0.553	0.598	0.648	0.593
dIon from scratch; DNL-v1, 200 peaks	0.560	0.533	0.616	0.522	0.624	0.541	0.555	0.698	0.511	0.552	0.582	0.539
Casanovo 5.2.1 · released	0.504	0.472	0.581	0.489	0.543	0.489	0.479	0.636	0.461	0.500	0.497	0.471
InstaNovo 1.2.2 · released	0.575	0.550	0.589	0.518	0.662	0.554	0.596	0.743	0.522	0.551	0.611	0.557

The Proteome Landscape of the Kingdoms of Life	Peptide precision at coverage 1
The Proteome Landscape of the Kingdoms of Life	Parabacteroides distasonis	Parabacteroides merdae	Phaeodactylum tricornutum	Porphyra umbilicalis	Prevotella copri	Rattus norvegicus	Roseburia intestinalis	Ruminococcus bromii	Ruminococcus gnavus	Ruminococcus torques	Saccharomyces cerevisiae	Stentor coeruleus
dIon init.; DNL-v1, 200 peaks	0.637	0.627	0.555	0.617	0.646	0.580	0.522	0.595	0.623	0.649	0.650	0.615
dIon from scratch; DNL-v1, 200 peaks	0.585	0.577	0.511	0.574	0.598	0.531	0.476	0.549	0.573	0.600	0.602	0.562
Casanovo 5.2.1 · released	0.523	0.520	0.457	0.497	0.537	0.482	0.445	0.505	0.505	0.530	0.517	0.486
InstaNovo 1.2.2 · released	0.588	0.577	0.532	0.583	0.599	0.580	0.497	0.562	0.578	0.604	0.602	0.572

The Proteome Landscape of the Kingdoms of Life	Peptide precision at coverage 1
The Proteome Landscape of the Kingdoms of Life	Streptococcus parasanguinis	Sulfolobus solfataricus	Sus scrofa	Symbiodinium microadriaticum	Synechocystis nigrescens	Tardigrade	Thalassiosira pseudonana	Triticum aestivum	Veillonella parvula	Vitis vinifera
dIon init.; DNL-v1, 200 peaks	0.627	0.567	0.577	0.551	0.652	0.618	0.562	0.616	0.554	0.581
dIon from scratch; DNL-v1, 200 peaks	0.577	0.514	0.525	0.510	0.615	0.564	0.515	0.569	0.509	0.543
Casanovo 5.2.1 · released	0.518	0.473	0.462	0.477	0.577	0.493	0.454	0.488	0.454	0.510
InstaNovo 1.2.2 · released	0.583	0.543	0.572	0.546	0.633	0.578	0.531	0.577	0.511	0.559

Peptide precision at full coverage per species of dIon's Kingdoms de novo test split. The 'Spectra' column, the number of test spectra per species, is not recorded as a measurement.

Table1 (MSKB-final)

dIon: Fragmentation-Based Invariance for Self-Supervised Learning of Tandem Mass Spectra, page 7: De novo sequencing on the Kingdoms de novo (all 70 species) and MSKB-final test sets. Within each training regime, scratch and dIon initialization use an identical end-to-end training protocol.

Casanovo digestion enzyme bias data as deposited	Peptide precision at coverage 1
Casanovo digestion enzyme bias data as deposited	MSKB-final
dIon from scratch, end-to-end; trained on MSKB-final	0.747
dIon init., end-to-end; trained on MSKB-final	0.802
dIon from scratch, end-to-end; trained on DNL-v1	0.819
dIon init., end-to-end; trained on DNL-v1	0.842
Casanovo 5.2.1 · released	0.783

Peptide precision here is at FULL coverage, unanswered spectra counted wrong, which is Casanovo's precision at coverage 1. Rows are grouped by training corpus, recorded in each variant. InstaNovo 1.2.2 has no MSKB-final value: it 'was trained on MassIVE-KB v1, the source of this test set'.

Table1 (Kingdoms de novo)

The Proteome Landscape of the Kingdoms of Life	Peptide precision at coverage 1
The Proteome Landscape of the Kingdoms of Life	Kingdoms
dIon from scratch, end-to-end; trained on MSKB-final	0.449
dIon init., end-to-end; trained on MSKB-final	0.533
dIon from scratch, end-to-end; trained on DNL-v1	0.554
dIon init., end-to-end; trained on DNL-v1	0.602
Casanovo 5.2.1 · released	0.492
InstaNovo 1.2.2 · released	0.570

GyroNovo: Error-Guided Fragment Imputation with Mass-Aware Attention for De Novo Peptide Sequencing (2026) · 6 tables

Table2 (Seven-species)

GyroNovo: Error-Guided Fragment Imputation with Mass-Aware Attention for De Novo Peptide Sequencing, page 7: PTM-levelperformanceontheNine-Species,SevenSpecies, and HC-PT datasets. Best results are shown in bold.

Seven-species benchmark NovoBench split	Ptm precision	Ptm recall
DeepNovo · quoted	0.391	0.373
PointNovo · quoted	0.117	0.094
InstaNovo · quoted	0.126	0.115
Casanovo · quoted	0.360	0.251
AdaNovo · quoted	0.448	0.321
LIPNovo · quoted	0.604	0.498
LIPNovo+ · quoted	0.648	0.561
LIPNovo reproduced · retrained	0.602	0.500
GyroNovo	0.665	0.565

Table2 (Nine-species)

Nine-species benchmark	Ptm precision	Ptm recall
DeepNovo · quoted	0.576	0.529
PointNovo · quoted	0.629	0.546
InstaNovo · quoted	0.443	0.294
Casanovo · quoted	0.706	0.566
AdaNovo · quoted	0.652	0.570
LIPNovo · quoted	0.765	0.656
LIPNovo+ · quoted	0.801	0.678
LIPNovo reproduced · retrained	0.773	0.646
GyroNovo	0.815	0.731

Table2 (HC-PT)

ProteomeTools HC-PT (NovoBench)	Ptm precision	Ptm recall
DeepNovo · quoted	0.626	0.615
PointNovo · quoted	0.676	0.740
InstaNovo · quoted	0.350	0.261
Casanovo · quoted	0.501	0.460
AdaNovo · quoted	0.552	0.482
LIPNovo · quoted	0.732	0.745
LIPNovo+ · quoted	0.757	0.805
LIPNovo reproduced · retrained	0.690	0.762
GyroNovo	0.790	0.817

Table1 (Seven-species)

GyroNovo: Error-Guided Fragment Imputation with Mass-Aware Attention for De Novo Peptide Sequencing, page 7: Amino-acid- and peptide-level performance on the Nine-Species, Seven-Species, and HC-PT datasets. Best results are shown in bold.

Seven-species benchmark NovoBench split	Amino acid precision	Amino acid recall	Peptide precision	Peptide AUC
DeepNovo · quoted	0.492	0.433	0.204	0.136
PointNovo · quoted	0.196	0.169	0.022	0.007
Casanovo · quoted	0.322	0.327	0.119	0.084
AdaNovo · quoted	0.379	0.385	0.174	0.135
InstaNovo · quoted	0.192	0.176	0.031	0.009
LIPNovo · quoted	0.557	0.560	0.327	0.281
LIPNovo+ · quoted	0.619	0.622	0.383	0.337
LIPNovo reproduced · retrained	0.553	0.554	0.325	0.270
GyroNovo	0.632	0.635	0.421	0.378

Table1 (Nine-species)

Nine-species benchmark	Amino acid precision	Amino acid recall	Peptide precision	Peptide AUC
PEAKS · quoted	0.748		0.428
DeepNovo · quoted	0.696	0.638	0.428	0.376
PointNovo · quoted	0.740	0.671	0.480	0.436
Casanovo · quoted	0.697	0.696	0.481	0.439
AdaNovo · quoted	0.698	0.709	0.505	0.469
InstaNovo · quoted	0.420	0.395	0.164	0.123
LIPNovo · quoted	0.797	0.797	0.582	0.547
LIPNovo+ · quoted	0.831	0.831	0.637	0.612
LIPNovo reproduced · retrained	0.794	0.796	0.583	0.549
GyroNovo	0.848	0.848	0.674	0.646

Table1 (HC-PT)

ProteomeTools HC-PT (NovoBench)	Amino acid precision	Amino acid recall	Peptide precision	Peptide AUC
DeepNovo · quoted	0.531	0.534	0.313	0.255
PointNovo · quoted	0.623	0.622	0.419	0.373
Casanovo · quoted	0.442	0.453	0.211	0.177
AdaNovo · quoted	0.442	0.451	0.212	0.178
InstaNovo · quoted	0.289	0.285	0.057	0.034
LIPNovo · quoted	0.637	0.643	0.458	0.427
LIPNovo+ · quoted	0.698	0.694	0.519	0.490
LIPNovo reproduced · retrained	0.653	0.654	0.469	0.438
GyroNovo	0.704	0.702	0.532	0.502

Learning from tandem mass spectra at scale with a self-supervised foundation model for proteomics (2026) · 1 table

Table S12

Learning from tandem mass spectra at scale with a self-supervised foundation model for proteomics, page 84: Peptide recall across the biological validation datasets for the de novo sequencing benchmark. The best model per dataset is in bold.

a dataset per evaluation set	Peptide recall
a dataset per evaluation set	HeLa degradome	Candidatus Scalindua brodae	Snake venoms	HeLa single-shot	Nanobodies	Wound exudates
InstaNovo-FM fine-tuned	0.821	0.740	0.230	0.662	0.528	0.366
InstaNovo v1.2	0.868	0.764	0.227	0.708	0.517	0.431
Casanovo	0.767	0.743	0.217	0.531	0.465	0.262
XuanjiNovo	0.808	0.741	0.233	0.673	0.529	0.466

No accession is printed for the six validation sets. They are mapped from the Methods, which call them InstaNovo's application datasets minus 'the Immuno and Herceptin datasets': GluC is InstaNovo's 'HeLa GluC degradome', TPL Antibodies its nanobodies, and Hela QC, by elimination, its HeLa single-shot set.

Discrete Diffusion with Physical Mass Constraints for De Novo Peptide Sequencing (2026) · 8 tables

Table5

Discrete Diffusion with Physical Mass Constraints for De Novo Peptide Sequencing, page 8: Comparison of inference speed and accuracy. PhysNovo enables a flexible speed–accuracy trade-off by adjusting the number of denoising steps (T).

not stated; page mentions Nine-species benchmark, ProteomeTools, Seven-species benchmark	Amino acid precision
Casanovo AR · retrained	0.697
π-PrimeNovo NAR	0.790
PhysNovo T = 100	0.815
PhysNovo T = 50	0.801

Table4

Discrete Diffusion with Physical Mass Constraints for De Novo Peptide Sequencing, page 8: Comparison of parameter efficiency and task performance.

not stated; page mentions Nine-species benchmark, ProteomeTools, Seven-species benchmark	Amino acid precision	Amino acid recall	Peptide precision	Peptide AUC
Casanovo · retrained	0.741	0.740	0.529	0.493
LIPNovo · retrained	0.797	0.797	0.582	0.547
PhysNovo	0.815	0.814	0.588	0.553

Table2 (Seven-species)

Discrete Diffusion with Physical Mass Constraints for De Novo Peptide Sequencing, page 7: Comparison with state-of-the-art methods on Nine-species, Seven-species, and HC-PT datasets in PTM-level performance.

Seven-species benchmark NovoBench split	Ptm precision	Ptm recall
DeepNovo · retrained	0.391	0.373
PointNovo · retrained	0.117	0.094
InstaNovo · retrained	0.126	0.115
Casanovo · retrained	0.360	0.251
AdaNovo · retrained	0.448	0.321
π-HelixNovo · retrained	0.473	0.366
LIPNovo · retrained	0.604	0.498
PhysNovo	0.654	0.549

Table2 (Nine-species)

Nine-species benchmark revised (balanced)	Ptm precision	Ptm recall
DeepNovo · retrained	0.576	0.529
PointNovo · retrained	0.629	0.546
InstaNovo · retrained	0.443	0.294
Casanovo · retrained	0.706	0.566
AdaNovo · retrained	0.652	0.570
π-HelixNovo · retrained	0.680	0.598
LIPNovo · retrained	0.765	0.656
PhysNovo	0.793	0.686

Table2 (HC-PT)

ProteomeTools HC-PT (NovoBench)	Ptm precision	Ptm recall
DeepNovo · retrained	0.626	0.615
PointNovo · retrained	0.676	0.740
InstaNovo · retrained	0.350	0.261
Casanovo · retrained	0.501	0.460
AdaNovo · retrained	0.552	0.482
π-HelixNovo · retrained	0.568	0.667
LIPNovo · retrained	0.732	0.745
PhysNovo	0.763	0.770

Table1 (Seven-species)

Discrete Diffusion with Physical Mass Constraints for De Novo Peptide Sequencing, page 7: Comparison with state-of-the-art methods on Nine-species, Seven-species, and HC-PT datasets in amino acid-level and peptidelevel performance. The best results are marked in bold, and the second best are underlined.

