LIPNovo

algorithm · Latent imputation

LIPNovo: algorithm · Latent imputation. Latent imputation

Latent imputation

Kind algorithm
Deep learning yes
Acquisition DDA
Family Latent imputation

Code

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

Evaluated on

Reported comparisons (17)

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 (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

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 (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

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

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 (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

Table3

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

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

Nine-species benchmark Amino acid precision
Bacillus subtilis Candidatus Thiodiazotropha endoloripes Apis mellifera Homo sapiens Methanosarcina mazei Mus musculus Vigna mungo Solanum lycopersicum Mean
Casanovo · retrained 0.743 0.754 0.761 0.769 0.754 0.753 0.753 0.717 0.751
LIPNovo 0.806 0.805 0.807 0.805 0.807 0.808 0.794 0.800 0.804
Nine-species benchmark Amino acid recall
Bacillus subtilis Candidatus Thiodiazotropha endoloripes Apis mellifera Homo sapiens Methanosarcina mazei Mus musculus Vigna mungo Solanum lycopersicum Mean
Casanovo · retrained 0.745 0.752 0.757 0.770 0.754 0.752 0.755 0.716 0.750
LIPNovo 0.807 0.805 0.806 0.805 0.807 0.807 0.796 0.798 0.804
Nine-species benchmark Peptide precision
Bacillus subtilis Candidatus Thiodiazotropha endoloripes Apis mellifera Homo sapiens Methanosarcina mazei Mus musculus Vigna mungo Solanum lycopersicum Mean
Casanovo · retrained 0.559 0.544 0.554 0.575 0.558 0.558 0.549 0.506 0.550
LIPNovo 0.607 0.591 0.606 0.596 0.596 0.607 0.577 0.577 0.595
Nine-species benchmark Peptide AUC
Bacillus subtilis Candidatus Thiodiazotropha endoloripes Apis mellifera Homo sapiens Methanosarcina mazei Mus musculus Vigna mungo Solanum lycopersicum Mean
Casanovo · retrained 0.523 0.509 0.519 0.540 0.532 0.521 0.510 0.464 0.515
LIPNovo 0.581 0.563 0.577 0.567 0.565 0.579 0.545 0.544 0.565
Nine-species benchmark Ptm precision
Bacillus subtilis Candidatus Thiodiazotropha endoloripes Apis mellifera Homo sapiens Methanosarcina mazei Mus musculus Vigna mungo Solanum lycopersicum Mean
Casanovo · retrained 0.797 0.742 0.762 0.784 0.779 0.799 0.760 0.710 0.767
LIPNovo 0.855 0.764 0.769 0.772 0.812 0.803 0.764 0.777 0.790
Nine-species benchmark Ptm recall
Bacillus subtilis Candidatus Thiodiazotropha endoloripes Apis mellifera Homo sapiens Methanosarcina mazei Mus musculus Vigna mungo Solanum lycopersicum Mean
Casanovo · retrained 0.689 0.591 0.624 0.612 0.582 0.608 0.593 0.540 0.605
LIPNovo 0.739 0.647 0.675 0.672 0.630 0.667 0.634 0.627 0.661

Table4

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing, page 7: Comparison with GraphNovo (Mao et al., 2023).

The same table is also printed in Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing (Table4, page 7).

GraphNovo dataset and checkpoint Peptide recall Peptide AUC Amino acid recall Amino acid precision
GraphNovo · quoted 0.712 0.876 0.874
LIPNovo 0.729 0.707 0.876 0.882

In the paper: column peptide AUC: the original table bolded LIPNovo (0.707), the only value, since GraphNovo was not run; with nothing to rank it is not marked here.
In the paper: column amino acid recall: the original table only bolded LIPNovo (0.876); GraphNovo (0.876) ties with it and is bolded here too.

Table7

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

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

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

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

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).

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 (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

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).

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 (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

