π-PrimeNovo

algorithm · Transformer (NAR)

π-PrimeNovo: algorithm · Transformer (NAR). NAR Transformer (CTC)

NAR Transformer (CTC)

Kind algorithm
Deep learning yes
Acquisition DDA
Also known as PrimeNovo
Family Transformer (NAR)

Code

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

Checkpoints

Version Trained on Host Licence Size Checked Backup
MassIVE MassIVE-KB Google Drive MIT 387 MB verified 2026-10-02 copy
phosphorylation fine-tuned from model_massive.ckpt on the 2020-Cell-LUAD dataset; predicts one extra token B for Phosphorylation (+79.97) Google Drive MIT 387 MB verified 2026-10-02 copy

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. Where a copy is linked, it is a backup of someone else’s weights kept in case the original link goes stale; the original is the link to cite and to prefer.

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

Evaluated on

Also used, purpose not recorded

Benchmarks

  • denovo_benchmarks: median peptide-level average precision 0.802 over 86 datasets, median rank 5.5 of 15 (version bm-1.0.0).
  • ProteoBench, on the nine-species benchmark, ProteoBench selection: peptide-level AUC 0.773; precision 0.682 at 88% coverage; amino-acid AUC 0.839 (version 1.0, beam search + PMC [1 Da], submitted 2026-08-07).

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

Reported comparisons (5)

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.

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
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

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
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

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
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

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
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

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
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

Compared against in other papers (7)

19 tables from 7 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
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
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))

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
original (DeepNovo, 2017) 		Amino acid precision
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
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

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.

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
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. 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
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

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.

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

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.

Prime-DiffNovo: Accurate Peptide De Novo Sequencing via Non-autoregressive Generation and Diffusion Refinement (2026) · 2 tables

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

Prime-DiffNovo: Accurate Peptide De Novo Sequencing via Non-autoregressive Generation and Diffusion Refinement, page 8: Peptide accuracy comparison of different de novo peptide sequencing methods across nine species on two benchmark datasets.

Nine-species benchmark
revised (build not stated) 		Peptide accuracy
Apis mellifera Bacillus subtilis Candidatus Thiodiazotropha endoloripes Homo sapiens Methanosarcina mazei Mus musculus Saccharomyces cerevisiae Solanum lycopersicum Vigna mungo Weighted average
InstaNovo v1.1 0.625 0.704 0.512 0.670 0.666 0.504 0.758 0.721 0.679 0.698
InstaNovo+ v1.1 0.646 0.731 0.545 0.687 0.691 0.523 0.779 0.743 0.704 0.723
π-PrimeNovo 0.602 0.702 0.549 0.704 0.681 0.537 0.671 0.699 0.731 0.682
Prime-DiffNovo 0.658 0.751 0.581 0.733 0.717 0.566 0.817 0.771 0.748 0.749

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

Prime-DiffNovo: Accurate Peptide De Novo Sequencing via Non-autoregressive Generation and Diffusion Refinement, page 8: Peptide accuracy comparison of different de novo peptide sequencing methods across nine species on two benchmark datasets.

Nine-species benchmark
original (DeepNovo, 2017) 		Peptide accuracy
Apis mellifera Bacillus subtilis Candidatus Thiodiazotropha endoloripes Homo sapiens Methanosarcina mazei Mus musculus Saccharomyces cerevisiae Solanum lycopersicum Vigna mungo Weighted average
InstaNovo v1.1 0.526 0.629 0.444 0.506 0.571 0.509 0.602 0.615 0.613 0.565
InstaNovo+ v1.1 0.555 0.662 0.475 0.516 0.596 0.525 0.634 0.642 0.662 0.593
π-PrimeNovo 0.592 0.697 0.511 0.566 0.627 0.558 0.688 0.679 0.678 0.630
Prime-DiffNovo 0.607 0.716 0.526 0.572 0.644 0.566 0.698 0.690 0.709 0.645
Discrete Diffusion with Physical Mass Constraints for De Novo Peptide Sequencing (2026) · 2 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

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
Bidirectional Representations Augmented Autoregressive Biological Sequence Generation (2025) · 3 tables

Table 8

Bidirectional Representations Augmented Autoregressive Biological Sequence Generation, page 28: Performance comparison on five representative PTMs after fine-tuning on the 21-PTM dataset. Bold values indicate the best performance for each column.

The same table is also printed in Bidirectional Representations Augmented Autoregressive Biological Sequence Generation (Table 8, page 28).

ProteomeTools
21-PTM subset 		Ptm accuracy Amino acid recall
Acetylation Biotinylation Crotonylation Butyrylation Dimethylation Acetylation Biotinylation Crotonylation Butyrylation Dimethylation
π-PrimeNovo 0.98 0.99 0.98 0.97 0.95 0.95 0.83 0.90 0.82 0.84
CrossNovo 0.98 0.98 0.98 0.98 0.97 0.96 0.83 0.86 0.85 0.85
ProteomeTools
21-PTM subset 		Peptide recall
Acetylation Biotinylation Crotonylation Butyrylation Dimethylation
π-PrimeNovo 0.89 0.70 0.79 0.65 0.66
CrossNovo 0.90 0.72 0.78 0.69 0.68

In the paper: column ptm accuracy, Acetylation: the original table only bolded CrossNovo (0.98); π-PrimeNovo (PrimeNovo) (0.98) ties with it and is bolded here too.
In the paper: column ptm accuracy, Crotonylation: the original table only bolded CrossNovo (0.98); π-PrimeNovo (PrimeNovo) (0.98) ties with it and is bolded here too.

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
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
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
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).

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)

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

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
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
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
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
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
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
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
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.

Papers describing it (2)

Papers using it (2)

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Authors (18)

Xiang Zhang (Shanghai AI Lab), Tianze Ling, Zhi Jin, Sheng Xu, Zhiqiang Gao, Boyan Sun, Zijie Qiu, Nanqing Dong, Guangshuai Wang, Guibin Wang, Leyuan Li, Muhammad Abdul-Mageed, Laks V.S. Lakshmanan, Wanli Ouyang, Cheng Chang, Siqi Sun, Jiaqi Wei, Fuchu He

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