Seven-species benchmark NovoBench split	Amino acid precision	Amino acid recall	Peptide precision	Peptide AUC
DeepNovo · retrained	0.492	0.433	0.204	0.136
PointNovo · retrained	0.196	0.169	0.022	0.007
InstaNovo · retrained	0.192	0.176	0.031	0.009
Casanovo · retrained	0.322	0.327	0.119	0.084
AdaNovo · retrained	0.379	0.385	0.174	0.135
π-HelixNovo · retrained	0.481	0.472	0.234	0.173
LIPNovo · retrained	0.557	0.560	0.327	0.281
ReNovo	0.512	0.514	0.278	0.228
PhysNovo	0.568	0.569	0.346	0.290

Table1 (Nine-species)

Nine-species benchmark revised (balanced)	Amino acid precision	Amino acid recall	Peptide precision	Peptide AUC
PEAKS	0.748		0.428
DeepNovo · retrained	0.696	0.638	0.428	0.376
PointNovo · retrained	0.740	0.671	0.480	0.436
InstaNovo · retrained	0.420	0.395	0.164	0.123
Casanovo · retrained	0.697	0.696	0.481	0.439
AdaNovo · retrained	0.698	0.709	0.505	0.469
π-HelixNovo · retrained	0.765	0.758	0.517	0.453
LIPNovo · retrained	0.797	0.797	0.582	0.547
π-PrimeNovo	0.790
ReNovo	0.770	0.769	0.568	0.528
PhysNovo	0.815	0.814	0.588	0.553

Table1 (HC-PT)

ProteomeTools HC-PT (NovoBench)	Amino acid precision	Amino acid recall	Peptide precision	Peptide AUC
DeepNovo · retrained	0.531	0.534	0.313	0.255
PointNovo · retrained	0.623	0.622	0.419	0.373
InstaNovo · retrained	0.289	0.285	0.057	0.034
Casanovo · retrained	0.442	0.453	0.211	0.177
AdaNovo · retrained	0.442	0.451	0.212	0.178
π-HelixNovo · retrained	0.588	0.582	0.356	0.318
LIPNovo · retrained	0.637	0.643	0.458	0.427
ReNovo	0.651	0.648	0.467	0.436
PhysNovo	0.649	0.652	0.473	0.444

MemNovo: Look Back at the Spectrum for Balanced De Novo Peptide Sequencing from Mass Spectrometry (2026) · 1 table

Table2

MemNovo: Look Back at the Spectrum for Balanced De Novo Peptide Sequencing from Mass Spectrometry, page 7: Performance comparison on the Nine Species benchmark. We report amino acid precision (AA) and peptide precision (Pep.) for each method. For Casanovo and InstaNovo, we additionally show results with MemNovo applied and the corresponding relative improvement (Rel. Imp.). Best results for each species are shown in bold. Results marked with † are reported from original publications, which may use different model versions and evaluation protocols; direct numerical comparison with our evaluation should be interpreted with caution.

Nine-species benchmark revised (balanced)	Amino acid precision
Nine-species benchmark revised (balanced)	Mus musculus	Homo sapiens	Saccharomyces cerevisiae	Methanosarcina mazei	Apis mellifera	Solanum lycopersicum	Vigna mungo	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Average
DeepNovo · quoted	0.6230	0.6100	0.7500	0.6940	0.6300	0.7310	0.6790	0.7420	0.6020	0.6734
PointNovo · quoted	0.6260	0.6060	0.7790	0.7120	0.6440	0.7330	0.7300	0.7680	0.5890	0.6874
Casanovo	0.6352	0.6885	0.7672	0.7253	0.6730	0.7302	0.7446	0.7701	0.6895	0.7137
AdaNovo · quoted	0.6460	0.6180	0.7930	0.7280	0.6500	0.7400	0.7190	0.7390	0.6420	0.6972
InstaNovo	0.7351	0.7934	0.8447	0.8214	0.7754	0.8181	0.8450	0.8367	0.7631	0.8037
MemNovo on Casanovo	0.6524	0.7031	0.7688	0.7287	0.7749	0.7442	0.7469	0.7941	0.7055	0.7354
MemNovo on InstaNovo	0.7614	0.8130	0.8553	0.8350	0.7934	0.8324	0.8566	0.8504	0.7912	0.8210

Nine-species benchmark revised (balanced)	Peptide precision
Nine-species benchmark revised (balanced)	Mus musculus	Homo sapiens	Saccharomyces cerevisiae	Methanosarcina mazei	Apis mellifera	Solanum lycopersicum	Vigna mungo	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Average
DeepNovo · quoted	0.2860	0.2930	0.4620	0.4220	0.3300	0.4540	0.4360	0.4490	0.2530	0.3761
PointNovo · quoted	0.3550	0.3510	0.5340	0.4780	0.3960	0.5130	0.5110	0.5180	0.2980	0.4393
Casanovo	0.2404	0.3105	0.4089	0.3254	0.2875	0.3873	0.3314	0.3585	0.2274	0.3197
AdaNovo · quoted	0.4670	0.3730	0.5930	0.4960	0.4310	0.5300	0.5460	0.5280	0.3720	0.4818
InstaNovo	0.3485	0.4562	0.5559	0.4927	0.4318	0.5116	0.5177	0.5185	0.3435	0.4640
MemNovo on Casanovo	0.3372	0.4541	0.4968	0.4295	0.5226	0.5250	0.3530	0.5309	0.3529	0.4447
MemNovo on InstaNovo	0.3522	0.4675	0.5981	0.5051	0.4419	0.5305	0.5481	0.5298	0.3668	0.4822

LIPNovo+: Self-Reflective Latent Imputation for Robust De Novo Peptide Sequencing (2026) · 8 tables

Table5

LIPNovo+: Self-Reflective Latent Imputation for Robust De Novo Peptide Sequencing, page 18: Comparison with state-of-the-art methods on GraphNovo dataset [11].

GraphNovo dataset and checkpoint	Peptide recall	Peptide AUC	Amino acid recall	Amino acid precision
PEAKS	0.475		0.758	0.775
Novor	0.431		0.740	0.753
DeepNovo	0.497		0.769	0.771
PointNovo	0.601		0.815	0.816
Casanovo · retrained	0.605		0.684	0.684
GraphNovo	0.712		0.876	0.874
LIPNovo	0.729	0.707	0.876	0.882
LIPNovo+	0.740	0.720	0.889	0.891

Table4

LIPNovo+: Self-Reflective Latent Imputation for Robust De Novo Peptide Sequencing, page 17: Leave-one-out cross-validation on the Nine-species dataset.

Nine-species benchmark	Amino acid precision
Nine-species benchmark	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Apis mellifera	Homo sapiens	Methanosarcina mazei	Mus musculus	Vigna mungo	Solanum lycopersicum	Mean
Casanovo retrained · retrained	0.743	0.754	0.761	0.769	0.754	0.753	0.753	0.717	0.751
LIPNovo	0.806	0.805	0.807	0.805	0.807	0.808	0.794	0.800	0.804
LIPNovo+	0.845	0.843	0.849	0.851	0.841	0.844	0.839	0.839	0.844

Nine-species benchmark	Amino acid recall
Nine-species benchmark	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Apis mellifera	Homo sapiens	Methanosarcina mazei	Mus musculus	Vigna mungo	Solanum lycopersicum	Mean
Casanovo retrained · retrained	0.745	0.752	0.757	0.770	0.754	0.752	0.755	0.716	0.750
LIPNovo	0.807	0.805	0.806	0.805	0.807	0.807	0.796	0.798	0.804
LIPNovo+	0.844	0.842	0.848	0.851	0.840	0.843	0.839	0.838	0.843

Nine-species benchmark	Peptide precision
Nine-species benchmark	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Apis mellifera	Homo sapiens	Methanosarcina mazei	Mus musculus	Vigna mungo	Solanum lycopersicum	Mean
Casanovo retrained · retrained	0.559	0.544	0.554	0.575	0.558	0.558	0.549	0.506	0.550
LIPNovo	0.607	0.591	0.606	0.596	0.596	0.607	0.577	0.577	0.595
LIPNovo+	0.669	0.657	0.679	0.673	0.657	0.658	0.653	0.652	0.662

Nine-species benchmark	Peptide AUC
Nine-species benchmark	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Apis mellifera	Homo sapiens	Methanosarcina mazei	Mus musculus	Vigna mungo	Solanum lycopersicum	Mean
Casanovo retrained · retrained	0.523	0.509	0.519	0.540	0.532	0.521	0.510	0.464	0.515
LIPNovo	0.581	0.563	0.577	0.567	0.565	0.579	0.545	0.544	0.565
LIPNovo+	0.647	0.634	0.657	0.651	0.632	0.633	0.628	0.626	0.639

Nine-species benchmark	Ptm precision
Nine-species benchmark	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Apis mellifera	Homo sapiens	Methanosarcina mazei	Mus musculus	Vigna mungo	Solanum lycopersicum	Mean
Casanovo retrained · retrained	0.797	0.742	0.762	0.784	0.779	0.799	0.760	0.710	0.767
LIPNovo	0.855	0.764	0.769	0.772	0.812	0.803	0.764	0.777	0.790
LIPNovo+	0.888	0.776	0.816	0.835	0.827	0.834	0.841	0.824	0.830

Nine-species benchmark	Ptm recall
Nine-species benchmark	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Apis mellifera	Homo sapiens	Methanosarcina mazei	Mus musculus	Vigna mungo	Solanum lycopersicum	Mean
Casanovo retrained · retrained	0.689	0.591	0.624	0.612	0.582	0.608	0.593	0.540	0.605
LIPNovo	0.739	0.647	0.675	0.672	0.630	0.667	0.634	0.627	0.661
LIPNovo+	0.782	0.699	0.728	0.696	0.707	0.703	0.703	0.704	0.715

Table3 (Seven-species)

LIPNovo+: Self-Reflective Latent Imputation for Robust De Novo Peptide Sequencing, page 16: Comparison with state-of-the-art methods on Nine-species, Seven-species, and HC-PT datasets in PTM-level performance. The best and second-best results are marked in bold and underline, respectively.

Seven-species benchmark NovoBench split	Ptm precision	Ptm recall
DeepNovo	0.391	0.373
PointNovo	0.117	0.094
InstaNovo	0.126	0.115
Casanovo · retrained	0.360	0.251
AdaNovo	0.448	0.321
π-HelixNovo	0.473	0.366
SearchNovo	0.472	0.447
Casanovo retrained · retrained	0.368	0.292
LIPNovo	0.604	0.498
LIPNovo+	0.648	0.561

Table3 (Nine-species)

Nine-species benchmark	Ptm precision	Ptm recall
DeepNovo	0.576	0.529
PointNovo	0.629	0.546
InstaNovo	0.443	0.294
Casanovo · retrained	0.706	0.566
AdaNovo	0.652	0.570
π-HelixNovo	0.680	0.598
SearchNovo	0.764	0.599
Casanovo retrained · retrained	0.755	0.601
LIPNovo	0.765	0.656
LIPNovo+	0.801	0.678

Table3 (HC-PT)

ProteomeTools HC-PT (NovoBench)	Ptm precision	Ptm recall
DeepNovo	0.626	0.615
PointNovo	0.676	0.740
InstaNovo	0.350	0.261
Casanovo · retrained	0.501	0.460
AdaNovo	0.552	0.482
π-HelixNovo	0.568	0.667
SearchNovo	0.715	0.772
Casanovo retrained · retrained	0.550	0.582
LIPNovo	0.732	0.745
LIPNovo+	0.757	0.805

In the paper: column ptm recall: the original table underlined SearchNovo (0.772), LIPNovo (0.745).

Table2 (Seven-species)

LIPNovo+: Self-Reflective Latent Imputation for Robust De Novo Peptide Sequencing, page 15: Comparison with state-of-the-art methods on Nine-species, Seven-species, and HC-PT datasets in amino acid-level and peptide-level performance. The best and second-best results are marked in bold and underline, respectively.

Seven-species benchmark NovoBench split	Amino acid precision	Amino acid recall	Peptide precision	Peptide AUC
DeepNovo	0.492	0.433	0.204	0.136
PointNovo	0.196	0.169	0.022	0.007
Casanovo · retrained	0.322	0.327	0.119	0.084
AdaNovo	0.379	0.385	0.174	0.135
π-HelixNovo	0.481	0.472	0.234	0.173
InstaNovo	0.192	0.176	0.031	0.009
SearchNovo	0.489	0.488	0.259	0.174
ReNovo	0.512	0.514	0.278	0.228
DiffuNovo	0.437	0.435	0.193	0.162
Casanovo retrained · retrained	0.357	0.366	0.159	0.119
LIPNovo	0.557	0.560	0.327	0.281
LIPNovo+	0.619	0.622	0.383	0.337

Table2 (Nine-species)

Nine-species benchmark	Amino acid precision	Amino acid recall	Peptide precision	Peptide AUC
PEAKS	0.748		0.428
DeepNovo	0.696	0.638	0.428	0.376
PointNovo	0.740	0.671	0.480	0.436
Casanovo · retrained	0.697	0.696	0.481	0.439
AdaNovo	0.698	0.709	0.505	0.469
π-HelixNovo	0.765	0.758	0.517	0.453
InstaNovo	0.420	0.395	0.164	0.123
SearchNovo	0.748	0.746	0.550	0.489
ReNovo	0.770	0.769	0.568	0.528
DiffuNovo	0.791	0.789	0.565	0.536
Casanovo retrained · retrained	0.741	0.740	0.529	0.493
LIPNovo	0.797	0.797	0.582	0.547
LIPNovo+	0.831	0.831	0.637	0.612

Table2 (HC-PT)

ProteomeTools HC-PT (NovoBench)	Amino acid precision	Amino acid recall	Peptide precision	Peptide AUC
DeepNovo	0.531	0.534	0.313	0.255
PointNovo	0.623	0.622	0.419	0.373
Casanovo · retrained	0.442	0.453	0.211	0.177
AdaNovo	0.442	0.451	0.212	0.178
π-HelixNovo	0.588	0.582	0.356	0.318
InstaNovo	0.289	0.285	0.057	0.034
SearchNovo	0.652	0.658	0.447	0.413
ReNovo	0.651	0.648	0.467	0.436
DiffuNovo	0.654	0.654	0.458	0.434
Casanovo retrained · retrained	0.525	0.530	0.324	0.290
LIPNovo	0.637	0.643	0.458	0.427
LIPNovo+	0.698	0.694	0.519	0.490

In the paper: column amino acid precision: the original table underlined LIPNovo (0.637).

Regressor-guided Diffusion Model for De Novo Peptide Sequencing with Explicit Mass Control (2026) · 6 tables

Table2 (Seven-species)

Regressor-guided Diffusion Model for De Novo Peptide Sequencing with Explicit Mass Control, page 7: The comparison of de novo peptide sequencing performance between our proposed model, DiffuNovo, and other stateof-the-art methods on the three benchmark datasets. We report precision and AUC at the peptide level, and precision and recall at the amino acid level. The best and the second best are highlighted with bold and underline, respectively.

Seven-species benchmark NovoBench split	Peptide precision	Peptide AUC	Amino acid precision	Amino acid recall
DeepNovo	0.204	0.136	0.492	0.433
PointNovo	0.022	0.007	0.196	0.169
Casanovo	0.119	0.084	0.322	0.327
AdaNovo	0.174	0.135	0.379	0.385
π-HelixNovo	0.234	0.173	0.481	0.472
DiffuNovo Logits	0.233	0.104	0.430	0.428
DiffuNovo MBR	0.193	0.162	0.437	0.435

Table2 (Nine-species)

Nine-species benchmark	Peptide precision	Peptide AUC	Amino acid precision	Amino acid recall
DeepNovo	0.428	0.376	0.696	0.638
PointNovo	0.480	0.436	0.740	0.671
Casanovo	0.481	0.439	0.697	0.696
AdaNovo	0.505	0.469	0.698	0.709
π-HelixNovo	0.517	0.453	0.765	0.758
DiffuNovo Logits	0.572	0.413	0.785	0.783
DiffuNovo MBR	0.565	0.536	0.791	0.789

In the paper: column amino acid precision: the original table underlined π-HelixNovo (0.765).
In the paper: column peptide precision: the original table underlined π-HelixNovo (0.517).
In the paper: column amino acid recall: the original table underlined π-HelixNovo (0.758).

