AdaNovo

algorithm · Transformer (AR)

AdaNovo: algorithm · Transformer (AR). Mutual info for PTMs

Mutual info for PTMs

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

Code

https://github.com/Westlake-OmicsAI/adanovo_v1

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

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.

Trained on

Nine-species benchmark (original (DeepNovo, 2017)) · 2024 preprint, 2024 ML conference

Evaluated on

Nine-species benchmark (version not stated) · 2024 preprint, 2024 ML conference

Benchmarks

denovo_benchmarks: median peptide-level average precision 0.535 over 86 datasets, median rank 11 of 15 (version bm-1.0.0).
ProteoBench, on the nine-species benchmark, ProteoBench selection: peptide-level AUC 0.837; precision 0.589 at 98% coverage; amino-acid AUC 0.882 (version 1.0, beam search, submitted 2026-08-07).

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

Reported comparisons (9)

The comparison tables 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.

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

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

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

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

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

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).

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

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

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

Compared against in other papers (11)

47 tables from 11 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

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

Discrete Diffusion with Physical Mass Constraints for De Novo Peptide Sequencing (2026) · 6 tables

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) · 6 tables

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) · 6 tables

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

Protein Language Model-Aligned Spectra Embeddings for De Novo Peptide Sequencing (2025) · 1 table

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

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing (2025) · 6 tables

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) · 4 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

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

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

Papers describing it (2)

AdaNovo: Adaptive De Novo Peptide Sequencing with Conditional Mutual Information (2024, arXiv, preprint)
AdaNovo: Towards Robust De Novo Peptide Sequencing in Proteomics against Data Biases (2024, NeurIPS 2024, ML conference)

Authors (12)

Jun Xia, Shaorong Chen, Jingbo Zhou, Tianze Ling, Wenjie Du, Sizhe Liu, Stan Z. Li, Xiaojun Shan, Zhangyang Gao, Cheng Tan, Bozhen Hu, Jiangbin Zheng

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