π-HelixNovo

algorithm · Transformer (AR)

π-HelixNovo: algorithm · Transformer (AR). Complementary spectra

Complementary spectra

Kind	algorithm
Deep learning	yes
Acquisition	DDA
Also known as	PandaNovo
Family	Transformer (AR)

Code

• https://github.com/PHOENIXcenter/pi-HelixNovo

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

Checkpoints

Version	Trained on	Host	Licence	Size	Checked
project archive v2	—	Zenodo archival	CC-BY-4.0	1.6 GB	live 2026-10-02
project archive	—	Zenodo archival	CC-BY-4.0	22.6 GB	live 2026-10-02

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

• BALF proteomics - In-depth proteomic analysis of human bronchoalveolar lavage fluid towards the biomarker discovery for (as deposited) · 2023 preprint, 2024 peer-reviewed
• Casanovo data set and model weights (as deposited) · 2024 peer-reviewed
• Diabetes causes marked inhibition of mitochondrial metabolism in pancreatic β-cells (as deposited) · 2023 preprint, 2024 peer-reviewed
• HeLa proteome of 12,250 protein-coding genes (as deposited) · 2024 peer-reviewed
• Low-density lipoprotein receptor-related protein 1 (LRP1)-derived peptides protect against aggregation of LDL and choles (as deposited) · 2023 preprint, 2024 peer-reviewed
• MassIVE-KB (v1) · 2023 preprint, 2024 peer-reviewed
• Mining waste streams of food production for bioactive plant polysaccharides that affect the fitness and expressed activi (as deposited) · 2023 preprint, 2024 peer-reviewed
• Nine-species benchmark (original (DeepNovo, 2017)) · 2023 preprint, 2024 peer-reviewed
• Proteomic analysis of six different tissues from the Atlantic bottlenose dolphin (Tursiops truncatus) (as deposited) · 2023 preprint, 2024 peer-reviewed

Evaluated on

• ABRF PRG 2018 HeLa DDA (PRG 2018 runs) · 2023 preprint, 2024 peer-reviewed
• Anti-FLAG-M2 antibody sequencing (as deposited) · 2024 peer-reviewed
• Casanovo data set and model weights (as deposited) · 2024 peer-reviewed
• HeLa proteome of 12,250 protein-coding genes (as deposited) · 2024 peer-reviewed
• High-resolution spatially-resolved proteome mapping using automated, sacrificial liquid-mediated sample transfer from la (as deposited) · 2023 preprint, 2024 peer-reviewed
• Mining waste streams of food production for bioactive plant polysaccharides that affect the fitness and expressed activi (as deposited) · 2023 preprint, 2024 peer-reviewed
• Monoclonal antibody de novo assembly (as deposited) · 2024 peer-reviewed
• Nine-species benchmark (original (DeepNovo, 2017)) · 2023 preprint, 2024 peer-reviewed
• Proteome of the rodent malaria parasite Plasmodium berghei liver stage merosomes (as deposited) · 2023 preprint, 2024 peer-reviewed
• Supplementary Data for “Comprehensive evaluation of peptide de novo sequencing tools for monoclonal antibody assembly” (as deposited) · 2024 peer-reviewed

Benchmarks

• denovo_benchmarks: median peptide-level average precision 0.825 over 86 datasets, median rank 5 of 15 (version bm-1.1.0).
• ProteoBench, on the nine-species benchmark, ProteoBench selection: peptide-level AUC 0.626; precision 0.595 at 100% coverage; amino-acid AUC 0.740 (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.

Compared against in other papers (10)

40 tables from 10 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.

MassNet: billion-scale AI-ready mass spectral corpus empowering scalable deep learning in proteomics (2026) · 2 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
	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
	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. 3a

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.

Nine-species benchmark original (DeepNovo, 2017)	Peptide recall
	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.

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)

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.

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)

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.

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)

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.

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)

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.

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

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)

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.

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)

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.

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)

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.

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)

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.

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)

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.

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)

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.

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)

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.

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)

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.

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)

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.

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)

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.

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)

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.

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)

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.

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) · 1 table

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
	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
	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).

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)

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).

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)

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).

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) · 3 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)

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).

Nine-species benchmark	Peptide precision
	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)

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).

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

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
	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
	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
	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'.

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)

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.

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)

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.

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)

• Introducing PandaNovo for practical large-scale de novo peptide sequencing (2023, bioRxiv, preprint)
• Introducing π-HelixNovo for practical large-scale de novo peptide sequencing (2024, Briefings in Bioinformatics, peer-reviewed)

Authors (12)

Tingpeng Yang, Tianze Ling, Boyan Sun, Zhendong Liang, Fan Xu, Xiansong Huang, Linhai Xie, Yonghong He, Leyuan Li, Fuchu He, Yu Wang, Cheng Chang

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