Table2 (HC-PT)

ProteomeTools HC-PT (NovoBench)	Peptide precision	Peptide AUC	Amino acid precision	Amino acid recall
DeepNovo	0.313	0.255	0.531	0.534
PointNovo	0.419	0.373	0.623	0.622
Casanovo	0.211	0.177	0.442	0.453
AdaNovo	0.212	0.178	0.442	0.451
π-HelixNovo	0.356	0.318	0.588	0.582
DiffuNovo Logits	0.485	0.324	0.648	0.648
DiffuNovo MBR	0.458	0.434	0.654	0.654

In the paper: column amino acid precision: the original table underlined PointNovo (0.623).
In the paper: column peptide precision: the original table underlined PointNovo (0.419).
In the paper: column amino acid recall: the original table underlined PointNovo (0.622).

Table 3 (Seven-species)

Regressor-guided Diffusion Model for De Novo Peptide Sequencing with Explicit Mass Control, page 7: Empirical comparison of PTM identification. We evaluate the ability of DiffuNovo and other models to identify Post-Translational Modifications (PTMs) on the benchmark datasets. The best results and the second best are highlighted with bold and underline, respectively.

Seven-species benchmark NovoBench split	Ptm precision
DeepNovo	0.391
PointNovo	0.117
AdaNovo	0.448
Casanovo	0.360
π-HelixNovo	0.473
DiffuNovo	0.515

Table 3 (Nine-species)

Nine-species benchmark	Ptm precision
DeepNovo	0.576
PointNovo	0.629
AdaNovo	0.652
Casanovo	0.706
π-HelixNovo	0.680
DiffuNovo	0.822

Table 3 (HC-PT)

ProteomeTools HC-PT (NovoBench)	Ptm precision
DeepNovo	0.626
PointNovo	0.676
AdaNovo	0.552
Casanovo	0.501
π-HelixNovo	0.568
DiffuNovo	0.705

CausalNovo: Advancing De Novo Peptide Sequencing via a Causality-Informed Framework (2026) · 8 tables

Table8

CausalNovo: Advancing De Novo Peptide Sequencing via a Causality-Informed Framework, page 16: Cross-species validation on the Seven-species dataset. † denotes our retrained results.

Seven-species benchmark original (DeepNovo, 2017)	Amino acid precision						Amino acid recall
Seven-species benchmark original (DeepNovo, 2017)	Caenorhabditis elegans	Escherichia coli	Drosophila melanogaster	Homo sapiens	Mus musculus	Pseudomonas aeruginosa	Caenorhabditis elegans	Escherichia coli	Drosophila melanogaster	Homo sapiens	Mus musculus	Pseudomonas aeruginosa
Casanovo	0.436	0.407	0.435	0.418	0.496	0.425	0.439	0.412	0.438	0.421	0.496	0.437
CausalNovo on †CasaNovo	0.512	0.511	0.503	0.507	0.534	0.502	0.513	0.514	0.513	0.509	0.536	0.502

Seven-species benchmark original (DeepNovo, 2017)	Peptide precision						Peptide AUC
Seven-species benchmark original (DeepNovo, 2017)	Caenorhabditis elegans	Escherichia coli	Drosophila melanogaster	Homo sapiens	Mus musculus	Pseudomonas aeruginosa	Caenorhabditis elegans	Escherichia coli	Drosophila melanogaster	Homo sapiens	Mus musculus	Pseudomonas aeruginosa
Casanovo	0.220	0.208	0.222	0.210	0.268	0.227	0.174	0.165	0.179	0.167	0.223	0.188
CausalNovo on †CasaNovo	0.284	0.285	0.293	0.292	0.299	0.281	0.234	0.241	0.250	0.247	0.256	0.242

Table3

CausalNovo: Advancing De Novo Peptide Sequencing via a Causality-Informed Framework, page 6: Cross-species validation on the Ninespecies dataset. † denotes our retrained results.

Nine-species benchmark	Amino acid precision
Nine-species benchmark	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Apis mellifera	Homo sapiens	Methanosarcina mazei	Mus musculus	Vigna mungo	Solanum lycopersicum
Casanovo · retrained	0.743	0.754	0.761	0.769	0.754	0.753	0.753	0.717
CausalNovo on †CasaNovo	0.773	0.772	0.772	0.780	0.773	0.784	0.763	0.748

Nine-species benchmark	Amino acid recall
Nine-species benchmark	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Apis mellifera	Homo sapiens	Methanosarcina mazei	Mus musculus	Vigna mungo	Solanum lycopersicum
Casanovo · retrained	0.745	0.752	0.757	0.770	0.754	0.752	0.755	0.716
CausalNovo on †CasaNovo	0.774	0.773	0.774	0.779	0.776	0.782	0.763	0.752

Nine-species benchmark	Peptide precision
Nine-species benchmark	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Apis mellifera	Homo sapiens	Methanosarcina mazei	Mus musculus	Vigna mungo	Solanum lycopersicum
Casanovo · retrained	0.559	0.544	0.554	0.575	0.558	0.558	0.549	0.506
CausalNovo on †CasaNovo	0.584	0.578	0.578	0.587	0.580	0.595	0.557	0.545

Nine-species benchmark	Peptide AUC
Nine-species benchmark	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Apis mellifera	Homo sapiens	Methanosarcina mazei	Mus musculus	Vigna mungo	Solanum lycopersicum
Casanovo · retrained	0.523	0.509	0.519	0.540	0.532	0.521	0.510	0.464
CausalNovo on †CasaNovo	0.551	0.544	0.545	0.553	0.544	0.560	0.521	0.506

Table2 (Seven-species)

CausalNovo: Advancing De Novo Peptide Sequencing via a Causality-Informed Framework, page 6: Comparison with state-of-the-art models in PTM-level performance. † denotes our retrained results, and others are given by NovoBench. The best is marked in bold.

Seven-species benchmark NovoBench split	Ptm precision	Ptm recall
DeepNovo · quoted	0.391	0.373
PointNovo · quoted	0.117	0.094
InstaNovo · quoted	0.126	0.115
SearchNovo · quoted	0.472	0.447
Casanovo · quoted	0.360	0.251
Casanovo · retrained	0.368	0.292
CausalNovo on †CasaNovo	0.503	0.422
AdaNovo · quoted	0.448	0.321
AdaNovo · retrained	0.430	0.356
CausalNovo on †AdaNovo	0.469	0.398
π-HelixNovo · quoted	0.473	0.366
π-HelixNovo · retrained	0.362	0.370
CausalNovo on †π-HelixNovo	0.513	0.427

Table2 (Nine-species)

Nine-species benchmark	Ptm precision	Ptm recall
DeepNovo · quoted	0.576	0.529
PointNovo · quoted	0.629	0.546
InstaNovo · quoted	0.443	0.294
SearchNovo · quoted	0.764	0.599
Casanovo · quoted	0.706	0.566
Casanovo · retrained	0.755	0.601
CausalNovo on †CasaNovo	0.791	0.608
AdaNovo · quoted	0.652	0.570
AdaNovo · retrained	0.678	0.552
CausalNovo on †AdaNovo	0.769	0.607
π-HelixNovo · quoted	0.680	0.598
π-HelixNovo · retrained	0.723	0.593
CausalNovo on †π-HelixNovo	0.731	0.616

Table2 (HC-PT)

ProteomeTools HC-PT (NovoBench)	Ptm precision	Ptm recall
DeepNovo · quoted	0.626	0.615
PointNovo · quoted	0.676	0.740
InstaNovo · quoted	0.350	0.261
SearchNovo · quoted	0.715	0.772
Casanovo · quoted	0.501	0.460
Casanovo · retrained	0.550	0.582
CausalNovo on †CasaNovo	0.671	0.741
AdaNovo · quoted	0.552	0.482
AdaNovo · retrained	0.562	0.532
CausalNovo on †AdaNovo	0.652	0.743
π-HelixNovo · quoted	0.568	0.667
π-HelixNovo · retrained	0.632	0.566
CausalNovo on †π-HelixNovo	0.737	0.746

Table1 (Seven-species)

CausalNovo: Advancing De Novo Peptide Sequencing via a Causality-Informed Framework, page 6: Comparison with state-of-the-art models in amino acid-level and peptide-level performance. † denotes our retrained results, and others are provided by NovoBench. The best results are marked in bold, and the second-best results are underlined.

Seven-species benchmark NovoBench split	Amino acid precision	Amino acid recall	Peptide precision	Peptide AUC
DeepNovo · quoted	0.492	0.433	0.204	0.136
PointNovo · quoted	0.196	0.169	0.022	0.007
InstaNovo · quoted	0.192	0.176	0.031	0.009
SearchNovo · quoted	0.489	0.488	0.259	0.174
Casanovo · quoted	0.322	0.327	0.119	0.084
Casanovo · retrained	0.357	0.366	0.159	0.119
CausalNovo on †CasaNovo	0.477	0.478	0.245	0.200
AdaNovo · quoted	0.379	0.385	0.174	0.135
AdaNovo · retrained	0.403	0.405	0.189	0.149
CausalNovo on †AdaNovo	0.453	0.453	0.233	0.192
π-HelixNovo · quoted	0.481	0.472	0.234	0.173
π-HelixNovo · retrained	0.465	0.462	0.218	0.156
CausalNovo on †π-HelixNovo	0.536	0.534	0.282	0.229

Table1 (Nine-species)

Nine-species benchmark	Amino acid precision	Amino acid recall	Peptide precision	Peptide AUC
PEAKS · quoted	0.748		0.428
DeepNovo · quoted	0.696	0.638	0.428	0.376
PointNovo · quoted	0.740	0.671	0.480	0.436
InstaNovo · quoted	0.420	0.395	0.164	0.123
SearchNovo · quoted	0.748	0.746	0.550	0.489
Casanovo · quoted	0.697	0.696	0.481	0.439
Casanovo · retrained	0.741	0.740	0.529	0.493
CausalNovo on †CasaNovo	0.765	0.766	0.564	0.528
AdaNovo · quoted	0.698	0.709	0.505	0.469
AdaNovo · retrained	0.681	0.681	0.473	0.439
CausalNovo on †AdaNovo	0.744	0.746	0.542	0.507
π-HelixNovo · quoted	0.765	0.758	0.517	0.453
π-HelixNovo · retrained	0.765	0.752	0.509	0.431
CausalNovo on †π-HelixNovo	0.787	0.784	0.543	0.483

Table1 (HC-PT)

ProteomeTools HC-PT (NovoBench)	Amino acid precision	Amino acid recall	Peptide precision	Peptide AUC
DeepNovo · quoted	0.531	0.534	0.313	0.255
PointNovo · quoted	0.623	0.622	0.419	0.373
InstaNovo · quoted	0.289	0.285	0.057	0.034
SearchNovo · quoted	0.652	0.658	0.447	0.413
Casanovo · quoted	0.442	0.453	0.211	0.177
Casanovo · retrained	0.525	0.530	0.324	0.290
CausalNovo on †CasaNovo	0.635	0.639	0.459	0.426
AdaNovo · quoted	0.442	0.451	0.212	0.178
AdaNovo · retrained	0.492	0.496	0.289	0.254
CausalNovo on †AdaNovo	0.634	0.637	0.453	0.420
π-HelixNovo · quoted	0.588	0.582	0.356	0.318
π-HelixNovo · retrained	0.532	0.537	0.301	0.261
CausalNovo on †π-HelixNovo	0.656	0.658	0.450	0.415

Bidirectional Representations Augmented Autoregressive Biological Sequence Generation (2025) · 4 tables

Table7

Bidirectional Representations Augmented Autoregressive Biological Sequence Generation, page 26: Comparison of the performance of CROSSNOVO and baseline methods on IgG1-Human. Bold values indicate the best performance.

The same table is also printed in Bidirectional Representations Augmented Autoregressive Biological Sequence Generation (Table7, page 26).

Monoclonal antibody de novo assembly as deposited	Amino acid precision
Monoclonal antibody de novo assembly as deposited	HC AspN	HC Chymotrypsin	HC GluC	HC LysC	HC Proteinase K	HC Trypsin	LC AspN	LC LysC	Average
Casanovo V2	0.520	0.472	0.605	0.757	0.354	0.759	0.666	0.778	0.642
ContraNovo	0.580	0.565	0.642	0.790	0.348	0.787	0.702	0.793	0.676
CrossNovo	0.613	0.617	0.694	0.814	0.367	0.803	0.719	0.807	0.702

Monoclonal antibody de novo assembly as deposited	Peptide recall
Monoclonal antibody de novo assembly as deposited	HC AspN	HC Chymotrypsin	HC GluC	HC LysC	HC Proteinase K	HC Trypsin	LC AspN	LC LysC	Average
Casanovo V2	0.265	0.274	0.399	0.569	0.206	0.595	0.325	0.625	0.446
ContraNovo	0.396	0.372	0.437	0.653	0.274	0.675	0.499	0.646	0.529
CrossNovo	0.415	0.421	0.512	0.701	0.275	0.699	0.544	0.676	0.560

Table6

Bidirectional Representations Augmented Autoregressive Biological Sequence Generation, page 26: Comparison of the performance of CROSSNOVO and baseline methods on WIgG1-Mouse. The bold font indicates the best performance.

The same table is also printed in Bidirectional Representations Augmented Autoregressive Biological Sequence Generation (Table6, page 26).

Monoclonal antibody de novo assembly as deposited	Amino acid precision					Peptide recall
Monoclonal antibody de novo assembly as deposited	HC AspN	HC Chymotrypsin	HC Trypsin	LC AspN	Average	HC AspN	HC Chymotrypsin	HC Trypsin	LC AspN	Average
Casanovo V2	0.714	0.591	0.723	0.668	0.674	0.557	0.483	0.636	0.456	0.533
ContraNovo	0.750	0.612	0.650	0.649	0.665	0.649	0.545	0.671	0.519	0.596
CrossNovo	0.769	0.640	0.747	0.724	0.720	0.662	0.577	0.699	0.581	0.630

Table2

Bidirectional Representations Augmented Autoregressive Biological Sequence Generation, page 8: Comparison of the performance of CROSSNOVO and baseline methods on 9-species-v2 test set. The bold font indicates the best performance.

The same table is also printed in Bidirectional Representations Augmented Autoregressive Biological Sequence Generation (Table2, page 8), Distilling Non-Autoregressive Model Knowledge for Autoregressive De Novo Peptide Sequencing (Table2, page 8).

Nine-species benchmark revised (build not stated)	Amino acid precision
Nine-species benchmark revised (build not stated)	Mus musculus	Homo sapiens	Saccharomyces cerevisiae	Methanosarcina mazei	Apis mellifera	Solanum lycopersicum	Vigna mungo	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Average
π-PrimeNovo	0.839	0.893	0.932	0.908	0.862	0.909	0.931	0.921	0.827	0.891
Casanovo V2	0.813	0.872	0.915	0.877	0.823	0.891	0.891	0.888	0.791	0.862
ContraNovo	0.839	0.920	0.919	0.896	0.848	0.898	0.913	0.901	0.807	0.882
CrossNovo	0.857	0.937	0.939	0.920	0.880	0.914	0.939	0.927	0.837	0.906

Nine-species benchmark revised (build not stated)	Peptide recall
Nine-species benchmark revised (build not stated)	Mus musculus	Homo sapiens	Saccharomyces cerevisiae	Methanosarcina mazei	Apis mellifera	Solanum lycopersicum	Vigna mungo	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Average
π-PrimeNovo	0.627	0.795	0.884	0.812	0.742	0.824	0.837	0.849	0.626	0.777
Casanovo V2	0.555	0.712	0.837	0.754	0.669	0.783	0.772	0.793	0.558	0.714
ContraNovo	0.616	0.820	0.854	0.780	0.711	0.794	0.799	0.815	0.575	0.752
CrossNovo	0.651	0.850	0.885	0.819	0.751	0.816	0.847	0.850	0.607	0.786

Table1

Bidirectional Representations Augmented Autoregressive Biological Sequence Generation, page 8: Comparison of the performance of CROSSNOVO and baseline methods on the 9-species-v1 test set.

The same table is also printed in Bidirectional Representations Augmented Autoregressive Biological Sequence Generation (Table1, page 8).

Nine-species benchmark original (DeepNovo, 2017)	Amino acid precision
Nine-species benchmark original (DeepNovo, 2017)	Mus musculus	Homo sapiens	Saccharomyces cerevisiae	Methanosarcina mazei	Apis mellifera	Solanum lycopersicum	Vigna mungo	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Average
PEAKS	0.600	0.639	0.748	0.673	0.633	0.728	0.644	0.719	0.586	0.663
π-PrimeNovo	0.784	0.729	0.802	0.801	0.763	0.815	0.822	0.846	0.734	0.788
DeepNovo	0.623	0.610	0.750	0.694	0.630	0.731	0.679	0.742	0.602	0.673
PointNovo	0.626	0.606	0.779	0.712	0.644	0.733	0.730	0.768	0.589	0.687
Casanovo	0.689	0.586	0.684	0.679	0.629	0.721	0.668	0.749	0.603	0.667
InstaNovo	0.703	0.636	0.691	0.712	0.660	0.732	0.711	0.739	0.619	0.689
Casanovo V2	0.760	0.676	0.752	0.755	0.706	0.785	0.748	0.790	0.681	0.739
π-HelixNovo	0.765	0.665	0.768	0.784	0.757	0.721	0.793	0.816	0.681	0.750
ContraNovo	0.798	0.771	0.797	0.799	0.745	0.810	0.807	0.828	0.711	0.785
CrossNovo	0.816	0.800	0.814	0.826	0.785	0.830	0.831	0.856	0.740	0.811

Nine-species benchmark original (DeepNovo, 2017)	Peptide recall
Nine-species benchmark original (DeepNovo, 2017)	Mus musculus	Homo sapiens	Saccharomyces cerevisiae	Methanosarcina mazei	Apis mellifera	Solanum lycopersicum	Vigna mungo	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Average
PEAKS	0.197	0.277	0.428	0.356	0.287	0.403	0.362	0.387	0.203	0.322
π-PrimeNovo	0.567	0.574	0.697	0.650	0.603	0.697	0.702	0.721	0.531	0.638
DeepNovo	0.286	0.293	0.462	0.422	0.330	0.454	0.436	0.449	0.253	0.376
PointNovo	0.355	0.351	0.534	0.478	0.396	0.513	0.511	0.518	0.298	0.439
Casanovo	0.426	0.341	0.490	0.478	0.406	0.521	0.506	0.537	0.330	0.448
InstaNovo	0.471	0.455	0.559	0.528	0.466	0.732	0.564	0.576	0.416	0.530
Casanovo V2	0.483	0.446	0.599	0.557	0.493	0.618	0.589	0.622	0.446	0.539
π-HelixNovo	0.483	0.392	0.568	0.560	0.473	0.560	0.623	0.596	0.388	0.517
ContraNovo	0.567	0.622	0.674	0.630	0.576	0.672	0.677	0.688	0.486	0.621
CrossNovo	0.596	0.661	0.698	0.660	0.610	0.695	0.716	0.726	0.518	0.654

In the paper: column peptide recall, Mouse: the original table only underlined ContraNovo (Contra.) (0.567); π-PrimeNovo (Prime.) (0.567) ties with it and is underlined here too.
In the paper: column peptide recall, Tomato: the original table bolded π-PrimeNovo (Prime.) (0.697), InstaNovo (Insta) (0.732) and underlined CrossNovo (Ours) (0.695).

RT-GCTnovo: A Peptide De Novo Sequencing Model Incorporating Gated Multi-scale Features and Dynamic Mass Masks (2025) · 1 table

TABLE I

RT-GCTnovo: A Peptide De Novo Sequencing Model Incorporating Gated Multi-scale Features and Dynamic Mass Masks, page 6: Comparison of Average Accuracy (%)

Nine-species benchmark	Peptide accuracy	Amino acid accuracy
DeepNovo	0.376	0.673
Casanovo	0.449	0.678
TSARseqNovo	0.546	0.775
RT-GCTnovo	0.577	0.798

Protein Language Model-Aligned Spectra Embeddings for De Novo Peptide Sequencing (2025) · 2 tables

Table 2

Protein Language Model-Aligned Spectra Embeddings for De Novo Peptide Sequencing, page 7: Performance comparison results in terms of amino acid precision and peptide recall on the ninespecies dataset [55]. The performance of the baseline methods, except for Casanovo v4.2, is reported from [65]. Bolded and underlined results represent the first and second best performance per species (or averaged across species in the last column), respectively.

Nine-species benchmark	Amino acid precision
Nine-species benchmark	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Apis mellifera	Homo sapiens	Methanosarcina mazei	Mus musculus	Vigna mungo	Solanum lycopersicum	Saccharomyces cerevisiae	Average
DeepNovo	0.742	0.602	0.630	0.610	0.694	0.623	0.679	0.731	0.750	0.673
PointNovo	0.768	0.589	0.644	0.606	0.712	0.626	0.730	0.733	0.779	0.687
Casanovo · retrained	0.749	0.603	0.629	0.586	0.679	0.689	0.668	0.721	0.684	0.667
AdaNovo	0.739	0.642	0.650	0.618	0.728	0.646	0.719	0.740	0.793	0.697
Casanovo V2 · retrained	0.790	0.681	0.706	0.676	0.755	0.760	0.748	0.785	0.752	0.739
Casanovo v4.2 · retrained	0.793	0.678	0.705	0.668	0.687	0.756	0.753	0.791	0.768	0.733
PLMNovo ESM-2 8M	0.794	0.684	0.707	0.670	0.755	0.759	0.764	0.790	0.755	0.742
PLMNovo ESM-2 650M	0.797	0.688	0.709	0.676	0.755	0.760	0.766	0.795	0.771	0.746

Nine-species benchmark	Peptide recall
Nine-species benchmark	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Apis mellifera	Homo sapiens	Methanosarcina mazei	Mus musculus	Vigna mungo	Solanum lycopersicum	Saccharomyces cerevisiae	Average
DeepNovo	0.449	0.253	0.330	0.293	0.422	0.286	0.436	0.454	0.462	0.376
PointNovo	0.518	0.298	0.396	0.351	0.478	0.355	0.511	0.513	0.534	0.439
Casanovo · retrained	0.537	0.330	0.406	0.341	0.478	0.426	0.506	0.521	0.490	0.448
AdaNovo	0.528	0.372	0.431	0.373	0.496	0.467	0.546	0.530	0.593	0.481
Casanovo V2 · retrained	0.622	0.446	0.493	0.446	0.557	0.483	0.589	0.618	0.599	0.539
Casanovo v4.2 · retrained	0.603	0.421	0.478	0.437	0.498	0.468	0.558	0.608	0.584	0.517
PLMNovo ESM-2 8M	0.602	0.423	0.484	0.434	0.544	0.470	0.565	0.606	0.571	0.522
PLMNovo ESM-2 650M	0.601	0.425	0.483	0.432	0.544	0.464	0.576	0.606	0.579	0.523

Table 1

Protein Language Model-Aligned Spectra Embeddings for De Novo Peptide Sequencing, page 6: Amino acid-level and peptide-level performance comparison on the MSKB and multi-enzyme test sets [32]. Numbers in bold represent the best result under each (metric, dataset) combination.

a dataset per row group	Amino acid precision		Amino acid recall		Peptide precision
a dataset per row group	MSKB (tryptic)	Multi-enzyme (non-tryptic)	MSKB (tryptic)	Multi-enzyme (non-tryptic)	MSKB (tryptic)	Multi-enzyme (non-tryptic)
Casanovo v4.2 · retrained	0.8919	0.5200	0.8884	0.5211	0.7381	0.2467
PLMNovo ESM-2 8M	0.8973	0.5249	0.8921	0.5283	0.7411	0.2410
PLMNovo ESM-2 650M	0.8976	0.5356	0.8915	0.5323	0.7400	0.2483

MSKB is MassIVE-KB, under the train/test split (200,000 tryptic test spectra) published with Melendez et al.'s enzyme-bias data on Zenodo 12587317, the paper's reference [32]; that split mixes MassIVE-KB v1 and v2.0.15, so no version is recorded. The multi-enzyme rows are the held-out non-tryptic set from the same deposit.

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing (2025) · 8 tables

Table7

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing, page 8: Parameters vs. model performance. ¶ is the extension.

The same table is also printed in Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing (Table7, page 8).

Nine-species benchmark	Amino acid precision	Amino acid recall	Peptide precision	Peptide AUC
Nine-species benchmark	Average	Average	Average	Average
Casanovo · retrained	0.741	0.740	0.529	0.493
Casanovo extended to 12 layers + FFN · retrained	0.750	0.751	0.539	0.494
LIPNovo	0.797	0.797	0.582	0.547

Every number is the nine-species average: the Baseline and LIPNovo rows repeat Table 1's Average column exactly. Baseline is LIPNovo's own retrained Casanovo; Baseline¶ is that model widened to 12 layers plus an FFN to match LIPNovo's size. The '# Params' column (47.4M, 69.4M, 68.4M) is read into the row labels and is not recorded as a measurement.

Table3

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing, page 7: Leave-one-out cross validation compared to baseline on the Nine-species dataset. † means our re-trained results.

The same table is also printed in Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing (Table3, page 7).

Nine-species benchmark	Amino acid precision
Nine-species benchmark	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Apis mellifera	Homo sapiens	Methanosarcina mazei	Mus musculus	Vigna mungo	Solanum lycopersicum	Mean
Casanovo · retrained	0.743	0.754	0.761	0.769	0.754	0.753	0.753	0.717	0.751
LIPNovo	0.806	0.805	0.807	0.805	0.807	0.808	0.794	0.800	0.804

Nine-species benchmark	Amino acid recall
Nine-species benchmark	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Apis mellifera	Homo sapiens	Methanosarcina mazei	Mus musculus	Vigna mungo	Solanum lycopersicum	Mean
Casanovo · retrained	0.745	0.752	0.757	0.770	0.754	0.752	0.755	0.716	0.750
LIPNovo	0.807	0.805	0.806	0.805	0.807	0.807	0.796	0.798	0.804

Nine-species benchmark	Peptide precision
Nine-species benchmark	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Apis mellifera	Homo sapiens	Methanosarcina mazei	Mus musculus	Vigna mungo	Solanum lycopersicum	Mean
Casanovo · retrained	0.559	0.544	0.554	0.575	0.558	0.558	0.549	0.506	0.550
LIPNovo	0.607	0.591	0.606	0.596	0.596	0.607	0.577	0.577	0.595

Nine-species benchmark	Peptide AUC
Nine-species benchmark	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Apis mellifera	Homo sapiens	Methanosarcina mazei	Mus musculus	Vigna mungo	Solanum lycopersicum	Mean
Casanovo · retrained	0.523	0.509	0.519	0.540	0.532	0.521	0.510	0.464	0.515
LIPNovo	0.581	0.563	0.577	0.567	0.565	0.579	0.545	0.544	0.565

Nine-species benchmark	Ptm precision
Nine-species benchmark	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Apis mellifera	Homo sapiens	Methanosarcina mazei	Mus musculus	Vigna mungo	Solanum lycopersicum	Mean
Casanovo · retrained	0.797	0.742	0.762	0.784	0.779	0.799	0.760	0.710	0.767
LIPNovo	0.855	0.764	0.769	0.772	0.812	0.803	0.764	0.777	0.790

Nine-species benchmark	Ptm recall
Nine-species benchmark	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Apis mellifera	Homo sapiens	Methanosarcina mazei	Mus musculus	Vigna mungo	Solanum lycopersicum	Mean
Casanovo · retrained	0.689	0.591	0.624	0.612	0.582	0.608	0.593	0.540	0.605
LIPNovo	0.739	0.647	0.675	0.672	0.630	0.667	0.634	0.627	0.661

Table2 (Seven-species)

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing, page 6: Empirical comparison with state-of-the-art methods on Nine-species, Seven-species, and HC-PT datasets in PTM-level performance. † denotes our retrained results, and other results are given by NovoBench. The best results are marked in bold.

The same table is also printed in Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing (Table2, page 6).

Seven-species benchmark NovoBench split	Ptm precision	Ptm recall
DeepNovo · quoted	0.391	0.373
PointNovo · quoted	0.117	0.094
InstaNovo · quoted	0.126	0.115
Casanovo · quoted	0.360	0.251
AdaNovo · quoted	0.448	0.321
AdaNovo · retrained	0.430	0.356
π-HelixNovo · quoted	0.473	0.366
π-HelixNovo · retrained	0.362	0.370
Casanovo · retrained	0.368	0.292
LIPNovo	0.604	0.498

Table2 (Nine-species)

The same table is also printed in Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing (Table2, page 6).

Nine-species benchmark	Ptm precision	Ptm recall
DeepNovo · quoted	0.576	0.529
PointNovo · quoted	0.629	0.546
InstaNovo · quoted	0.443	0.294
Casanovo · quoted	0.706	0.566
AdaNovo · quoted	0.652	0.570
AdaNovo · retrained	0.678	0.552
π-HelixNovo · quoted	0.680	0.598
π-HelixNovo · retrained	0.723	0.593
Casanovo · retrained	0.755	0.601
LIPNovo	0.765	0.656

Table2 (HC-PT)

The same table is also printed in Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing (Table2, page 6).

ProteomeTools HC-PT (NovoBench)	Ptm precision	Ptm recall
DeepNovo · quoted	0.626	0.615
PointNovo · quoted	0.676	0.740
InstaNovo · quoted	0.350	0.261
Casanovo · quoted	0.501	0.460
AdaNovo · quoted	0.552	0.482
AdaNovo · retrained	0.562	0.532
π-HelixNovo · quoted	0.568	0.667
π-HelixNovo · retrained	0.632	0.566
Casanovo · retrained	0.550	0.582
LIPNovo	0.732	0.745

Table1 (Seven-species)

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing, page 6: Empirical comparison with state-of-the-art methods on Nine-species, Seven-species, and HC-PT datasets in amino acid-level and peptide-level performance. † denotes our retrained results, and other results are provided by NovoBench. The best is marked in bold.

The same table is also printed in Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing (Table1, page 6).

Seven-species benchmark NovoBench split	Amino acid precision	Amino acid recall	Peptide precision	Peptide AUC
DeepNovo · quoted	0.492	0.433	0.204	0.136
PointNovo · quoted	0.196	0.169	0.022	0.007
InstaNovo · quoted	0.192	0.176	0.031	0.009
Casanovo · quoted	0.322	0.327	0.119	0.084
AdaNovo · quoted	0.379	0.385	0.174	0.135
AdaNovo · retrained	0.403	0.405	0.189	0.149
π-HelixNovo · quoted	0.481	0.472	0.234	0.173
π-HelixNovo · retrained	0.465	0.462	0.218	0.156
Casanovo · retrained	0.357	0.366	0.159	0.119
LIPNovo	0.557	0.560	0.327	0.281

Table1 (Nine-species)

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing, page 6: Empirical comparison with state-of-the-art methods on Nine-species, Seven-species, and HC-PT datasets in amino acid-level and peptide-level performance. † denotes our retrained results, and other results are provided by NovoBench. The best is marked in bold.

The same table is also printed in Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing (Table1, page 6).

Nine-species benchmark	Amino acid precision	Amino acid recall	Peptide precision	Peptide AUC
PEAKS · quoted	0.748		0.428
DeepNovo · quoted	0.696	0.638	0.428	0.376
PointNovo · quoted	0.740	0.671	0.480	0.436
InstaNovo · quoted	0.420	0.395	0.164	0.123
Casanovo · quoted	0.697	0.696	0.481	0.439
AdaNovo · quoted	0.698	0.709	0.505	0.469
AdaNovo · retrained	0.681	0.681	0.473	0.439
π-HelixNovo · quoted	0.765	0.758	0.517	0.453
π-HelixNovo · retrained	0.765	0.752	0.509	0.431
Casanovo · retrained	0.741	0.740	0.529	0.493
LIPNovo	0.797	0.797	0.582	0.547

In the paper: column amino acid precision: the original table only underlined π-HelixNovo (π-HelixNovo† (Yang et al., 2024)) (0.765); π-HelixNovo (π-HelixNovo (Yang et al., 2024)) (0.765) ties with it and is underlined here too.

Table1 (HC-PT)

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing, page 6: Empirical comparison with state-of-the-art methods on Nine-species, Seven-species, and HC-PT datasets in amino acid-level and peptide-level performance. † denotes our retrained results, and other results are provided by NovoBench. The best is marked in bold.

The same table is also printed in Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing (Table1, page 6).

ProteomeTools HC-PT (NovoBench)	Amino acid precision	Amino acid recall	Peptide precision	Peptide AUC
DeepNovo · quoted	0.531	0.534	0.313	0.255
PointNovo · quoted	0.623	0.622	0.419	0.373
InstaNovo · quoted	0.289	0.285	0.057	0.034
Casanovo · quoted	0.442	0.453	0.211	0.177
AdaNovo · quoted	0.442	0.451	0.212	0.178
AdaNovo · retrained	0.492	0.496	0.289	0.254
π-HelixNovo · quoted	0.588	0.582	0.356	0.318
π-HelixNovo · retrained	0.532	0.537	0.301	0.261
Casanovo · retrained	0.525	0.530	0.324	0.290
LIPNovo	0.637	0.643	0.458	0.427

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing (2025) · 5 tables

Table6 (Seven-species)

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing, page 18: Performance comparison on the NovoBench benchmark (yeast test species). Scores for models marked with * are quoted from the NovoBench paper or original publications. CV denotes cross-validation results from the original PrimeNovo paper. “–” indicates data not available.

The same table is also printed in Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing (Table6, page 18).

Seven-species benchmark NovoBench split	Peptide precision
Casanovo · quoted	0.12
AdaNovo · quoted	0.17
π-HelixNovo · quoted	0.23
SearchNovo · quoted	0.26
Casanovo pretrained · released	0.05
π-PrimeNovo	0.09
RefineNovo	0.09

In the paper: column peptide precision, yeast: the original table bolded RefineNovo (0.09).

Table6 (Nine-species)

The same table is also printed in Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing (Table6, page 18).

Nine-species benchmark	Peptide precision
Nine-species benchmark	Saccharomyces cerevisiae
Casanovo · quoted	0.48
InstaNovo · quoted	0.53
AdaNovo · quoted	0.50
π-HelixNovo · quoted	0.52
SearchNovo · quoted	0.55
π-PrimeNovo CV · quoted	0.58
Casanovo pretrained · released	0.60
π-PrimeNovo	0.70
RefineNovo	0.71

Table6 (HC-PT)

The same table is also printed in Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing (Table6, page 18).

ProteomeTools HC-PT (NovoBench)	Peptide precision
Casanovo · quoted	0.21
InstaNovo · quoted	0.57
AdaNovo · quoted	0.21
π-HelixNovo · quoted	0.21
SearchNovo · quoted	0.45
π-PrimeNovo	0.85
RefineNovo	0.88

Table2

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing, page 9: Comparison of the performance on the 9-species-V2 benchmark datasets. AT stands for Autoregressive Transformer and NAT stands for non-autoregressive Transformer.

The same table is also printed in Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing (Table2, page 9).

Nine-species benchmark revised (build not stated)	Amino acid precision
Nine-species benchmark revised (build not stated)	Mus musculus	Homo sapiens	Saccharomyces cerevisiae	Methanosarcina mazei	Apis mellifera	Solanum lycopersicum	Vigna mungo	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Average
Casanovo V2 · released	0.813	0.872	0.915	0.877	0.823	0.891	0.891	0.888	0.791	0.862
π-PrimeNovo · released	0.839	0.893	0.932	0.908	0.862	0.909	0.931	0.921	0.827	0.891
RefineNovo	0.850	0.921	0.941	0.921	0.879	0.916	0.931	0.942	0.841	0.907

Nine-species benchmark revised (build not stated)	Peptide recall
Nine-species benchmark revised (build not stated)	Mus musculus	Homo sapiens	Saccharomyces cerevisiae	Methanosarcina mazei	Apis mellifera	Solanum lycopersicum	Vigna mungo	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Average
Casanovo V2 · released	0.555	0.712	0.837	0.754	0.669	0.783	0.772	0.793	0.558	0.714
π-PrimeNovo · released	0.627	0.795	0.884	0.812	0.742	0.824	0.837	0.849	0.626	0.777
RefineNovo	0.637	0.805	0.895	0.827	0.762	0.829	0.862	0.856	0.637	0.790

In the paper: column amino acid precision, Ricebean: the original table only bolded RefineNovo (Ours) (0.931); π-PrimeNovo (Prime. (Zhang et al., 2025)) (0.931) ties with it and is bolded here too.

Table1

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing, page 7: Comparison of the performance on the 9-species-V1 benchmark datasets. The models are categorized by their architecture type: DB represents Database, AR stands for Autoregressive Generation, and NAR denotes Non-Autoregressive Generation. The bold font indicates the best performance.

The same table is also printed in Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing (Table1, page 7).

Nine-species benchmark original (DeepNovo, 2017)	Amino acid precision
Nine-species benchmark original (DeepNovo, 2017)	Mus musculus	Homo sapiens	Saccharomyces cerevisiae	Methanosarcina mazei	Apis mellifera	Solanum lycopersicum	Vigna mungo	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Average
PEAKS	0.600	0.639	0.748	0.673	0.633	0.728	0.644	0.719	0.586	0.663
DeepNovo	0.623	0.610	0.750	0.694	0.630	0.731	0.679	0.742	0.602	0.673
PointNovo	0.626	0.606	0.779	0.712	0.644	0.733	0.730	0.768	0.589	0.687
Casanovo · released	0.689	0.586	0.684	0.679	0.629	0.721	0.668	0.749	0.603	0.667
AdaNovo	0.646	0.618	0.793	0.728	0.650	0.740	0.719	0.739	0.642	0.697
Casanovo V2 · released	0.760	0.676	0.752	0.755	0.706	0.785	0.748	0.790	0.681	0.739
π-PrimeNovo · released	0.784	0.729	0.802	0.801	0.763	0.815	0.822	0.846	0.734	0.788
RefineNovo	0.800	0.730	0.818	0.819	0.780	0.825	0.835	0.854	0.742	0.800

Nine-species benchmark original (DeepNovo, 2017)	Peptide recall
Nine-species benchmark original (DeepNovo, 2017)	Mus musculus	Homo sapiens	Saccharomyces cerevisiae	Methanosarcina mazei	Apis mellifera	Solanum lycopersicum	Vigna mungo	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Average
PEAKS	0.197	0.277	0.428	0.356	0.287	0.403	0.362	0.387	0.203	0.322
DeepNovo	0.286	0.293	0.462	0.422	0.330	0.454	0.436	0.449	0.253	0.376
PointNovo	0.355	0.351	0.534	0.478	0.396	0.513	0.511	0.518	0.298	0.439
Casanovo · released	0.426	0.341	0.490	0.478	0.406	0.521	0.506	0.537	0.330	0.448
AdaNovo	0.467	0.373	0.593	0.496	0.431	0.530	0.546	0.528	0.372	0.481
Casanovo V2 · released	0.483	0.446	0.599	0.557	0.493	0.618	0.589	0.622	0.446	0.539
π-PrimeNovo · released	0.567	0.574	0.697	0.650	0.603	0.697	0.702	0.721	0.531	0.638
RefineNovo	0.583	0.581	0.709	0.667	0.616	0.705	0.720	0.736	0.549	0.653

Universal Biological Sequence Reranking for Improved De Novo Peptide Sequencing (2025) · 3 tables

Table7

Universal Biological Sequence Reranking for Improved De Novo Peptide Sequencing, page 17: Evaluation of RankNovo in comparison to baseline and base methods on the 9-species-V2 test set. Bolded entries indicate the best-performing models. The symbol “†” indicates that the model serves as both a baseline and a base model. “⋆” signifies that the base models were developed and trained by us.

The same table is also printed in RankNovo: A Universal Reranking Approach for Robust De Novo Peptide Sequencing (Table2, page 8).

Nine-species benchmark revised (build not stated)	Amino acid precision
Nine-species benchmark revised (build not stated)	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Apis mellifera	Homo sapiens	Methanosarcina mazei	Mus musculus	Vigna mungo	Solanum lycopersicum	Saccharomyces cerevisiae	Average
Casanovo V2 · released	0.888	0.791	0.823	0.872	0.877	0.813	0.891	0.891	0.915	0.862
ContraNovo · released	0.901	0.807	0.848	0.920	0.896	0.839	0.913	0.898	0.919	0.882
ByNovo	0.92	0.823	0.876	0.917	0.914	0.841	0.932	0.912	0.934	0.897
Casanovo reverse decoding · retrained	0.876	0.804	0.814	0.891	0.867	0.821	0.881	0.891	0.898	0.860
ContraNovo reverse decoding · retrained	0.909	0.815	0.865	0.923	0.901	0.849	0.919	0.907	0.925	0.890
ByNovo reverse decoding	0.919	0.822	0.879	0.912	0.912	0.843	0.932	0.913	0.936	0.897
RankNovo	0.926	0.838	0.885	0.929	0.920	0.860	0.938	0.918	0.938	0.906

Nine-species benchmark revised (build not stated)	Peptide recall
Nine-species benchmark revised (build not stated)	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Apis mellifera	Homo sapiens	Methanosarcina mazei	Mus musculus	Vigna mungo	Solanum lycopersicum	Saccharomyces cerevisiae	Average
Casanovo V2 · released	0.793	0.558	0.669	0.712	0.754	0.555	0.772	0.783	0.837	0.714
ContraNovo · released	0.815	0.575	0.711	0.820	0.780	0.616	0.799	0.794	0.854	0.752
ByNovo	0.833	0.582	0.731	0.789	0.799	0.596	0.814	0.807	0.871	0.758
Casanovo reverse decoding · retrained	0.759	0.558	0.643	0.732	0.723	0.558	0.721	0.768	0.799	0.696
ContraNovo reverse decoding · retrained	0.821	0.581	0.719	0.815	0.779	0.620	0.804	0.798	0.861	0.755
ByNovo reverse decoding	0.831	0.585	0.729	0.781	0.794	0.589	0.815	0.803	0.873	0.756
RankNovo	0.851	0.620	0.752	0.820	0.813	0.629	0.836	0.822	0.885	0.781

Table6

Universal Biological Sequence Reranking for Improved De Novo Peptide Sequencing, page 17: Evaluation of RankNovo in comparison to baseline and base methods on the 9-species-V1 test set. Bolded entries indicate the best-performing models. The symbol “†” indicates that the model serves as both a baseline and a base model. “⋆” signifies that the base models were developed and trained by us.

The same table is also printed in RankNovo: A Universal Reranking Approach for Robust De Novo Peptide Sequencing (Table1, page 8).

Nine-species benchmark original (DeepNovo, 2017)	Amino acid precision
Nine-species benchmark original (DeepNovo, 2017)	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Apis mellifera	Homo sapiens	Methanosarcina mazei	Mus musculus	Vigna mungo	Solanum lycopersicum	Saccharomyces cerevisiae	Average
PEAKS	0.719	0.586	0.633	0.639	0.673	0.600	0.644	0.728	0.748	0.663
DeepNovo	0.742	0.602	0.630	0.610	0.694	0.623	0.679	0.731	0.750	0.673
PointNovo	0.768	0.589	0.644	0.606	0.712	0.626	0.730	0.733	0.779	0.687
Casanovo	0.749	0.603	0.629	0.586	0.679	0.689	0.668	0.721	0.684	0.667
Casanovo V2 · released	0.806	0.685	0.727	0.69	0.774	0.768	0.769	0.799	0.762	0.753
ContraNovo · released	0.828	0.706	0.761	0.771	0.798	0.799	0.804	0.808	0.782	0.784
ByNovo	0.858	0.723	0.791	0.767	0.823	0.803	0.836	0.828	0.804	0.804
Casanovo reverse decoding · retrained	0.804	0.699	0.728	0.719	0.769	0.776	0.782	0.795	0.762	0.759
ContraNovo reverse decoding · retrained	0.839	0.716	0.775	0.782	0.806	0.811	0.816	0.822	0.798	0.796
ByNovo reverse decoding	0.855	0.724	0.794	0.762	0.821	0.81	0.835	0.831	0.762	0.799
RankNovo	0.874	0.746	0.81	0.802	0.84	0.828	0.859	0.844	0.816	0.824

Nine-species benchmark original (DeepNovo, 2017)	Peptide recall
Nine-species benchmark original (DeepNovo, 2017)	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Apis mellifera	Homo sapiens	Methanosarcina mazei	Mus musculus	Vigna mungo	Solanum lycopersicum	Saccharomyces cerevisiae	Average
PEAKS	0.387	0.203	0.287	0.277	0.356	0.197	0.362	0.403	0.428	0.322
DeepNovo	0.449	0.253	0.330	0.293	0.422	0.286	0.436	0.454	0.462	0.376
PointNovo	0.518	0.298	0.396	0.351	0.478	0.355	0.511	0.513	0.534	0.439
Casanovo	0.537	0.330	0.406	0.341	0.478	0.426	0.506	0.521	0.490	0.448
Casanovo V2 · released	0.646	0.46	0.527	0.492	0.592	0.493	0.628	0.637	0.629	0.567
ContraNovo · released	0.684	0.487	0.576	0.624	0.628	0.563	0.676	0.655	0.669	0.618
ByNovo	0.708	0.499	0.597	0.584	0.639	0.545	0.696	0.667	0.676	0.623
Casanovo reverse decoding · retrained	0.628	0.467	0.515	0.511	0.57	0.505	0.611	0.611	0.601	0.558
ContraNovo reverse decoding · retrained	0.682	0.499	0.583	0.606	0.621	0.566	0.673	0.654	0.664	0.616
ByNovo reverse decoding	0.703	0.493	0.59	0.554	0.637	0.543	0.685	0.659	0.629	0.610
RankNovo	0.738	0.539	0.63	0.642	0.672	0.583	0.733	0.691	0.703	0.660

Table1

Universal Biological Sequence Reranking for Improved De Novo Peptide Sequencing, page 7: Evaluation of RankNovo in comparison to baseline and base models on the 9-species-V1 and 9-species-V2 test sets. Bolded entries indicate the best-performing models. The symbol “†” indicates that the model serves as both a baseline and a base model. “⋆” signifies that the base models were developed and trained by us. Here, ByNovo is the best base model. So the performance of three self-trained base models (R-Casa, R-Contra, R-By) is only provided in Appendix D.1.

Nine-species benchmark	Amino acid precision
Nine-species benchmark	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Apis mellifera	Homo sapiens	Methanosarcina mazei	Mus musculus	Vigna mungo	Solanum lycopersicum	Saccharomyces cerevisiae	Average
PEAKS	0.719	0.586	0.633	0.639	0.673	0.600	0.644	0.728	0.748	0.663
DeepNovo	0.742	0.602	0.630	0.610	0.694	0.623	0.679	0.731	0.750	0.673
PointNovo	0.768	0.589	0.644	0.606	0.712	0.626	0.730	0.733	0.779	0.687
Casanovo	0.749	0.603	0.629	0.586	0.679	0.689	0.668	0.721	0.684	0.667
Casanovo V2 · released	0.888	0.791	0.823	0.872	0.877	0.813	0.891	0.891	0.915	0.862
ContraNovo · released	0.901	0.807	0.848	0.920	0.896	0.839	0.913	0.898	0.919	0.882
ByNovo	0.920	0.823	0.876	0.917	0.914	0.841	0.932	0.912	0.934	0.897
RankNovo	0.926	0.838	0.885	0.929	0.920	0.860	0.938	0.918	0.938	0.906

Nine-species benchmark	Peptide recall
Nine-species benchmark	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Apis mellifera	Homo sapiens	Methanosarcina mazei	Mus musculus	Vigna mungo	Solanum lycopersicum	Saccharomyces cerevisiae	Average
PEAKS	0.387	0.203	0.287	0.277	0.356	0.197	0.362	0.403	0.428	0.322
DeepNovo	0.449	0.253	0.330	0.293	0.422	0.286	0.436	0.454	0.462	0.376
PointNovo	0.518	0.298	0.396	0.351	0.478	0.355	0.511	0.513	0.534	0.439
Casanovo	0.537	0.330	0.406	0.341	0.478	0.426	0.506	0.521	0.490	0.448
Casanovo V2 · released	0.793	0.558	0.669	0.712	0.754	0.555	0.772	0.783	0.837	0.714
ContraNovo · released	0.815	0.575	0.711	0.820	0.780	0.616	0.799	0.794	0.854	0.752
ByNovo	0.833	0.582	0.731	0.789	0.799	0.596	0.814	0.807	0.871	0.758
RankNovo	0.851	0.620	0.752	0.820	0.813	0.629	0.836	0.822	0.885	0.781

RankNovo reranks the candidates of six base models. Casanovo V2 and ContraNovo are the released models; ByNovo is RankNovo's own in-house model, with an auxiliary b/y ion-annotation head (its Appendix B.4); R-Casanovo, R-ContraNovo and R-ByNovo are reverse-decoding versions of the three, trained by RankNovo's authors and recorded as the 'reverse decoding' variant. All six were trained on MassIVE-KB and evaluated zero-shot. '†' marks a model that is both baseline and base model, '⋆' one the authors trained. The plain Casanovo row repeats the values other papers print for Casanovo (0.749 and 0.537 on Bacillus) and the paper does not say where they come from. THE TEXT AND THE TABLE DISAGREE on V1: the prose gives RankNovo an amino-acid precision of 0.829 and a peptide recall '6.1%' above ByNovo; the table prints 0.824, and 0.660 against ByNovo's 0.623. The table's values are recorded.
In the paper: column amino acid precision, Average: the original table bolded RankNovo (0.824), RankNovo (0.906).
In the paper: column amino acid precision, Bacillus: the original table bolded RankNovo (0.874), RankNovo (0.926).
In the paper: column amino acid precision, C. bacteria: the original table bolded RankNovo (0.746), RankNovo (0.838).
In the paper: column amino acid precision, Honeybee: the original table bolded RankNovo (0.810), RankNovo (0.885).
In the paper: column amino acid precision, Human: the original table bolded RankNovo (0.802), RankNovo (0.929).
In the paper: column amino acid precision, M. mazei: the original table bolded RankNovo (0.840), RankNovo (0.920).
In the paper: column amino acid precision, Mouse: the original table bolded RankNovo (0.828), RankNovo (0.860).
In the paper: column amino acid precision, Ricebean: the original table bolded RankNovo (0.859), RankNovo (0.938).
In the paper: column amino acid precision, Tomato: the original table bolded RankNovo (0.844), RankNovo (0.918).
In the paper: column amino acid precision, Yeast: the original table bolded RankNovo (0.816), RankNovo (0.938).
In the paper: column peptide recall, Average: the original table bolded RankNovo (0.660), RankNovo (0.781).
In the paper: column peptide recall, Bacillus: the original table bolded RankNovo (0.738), RankNovo (0.851).
In the paper: column peptide recall, C. bacteria: the original table bolded RankNovo (0.539), RankNovo (0.620).
In the paper: column peptide recall, Honeybee: the original table bolded RankNovo (0.630), RankNovo (0.752).
In the paper: column peptide recall, Human: the original table bolded RankNovo (0.642), RankNovo (0.820).
In the paper: column peptide recall, M. mazei: the original table bolded RankNovo (0.672), RankNovo (0.813).
In the paper: column peptide recall, Mouse: the original table bolded RankNovo (0.583), RankNovo (0.629).
In the paper: column peptide recall, Ricebean: the original table bolded RankNovo (0.733), RankNovo (0.836).
In the paper: column peptide recall, Tomato: the original table bolded RankNovo (0.691), RankNovo (0.822).
In the paper: column peptide recall, Yeast: the original table bolded RankNovo (0.703), RankNovo (0.885).

Pairwise Attention: Leveraging Mass Differences to Enhance De Novo Sequencing of Mass Spectra (2025) · 3 tables

Table3

Pairwise Attention: Leveraging Mass Differences to Enhance De Novo Sequencing of Mass Spectra, page 8: Performance after training the models on the MassIVE-KB set. Here we tested on each of the species in the nine-species dataset and report peptide precision at 100% coverage. The models tested are Base and PA, alongside Casanovo’s reported numbers7.

Nine-species benchmark revised (MassIVE 2024)	Peptide precision at coverage 1
Nine-species benchmark revised (MassIVE 2024)	Apis mellifera	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Homo sapiens	Methanosarcina mazei	Mus musculus	Solanum lycopersicum	Saccharomyces cerevisiae	Vigna mungo	Average
Pairwise base	0.618	0.706	0.525	0.737	0.700	0.563	0.735	0.765	0.753	0.678
Pairwise	0.640	0.732	0.549	0.746	0.720	0.579	0.751	0.784	0.783	0.698
Casanovo · quoted	0.662	0.778	0.656	0.740	0.710	0.552	0.799	0.840	0.762	0.722

Table2 (Nine-species · revised (build not stated))

Pairwise Attention: Leveraging Mass Differences to Enhance De Novo Sequencing of Mass Spectra, page 7: Peptide precision at 100% coverage as measured on the V1 and V2 of the nine-species dataset. Here, Base denotes our implementation of a standard transformer, i.e. without pairwise features. PA is our implementation of pairwise attention, and Casanovo is the reported numbers for Casanovo bm in the original publication7. For PA we report the average of 3 runs at seeds 0, 10, and 20, with standard deviation in parentheses.

Nine-species benchmark revised (MassIVE 2024)	Peptide precision
Nine-species benchmark revised (MassIVE 2024)	Apis mellifera	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Homo sapiens	Methanosarcina mazei	Mus musculus	Solanum lycopersicum	Saccharomyces cerevisiae	Vigna mungo	Average
Pairwise base	0.390	0.494	0.356	0.451	0.509	0.410	0.543	0.558	0.525	0.471
Pairwise	0.446	0.583	0.425	0.521	0.579	0.435	0.623	0.631	0.598	0.538
Casanovo · quoted	0.456	0.538	0.468	0.533	0.529	0.395	0.608	0.561	0.428	0.502

Table2 (Nine-species · original (DeepNovo, 2017))

Nine-species benchmark original (DeepNovo, 2017)	Peptide precision
Nine-species benchmark original (DeepNovo, 2017)	Apis mellifera	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Homo sapiens	Methanosarcina mazei	Mus musculus	Solanum lycopersicum	Saccharomyces cerevisiae	Vigna mungo	Average
Pairwise base	0.390	0.536	0.357	0.340	0.503	0.433	0.509	0.537	0.570	0.464
Pairwise	0.463	0.612	0.409	0.391	0.554	0.472	0.590	0.612	0.625	0.523
Casanovo · quoted	0.433	0.573	0.390	0.383	0.515	0.431	0.522	0.580	0.552	0.487

A transformer-based semi-autoregressive framework for high-speed and accurate de novo peptide sequencing (2025) · 1 table

Table1

A transformer-based semi-autoregressive framework for high-speed and accurate de novo peptide sequencing, page 5: Comparative Results of TSARseqNovo, CasaNovo, and π-HelixNovo on the Nine-Species Dataset

Nine-species benchmark	Peptide precision
Nine-species benchmark	Candidatus Thiodiazotropha endoloripes	Homo sapiens	Apis mellifera	Mus musculus	Solanum lycopersicum	Methanosarcina mazei	Bacillus subtilis	Saccharomyces cerevisiae	Vigna mungo	Average
TSARseqNovo	0.419	0.430	0.512	0.532	0.571	0.582	0.612	0.618	0.637	0.535
Casanovo	0.371‡	0.346‡	0.408‡	0.442‡	0.461‡	0.486‡	0.540‡	0.505‡	0.523‡	0.445‡
π-HelixNovo	0.388‡	0.392‡	0.473‡	0.483‡	0.560‡	0.560‡	0.596‡	0.568‡	0.623‡	0.503‡

Nine-species benchmark	Amino acid precision
Nine-species benchmark	Candidatus Thiodiazotropha endoloripes	Homo sapiens	Apis mellifera	Mus musculus	Solanum lycopersicum	Methanosarcina mazei	Bacillus subtilis	Saccharomyces cerevisiae	Vigna mungo	Average
TSARseqNovo	0.706	0.677	0.755	0.812	0.793	0.783	0.827	0.792	0.835	0.776
Casanovo	0.621‡	0.585‡	0.638‡	0.709‡	0.674‡	0.704‡	0.744‡	0.705‡	0.718‡	0.678‡
π-HelixNovo	0.681‡	0.665‡	0.721‡	0.765‡	0.771‡	0.784‡	0.816‡	0.768‡	0.793‡	0.752‡

Nine-species benchmark	Amino acid recall
Nine-species benchmark	Candidatus Thiodiazotropha endoloripes	Homo sapiens	Apis mellifera	Mus musculus	Solanum lycopersicum	Methanosarcina mazei	Bacillus subtilis	Saccharomyces cerevisiae	Vigna mungo	Average
TSARseqNovo	0.699	0.683	0.767	0.822	0.786	0.795	0.828	0.799	0.833	0.779
Casanovo	0.624‡	0.591‡	0.640‡	0.709‡	0.675‡	0.705‡	0.745‡	0.706‡	0.719‡	0.679‡
π-HelixNovo	0.668‡	0.667‡	0.729‡	0.767‡	0.771‡	0.784‡	0.817‡	0.744‡	0.797‡	0.749‡

‡ Not printed in the paper: computed from the differences it prints, as described under the table.
The original table prints only TSARseqNovo's scores, each followed by two 'vs' rows giving its improvement in percentage points over CasaNovo and pi-HelixNovo. The CasaNovo and pi-HelixNovo values here are computed as TSARseqNovo minus that improvement; they are not printed in the paper. The table also misspells pi-HelixNovo as 'pi-HelexiNovo'.

Distilling Non-Autoregressive Model Knowledge for Autoregressive De Novo Peptide Sequencing (2025) · 1 table

Table1

Distilling Non-Autoregressive Model Knowledge for Autoregressive De Novo Peptide Sequencing, page 7: Comparison of the performance of CrossNovo and baseline methods on 9-species-v1 test set. The bold font indicates the best performance.

Nine-species benchmark original (DeepNovo, 2017)	Amino acid precision
Nine-species benchmark original (DeepNovo, 2017)	Mus musculus	Homo sapiens	Saccharomyces cerevisiae	Methanosarcina mazei	Apis mellifera	Solanum lycopersicum	Vigna mungo	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Average
PEAKS	0.600	0.639	0.748	0.673	0.633	0.728	0.644	0.719	0.586	0.663
π-PrimeNovo	0.784	0.729	0.802	0.801	0.763	0.815	0.822	0.846	0.734	0.788
DeepNovo	0.623	0.610	0.750	0.694	0.630	0.731	0.679	0.742	0.602	0.673
PointNovo	0.626	0.606	0.779	0.712	0.644	0.733	0.730	0.768	0.589	0.687
Casanovo	0.689	0.586	0.684	0.679	0.629	0.721	0.668	0.749	0.603	0.667
Casanovo V2	0.760	0.676	0.752	0.755	0.706	0.785	0.748	0.790	0.681	0.739
ContraNovo	0.798	0.771	0.797	0.799	0.745	0.810	0.807	0.828	0.711	0.785
CrossNovo	0.816	0.800	0.814	0.826	0.785	0.830	0.831	0.856	0.740	0.811

Nine-species benchmark original (DeepNovo, 2017)	Peptide recall
Nine-species benchmark original (DeepNovo, 2017)	Mus musculus	Homo sapiens	Saccharomyces cerevisiae	Methanosarcina mazei	Apis mellifera	Solanum lycopersicum	Vigna mungo	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Average
PEAKS	0.197	0.277	0.428	0.356	0.287	0.403	0.362	0.387	0.203	0.322
π-PrimeNovo	0.567	0.574	0.697	0.650	0.603	0.697	0.702	0.721	0.531	0.638
DeepNovo	0.286	0.293	0.462	0.422	0.330	0.454	0.436	0.449	0.253	0.376
PointNovo	0.355	0.351	0.534	0.478	0.396	0.513	0.511	0.518	0.298	0.439
Casanovo	0.426	0.341	0.490	0.478	0.406	0.521	0.506	0.537	0.330	0.448
Casanovo V2	0.483	0.446	0.599	0.557	0.493	0.618	0.589	0.622	0.446	0.539
ContraNovo	0.567	0.622	0.674	0.630	0.576	0.672	0.677	0.688	0.486	0.621
CrossNovo	0.596	0.661	0.698	0.660	0.610	0.695	0.716	0.726	0.518	0.654

In the paper: column peptide recall, Mouse: the original table only underlined ContraNovo (Contra.) (0.567); π-PrimeNovo (Prime.) (0.567) ties with it and is underlined here too.

π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing (2025) · 5 tables

SupplementaryTable7

π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing, page 30: The performance of PrimeNovo compared to Casanovo and Casanovo V2 on six different proteolytic enzymes in the IgG1-Human-HC dataset.

The same table is also printed in π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing (SupplementaryTable7, page 36).

Monoclonal antibody de novo assembly as deposited	Amino acid precision						Peptide recall
Monoclonal antibody de novo assembly as deposited	HC AspN	HC Chymotrypsin	HC GluC	HC LysC	HC Proteinase K	HC Trypsin	HC AspN	HC Chymotrypsin	HC GluC	HC LysC	HC Proteinase K	HC Trypsin
Casanovo	0.3089	0.2114	0.2878	0.4359	0.3844	0.4395	0.0726	0.0298	0.0686	0.2108	0.089	0.1703
Casanovo V2	0.5236	0.4794	0.5214	0.7002	0.6735	0.7374	0.2611	0.2493	0.3133	0.5224	0.4568	0.5483
π-PrimeNovo	0.6059	0.6227	0.6427	0.749	0.7567	0.7818	0.3593	0.446	0.4625	0.6204	0.5972	0.6649

SupplementaryTable6

π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing, page 29: The average performance of PrimeNovo compared to Casanovo and Casanovo V2 across four distinct large-scale MS/MS datasets.

The same table is also printed in π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing (SupplementaryTable6, page 35).

a dataset per evaluation set	Amino acid precision				Peptide recall
a dataset per evaluation set	HCC	IgG1-Human-HC	PT	Three-species	HCC	IgG1-Human-HC	PT	Three-species
Casanovo	0.0908	0.34465	0.4443	0.5633	0.0001	0.1154	0.2873	0.5037
Casanovo V2	0.4968	0.605917000	0.6284	0.8167	0.161	0.4065	0.4543	0.6873
π-PrimeNovo	0.5793	0.693133000	0.7307	0.8767	0.3817	0.5416	0.5857	0.7867

SupplementaryTable5

π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing, page 28: Peptide recall on the revised nine-species benchmark dataset. Note: the bold text indicates the highest performance in each row.

The same table is also printed in π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing (SupplementaryTable5, page 33).

Nine-species benchmark revised (MassIVE 2022)	Peptide recall
Nine-species benchmark revised (MassIVE 2022)	Apis mellifera	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Homo sapiens	Methanosarcina mazei	Mus musculus	Saccharomyces cerevisiae	Solanum lycopersicum	Vigna mungo	Average
Casanovo	0.3578	0.4637	0.3055	0.4468	0.4927	0.4063	0.4657	0.4354	0.3977	0.4191
Casanovo V2	0.57	0.71	0.51	0.65	0.68	0.52	0.73	0.69	0.7	0.64
π-PrimeNovo	0.70	0.80	0.59	0.75	0.78	0.60	0.83	0.78	0.84	0.74

SupplementaryTable4

π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing, page 27: Amino acid precision on the nine-species benchmark dataset. Note: the bold text indicates the highest performance in each row.

The same table is also printed in π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing (SupplementaryTable4, page 33).

Nine-species benchmark	Amino acid precision
Nine-species benchmark	Apis mellifera	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Homo sapiens	Methanosarcina mazei	Mus musculus	Saccharomyces cerevisiae	Solanum lycopersicum	Vigna mungo	Average
PEAKS	0.63	0.72	0.59	0.64	0.67	0.60	0.75	0.73	0.64	0.66
DeepNovo	0.63	0.74	0.60	0.61	0.69	0.62	0.75	0.73	0.68	0.67
PointNovo	0.64	0.77	0.59	0.61	0.71	0.63	0.78	0.73	0.73	0.69
Casanovo	0.63	0.75	0.60	0.59	0.68	0.69	0.68	0.72	0.67	0.67
Casanovo V2	0.71	0.79	0.68	0.68	0.76	0.76	0.75	0.79	0.75	0.74
π-PrimeNovo	0.76	0.84	0.72	0.72	0.80	0.78	0.80	0.82	0.82	0.79

Supplementary Table 3

π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing, page 26: Peptide recall on the nine-species benchmark dataset. Note: the bold text indicates the highest performance in each row.

The same table is also printed in π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing (Supplementary Table 3, page 31).

Nine-species benchmark	Peptide recall
Nine-species benchmark	Apis mellifera	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Homo sapiens	Methanosarcina mazei	Mus musculus	Saccharomyces cerevisiae	Solanum lycopersicum	Vigna mungo	Average
PEAKS	0.29	0.39	0.20	0.28	0.36	0.20	0.43	0.40	0.36	0.32
PepNet	0.28	0.42	0.24	0.23	0.38	0.28	0.37	0.43	0.32	0.33
DeepNovo	0.33	0.45	0.25	0.29	0.42	0.29	0.46	0.45	0.44	0.38
PointNovo	0.40	0.52	0.30	0.35	0.48	0.36	0.53	0.51	0.51	0.44
Casanovo	0.41	0.54	0.33	0.34	0.48	0.43	0.49	0.52	0.51	0.45
Casanovo V2	0.49	0.62	0.45	0.45	0.56	0.48	0.60	0.62	0.59	0.54
π-PrimeNovo	0.60	0.72	0.53	0.57	0.65	0.57	0.70	0.70	0.70	0.64

AdaNovo: Towards Robust De Novo Peptide Sequencing in Proteomics against Data Biases (2024) · 4 tables

Table4

AdaNovo: Towards Robust De Novo Peptide Sequencing in Proteomics against Data Biases, page 8: Comparisons with alternative methods in terms of identifying amino acids with PTMs. All results are reported using the yeast dataset as test set.

Nine-species benchmark	Amino acid precision	Peptide precision	Ptm precision
Casanovo · retrained	0.753	0.568	0.552
Casanovo re-weight · retrained	0.762	0.576	0.564
Casanovo focal loss · retrained	0.745	0.543	0.550
AdaNovo w/o PSM-level objective	0.793	0.594	0.616
AdaNovo	0.825	0.612	0.665

Table3

AdaNovo: Towards Robust De Novo Peptide Sequencing in Proteomics against Data Biases, page 8: Models’ Performance on mass spectrum dataset with synthetic noise. The results are reported using the Clam bacteria as test set.

Nine-species benchmark	Amino acid precision	Peptide precision
Casanovo · retrained	0.582	0.297
AdaNovo w/o PSM-level objective	0.617	0.336
AdaNovo w/o AA-level objective	0.644	0.372
AdaNovo	0.656	0.397

Table2

AdaNovo: Towards Robust De Novo Peptide Sequencing in Proteomics against Data Biases, page 8: Ablations on amino acid-level (AA-level) and peptide-level training strategies. The results are reported using the Human datasets as test set.

Nine-species benchmark	Amino acid precision	Peptide precision	Ptm precision
Nine-species benchmark	Homo sapiens	Homo sapiens	Homo sapiens
Casanovo · retrained	0.585	0.343	0.300
AdaNovo w/o PSM-level objective	0.607	0.360	0.478
AdaNovo w/o AA-level objective	0.594	0.349	0.314
AdaNovo	0.618	0.373	0.483

Table1

AdaNovo: Towards Robust De Novo Peptide Sequencing in Proteomics against Data Biases, page 7: Empirical comparison of previous models on 9-species benchmark. The best and the second best results are highlighted with bold and underlined, respectively. The reproduced performance of Casanovo is denoted as Casanovo (rep.). Following Casanovo [35], we train 5 models with different random initializations and report the standard deviation when the test dataset is Mouse or Human. The other standard deviations are not reported because it would be too computationally expensive.

Nine-species benchmark	Peptide precision
Nine-species benchmark	Mus musculus	Homo sapiens	Saccharomyces cerevisiae	Methanosarcina mazei	Apis mellifera	Solanum lycopersicum	Vigna mungo	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Average
DeepNovo · quoted	0.286	0.293	0.462	0.422	0.330	0.454	0.436	0.449	0.253	0.376
PointNovo · quoted	0.355	0.351	0.534	0.478	0.396	0.513	0.511	0.518	0.298	0.439
Casanovo · quoted	0.443	0.367	0.561	0.486	0.408	0.460	0.437	0.540	0.371	0.453
Casanovo rep. · retrained	0.449	0.343	0.568	0.474	0.422	0.463	0.549	0.513	0.347	0.459
AdaNovo	0.493	0.373	0.612	0.523	0.431	0.552	0.546	0.561	0.397	0.499

Nine-species benchmark	Amino acid precision
Nine-species benchmark	Mus musculus	Homo sapiens	Saccharomyces cerevisiae	Methanosarcina mazei	Apis mellifera	Solanum lycopersicum	Vigna mungo	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Average
DeepNovo · quoted	0.623	0.610	0.750	0.694	0.630	0.731	0.679	0.742	0.602	0.673
PointNovo · quoted	0.626	0.606	0.779	0.712	0.644	0.733	0.730	0.768	0.589	0.687
Casanovo · quoted	0.562	0.424	0.591	0.518	0.461	0.471	0.442	0.573	0.405	0.494
Casanovo rep. · retrained	0.612	0.585	0.753	0.686	0.640	0.720	0.727	0.711	0.617	0.672
AdaNovo	0.667	0.618	0.825	0.757	0.650	0.767	0.719	0.788	0.656	0.716

The plain Casanovo column is quoted: each value is Casanovo's own Table 2 precision at coverage 1, set here under AdaNovo's precision. AdaNovo's own run of Casanovo is 'Casanovo (rep.)', the column the arXiv version prints as plain 'Casanovo'.
In the paper: column amino acid precision, Bacillus: the original table bolded PointNovo (0.768), AdaNovo (0.788) and underlined DeepNovo (0.742).

ReNovo: Retrieval-Based De Novo Mass Spectrometry Peptide Sequencing (2024) · 3 tables

Table1 (Seven-species)

ReNovo: Retrieval-Based De Novo Mass Spectrometry Peptide Sequencing, page 7: Empirical comparison of de novo peptide sequencing models. The best and the second best are highlighted with bold and underline, respectively.

Seven-species benchmark NovoBench split	Peptide precision	Peptide AUC	Amino acid precision	Amino acid recall
DeepNovo	0.204	0.136	0.492	0.433
PointNovo	0.022	0.007	0.196	0.169
Casanovo	0.119	0.084	0.322	0.327
π-HelixNovo	0.234	0.173	0.481	0.472
AdaNovo	0.174	0.135	0.379	0.385
ReNovo	0.278	0.228	0.512	0.514

Table1 (Nine-species)

Nine-species benchmark	Peptide precision	Peptide AUC	Amino acid precision	Amino acid recall
DeepNovo	0.428	0.376	0.696	0.638
PointNovo	0.480	0.436	0.740	0.671
Casanovo	0.481	0.439	0.697	0.696
π-HelixNovo	0.517	0.453	0.765	0.758
AdaNovo	0.505	0.469	0.698	0.709
ReNovo	0.568	0.528	0.770	0.769

Table1 (HC-PT)

ProteomeTools HC-PT (NovoBench)	Peptide precision	Peptide AUC	Amino acid precision	Amino acid recall
DeepNovo	0.313	0.255	0.531	0.534
PointNovo	0.419	0.373	0.623	0.622
Casanovo	0.211	0.177	0.442	0.453
π-HelixNovo	0.356	0.318	0.588	0.582
AdaNovo	0.212	0.178	0.442	0.451
ReNovo	0.467	0.436	0.651	0.648

ContraNovo: A Contrastive Learning Approach to Enhance De Novo Peptide Sequencing (2024) · 2 tables

Table2

ContraNovo: A Contrastive Learning Approach to Enhance De Novo Peptide Sequencing, page 7: Comparison of the performance of ContraNovo and CasaNovoV2 methods on 9-species-V2 test set. The bold font indicates the best performance.

Nine-species benchmark revised (build not stated)	Amino acid precision
Nine-species benchmark revised (build not stated)	Mus musculus	Homo sapiens	Saccharomyces cerevisiae	Methanosarcina mazei	Apis mellifera	Solanum lycopersicum	Vigna mungo	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Average
Casanovo V2	0.792	0.836	0.851	0.828	0.759	0.823	0.858	0.852	0.760	0.818
ContraNovo	0.816	0.893	0.875	0.863	0.804	0.860	0.909	0.861	0.778	0.851

Nine-species benchmark revised (build not stated)	Peptide precision
Nine-species benchmark revised (build not stated)	Mus musculus	Homo sapiens	Saccharomyces cerevisiae	Methanosarcina mazei	Apis mellifera	Solanum lycopersicum	Vigna mungo	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Average
Casanovo V2	0.520	0.651	0.729	0.675	0.572	0.692	0.702	0.717	0.505	0.640
ContraNovo	0.566	0.745	0.719	0.716	0.608	0.716	0.791	0.713	0.521	0.677

Table 1

ContraNovo: A Contrastive Learning Approach to Enhance De Novo Peptide Sequencing, page 6: Comparison of the performance of ContraNovo and five baseline methods on 9-species-V1 test set. The bold font indicates the best performance.

Nine-species benchmark original (DeepNovo, 2017)	Amino acid precision
Nine-species benchmark original (DeepNovo, 2017)	Mus musculus	Homo sapiens	Saccharomyces cerevisiae	Methanosarcina mazei	Apis mellifera	Solanum lycopersicum	Vigna mungo	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Average
PEAKS	0.600	0.639	0.748	0.673	0.633	0.728	0.644	0.719	0.586	0.663
DeepNovo	0.623	0.610	0.750	0.694	0.630	0.731	0.679	0.742	0.602	0.673
PointNovo	0.626	0.606	0.779	0.712	0.644	0.733	0.730	0.768	0.589	0.687
Casanovo	0.689	0.586	0.684	0.679	0.629	0.721	0.668	0.749	0.603	0.667
Casanovo V2	0.760	0.676	0.752	0.755	0.706	0.785	0.748	0.790	0.681	0.739
ContraNovo	0.798	0.771	0.797	0.799	0.745	0.810	0.807	0.828	0.711	0.785

Nine-species benchmark original (DeepNovo, 2017)	Peptide precision
Nine-species benchmark original (DeepNovo, 2017)	Mus musculus	Homo sapiens	Saccharomyces cerevisiae	Methanosarcina mazei	Apis mellifera	Solanum lycopersicum	Vigna mungo	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Average
PEAKS	0.197	0.277	0.428	0.356	0.287	0.403	0.362	0.387	0.203	0.322
DeepNovo	0.286	0.293	0.462	0.422	0.330	0.454	0.436	0.449	0.253	0.376
PointNovo	0.355	0.351	0.534	0.478	0.396	0.513	0.511	0.518	0.298	0.439
Casanovo	0.426	0.341	0.490	0.478	0.406	0.521	0.506	0.537	0.330	0.448
Casanovo V2	0.483	0.446	0.599	0.557	0.493	0.618	0.589	0.622	0.446	0.539
ContraNovo	0.567	0.622	0.674	0.630	0.576	0.672	0.677	0.688	0.486	0.621

AdaNovo: Adaptive De Novo Peptide Sequencing with Conditional Mutual Information (2024) · 5 tables

Table5

AdaNovo: Adaptive De Novo Peptide Sequencing with Conditional Mutual Information, page 8: Comparisons with alternative methods in terms of identi- to reduced precision in identification. To address these isfying amino acids with PTMs. All results are for the yeast test set, sues, we introduce a novel approach involving the calculawhich is one of 9-species benchmark (Tran et al., 2017). tion of conditional mutual information between the spec-

Nine-species benchmark	Amino acid precision	Peptide precision
Casanovo · retrained	0.753	0.568
Casanovo re-weight · retrained	0.762	0.576
Casanovo focal loss · retrained	0.745	0.543
AdaNovo w/o PSM-level MI	0.784	0.582
AdaNovo	0.793	0.593

Table4

AdaNovo: Adaptive De Novo Peptide Sequencing with Conditional Mutual Information, page 8: Models’ Performance on mass spectrum dataset with syn- datasets are derived from different species, there exists a thetic noise. The results are for the Clam bacteria test set, which is significant difference in the distribution of PTMs quantities.

Nine-species benchmark	Amino acid precision	Peptide precision
Casanovo · retrained	0.582	0.297
AdaNovo w/o PSM-level MI	0.586	0.311
AdaNovo w/o AA-level CMI	0.614	0.335
AdaNovo	0.621	0.342

Table3

AdaNovo: Adaptive De Novo Peptide Sequencing with Conditional Mutual Information, page 8: Ablations on amino acid-level (AA-level) and peptidelevel adaptive training strategies. The results are for the Human test set, which is one of 9-species benchmark (Tran et al., 2017).

Nine-species benchmark	Amino acid precision	Peptide precision	Ptm precision
Nine-species benchmark	Homo sapiens	Homo sapiens	Homo sapiens
Casanovo · retrained	0.585	0.343	0.300
AdaNovo w/o PSM-level MI	0.607	0.360	0.478
AdaNovo w/o AA-level CMI	0.594	0.349	0.314
AdaNovo	0.618	0.373	0.483

Table2

AdaNovo: Adaptive De Novo Peptide Sequencing with Conditional Mutual Information, page 7: Empirical comparison of de novo sequencing models in terms of identifying amino acids with PTMs. The best and the second best results are highlighted bold and underlined, respectively.

Nine-species benchmark	Ptm precision
Nine-species benchmark	Homo sapiens	Vigna mungo	Candidatus Thiodiazotropha endoloripes	Bacillus subtilis
DeepNovo · quoted	0.369	0.644	0.510	0.483
PointNovo · quoted	0.415	0.653	0.526	0.524
Casanovo · retrained	0.398	0.646	0.508	0.470
AdaNovo	0.483	0.689	0.575	0.565

Table1

AdaNovo: Adaptive De Novo Peptide Sequencing with Conditional Mutual Information, page 7: Empirical comparison of de novo sequencing models. The table lists the peptide-level and amino acid-level precision of three competing models on all nine benchmark cross-validation folds. Each fold’s test set contains spectra from a single species. Kindly note that peptide-level performance measures are the primary quantifier of the model’s practical utility because the goal is to assign a complete peptide sequence to each spectrum. The best and the second best results are highlighted bold and underlined, respectively.

Nine-species benchmark	Peptide precision
Nine-species benchmark	Mus musculus	Homo sapiens	Saccharomyces cerevisiae	Methanosarcina mazei	Apis mellifera	Solanum lycopersicum	Vigna mungo	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes
DeepNovo · quoted	0.286	0.293	0.462	0.422	0.330	0.454	0.436	0.449	0.253
PointNovo · quoted	0.355	0.351	0.534	0.478	0.396	0.513	0.511	0.518	0.298
Casanovo · retrained	0.449	0.343	0.568	0.474	0.422	0.463	0.549	0.513	0.347
AdaNovo	0.467	0.373	0.593	0.496	0.431	0.530	0.546	0.528	0.372

Nine-species benchmark	Amino acid precision
Nine-species benchmark	Mus musculus	Homo sapiens	Saccharomyces cerevisiae	Methanosarcina mazei	Apis mellifera	Solanum lycopersicum	Vigna mungo	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes
DeepNovo · quoted	0.623	0.610	0.750	0.694	0.630	0.731	0.679	0.742	0.602
PointNovo · quoted	0.626	0.606	0.779	0.712	0.644	0.733	0.730	0.768	0.589
Casanovo · retrained	0.612	0.585	0.753	0.686	0.640	0.720	0.727	0.718	0.617
AdaNovo	0.646	0.618	0.793	0.728	0.650	0.740	0.719	0.739	0.642

Papers describing it (7)

De novo mass spectrometry peptide sequencing with a transformer model (2022, bioRxiv, preprint)
De novo mass spectrometry peptide sequencing with a transformer model (2022, ICML 2022, ML conference)
Sequence-to-sequence translation from mass spectra to peptides with a transformer model (2023, bioRxiv, preprint)
Sequence-to-sequence translation from mass spectra to peptides with a transformer model (2024, Nature Communications, peer-reviewed)
Accounting for Digestion Enzyme Bias in Casanovo (2024, Journal of Proteome Research, peer-reviewed)
Improvements to CasaNovo, a deep learning de novo peptide sequencer (2025, bioRxiv, preprint)
Improvements to Casanovo, a Deep Learning De Novo Peptide Sequencer (2025, Journal of Proteome Research, peer-reviewed)

Papers using it (12)

Applications and evaluations that ran this method. They are not counted among its authors below.

PepPr: De Novo peptide sequencing with peptide priors (2026, bioRxiv, preprint)
pXg: Comprehensive and Reliable Identification of MHC-I Antigens Using De Novo Peptide Sequencing and Immunotranscriptomic Datasets (2026, Methods in Molecular Biology, peer-reviewed)
Beyond Best Guesses: A Multi-Level Confidence Assessment Framework for De Novo Peptide Sequencing Results (2026, HUPO 2026 World Congress, presentation)
Can we Confidently Sequence the „Peptide Dark Matter“? Reaching a Consensus for De Novo Peptide Sequencing (2026, Proceedings of the 38th European Peptide Symposium, presentation)
Spectra Fragment-Ion and Amino Acid Probability Prediction for Peptide Sequencing (2026, thesis)
Reference-free protein sequencing by consensus assembly of redundant de novo peptide reads (2026, bioRxiv, preprint)
Tandem Mass Spectra Representation Design for Transformer-Based De Novo Peptide Sequencing (2026, thesis)
CasanovoGUI: a cross-platform desktop application for deep learning-based de novo peptide sequencing with Casanovo (2026, bioRxiv, preprint)
Self-assembling proteins compose the chemically resistant shell biomaterial of planktonic tintinnid ciliates (2026, Nature Communications, peer-reviewed)
NovoTax: prokaryotic strain identification from mass spectrometry-based proteomics data (2026, bioRxiv, preprint)
Systematic benchmarking of mass spectrometry-based antibody sequencing reveals methodological biases (2025, Cell Systems, peer-reviewed)
Systematic benchmarking of mass spectrometry-based antibody sequencing reveals methodological biases (2024, bioRxiv, preprint)

Authors (17)

Melih Yilmaz, William E. Fondrie, Wout Bittremieux, Sewoong Oh, William Stafford Noble, Carlo F. Melendez, Rowan Nelson, Varun Ananth, Justin Sanders, Gwenneth Straub, Chris Hsu, Daniela Klaproth-Andrade, Michael Riffle, Bo Wen, Lingwen Xu, Michael J. MacCoss, Marina Pominova

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Code

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Data

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Deposited

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Table2

Compared against in other papers (24)

Table 1 (Nine-species · revised (build not stated))

Table 1 (Nine-species · original (DeepNovo, 2017))

Fig. 4b

Supplementary Fig. 3c

Supplementary Fig. 3b

Supplementary Fig. 3a

Table 26

Table25

Table1 (MSKB-final)

Table1 (Kingdoms de novo)

Table2 (Seven-species)

Table2 (Nine-species)

Table2 (HC-PT)

Table1 (Seven-species)

Table1 (Nine-species)

Table1 (HC-PT)

Table S12

Table5

Table4

Table2 (Seven-species)

Table2 (Nine-species)

Table2 (HC-PT)

Table1 (Seven-species)

Table1 (Nine-species)

Table1 (HC-PT)

Table2

Table5

Table4

Table3 (Seven-species)

Table3 (Nine-species)

Table3 (HC-PT)

Table2 (Seven-species)

Table2 (Nine-species)

Table2 (HC-PT)

Table2 (Seven-species)

Table2 (Nine-species)

Table2 (HC-PT)

Table 3 (Seven-species)

Table 3 (Nine-species)

Table 3 (HC-PT)

Table8

Table3

Table2 (Seven-species)

Table2 (Nine-species)

Table2 (HC-PT)

Table1 (Seven-species)

Table1 (Nine-species)

Table1 (HC-PT)

Table7

Table6

Table2

Table1

TABLE I

Table 2

Table 1

Table7

Table3

Table2 (Seven-species)

Table2 (Nine-species)

Table2 (HC-PT)

Table1 (Seven-species)

Table1 (Nine-species)

Table1 (HC-PT)

Table6 (Seven-species)

Table6 (Nine-species)

Table6 (HC-PT)

Table2

Table1

Table7

Table6