Table4

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

Nine-species benchmark Amino acid precision
Bacillus subtilis Candidatus Thiodiazotropha endoloripes Apis mellifera Homo sapiens Methanosarcina mazei Mus musculus Vigna mungo Solanum lycopersicum Mean
Casanovo retrained · retrained 0.743 0.754 0.761 0.769 0.754 0.753 0.753 0.717 0.751
LIPNovo 0.806 0.805 0.807 0.805 0.807 0.808 0.794 0.800 0.804
LIPNovo+ 0.845 0.843 0.849 0.851 0.841 0.844 0.839 0.839 0.844
Nine-species benchmark Amino acid recall
Bacillus subtilis Candidatus Thiodiazotropha endoloripes Apis mellifera Homo sapiens Methanosarcina mazei Mus musculus Vigna mungo Solanum lycopersicum Mean
Casanovo retrained · retrained 0.745 0.752 0.757 0.770 0.754 0.752 0.755 0.716 0.750
LIPNovo 0.807 0.805 0.806 0.805 0.807 0.807 0.796 0.798 0.804
LIPNovo+ 0.844 0.842 0.848 0.851 0.840 0.843 0.839 0.838 0.843
Nine-species benchmark Peptide precision
Bacillus subtilis Candidatus Thiodiazotropha endoloripes Apis mellifera Homo sapiens Methanosarcina mazei Mus musculus Vigna mungo Solanum lycopersicum Mean
Casanovo retrained · retrained 0.559 0.544 0.554 0.575 0.558 0.558 0.549 0.506 0.550
LIPNovo 0.607 0.591 0.606 0.596 0.596 0.607 0.577 0.577 0.595
LIPNovo+ 0.669 0.657 0.679 0.673 0.657 0.658 0.653 0.652 0.662
Nine-species benchmark Peptide AUC
Bacillus subtilis Candidatus Thiodiazotropha endoloripes Apis mellifera Homo sapiens Methanosarcina mazei Mus musculus Vigna mungo Solanum lycopersicum Mean
Casanovo retrained · retrained 0.523 0.509 0.519 0.540 0.532 0.521 0.510 0.464 0.515
LIPNovo 0.581 0.563 0.577 0.567 0.565 0.579 0.545 0.544 0.565
LIPNovo+ 0.647 0.634 0.657 0.651 0.632 0.633 0.628 0.626 0.639
Nine-species benchmark Ptm precision
Bacillus subtilis Candidatus Thiodiazotropha endoloripes Apis mellifera Homo sapiens Methanosarcina mazei Mus musculus Vigna mungo Solanum lycopersicum Mean
Casanovo retrained · retrained 0.797 0.742 0.762 0.784 0.779 0.799 0.760 0.710 0.767
LIPNovo 0.855 0.764 0.769 0.772 0.812 0.803 0.764 0.777 0.790
LIPNovo+ 0.888 0.776 0.816 0.835 0.827 0.834 0.841 0.824 0.830
Nine-species benchmark Ptm recall
Bacillus subtilis Candidatus Thiodiazotropha endoloripes Apis mellifera Homo sapiens Methanosarcina mazei Mus musculus Vigna mungo Solanum lycopersicum Mean
Casanovo retrained · retrained 0.689 0.591 0.624 0.612 0.582 0.608 0.593 0.540 0.605
LIPNovo 0.739 0.647 0.675 0.672 0.630 0.667 0.634 0.627 0.661
LIPNovo+ 0.782 0.699 0.728 0.696 0.707 0.703 0.703 0.704 0.715

Table5

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

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

Compared against in other papers (2)

14 tables from 2 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.

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)

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.

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)

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.

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)

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.

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)

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.

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) · 8 tables

Table4

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

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

Table3

Discrete Diffusion with Physical Mass Constraints for De Novo Peptide Sequencing, page 8: Leave-one-out cross-validation performance on the Ninespecies dataset compared against the state-of-the-art Method.

Nine-species benchmark Amino acid precision
Bacillus subtilis Candidatus Thiodiazotropha endoloripes Apis mellifera Homo sapiens Methanosarcina mazei Mus musculus Vigna mungo Solanum lycopersicum Mean
LIPNovo · retrained 0.806 0.805 0.807 0.805 0.807 0.808 0.794 0.800 0.804
PhysNovo 0.822 0.819 0.825 0.811 0.825 0.827 0.813 0.815 0.820
Nine-species benchmark Amino acid recall
Bacillus subtilis Candidatus Thiodiazotropha endoloripes Apis mellifera Homo sapiens Methanosarcina mazei Mus musculus Vigna mungo Solanum lycopersicum Mean
LIPNovo · retrained 0.807 0.805 0.806 0.805 0.807 0.807 0.796 0.798 0.804
PhysNovo 0.822 0.820 0.825 0.810 0.826 0.827 0.815 0.815 0.820
Nine-species benchmark Peptide precision
Bacillus subtilis Candidatus Thiodiazotropha endoloripes Apis mellifera Homo sapiens Methanosarcina mazei Mus musculus Vigna mungo Solanum lycopersicum Mean
LIPNovo · retrained 0.607 0.591 0.606 0.596 0.596 0.607 0.577 0.577 0.595
PhysNovo 0.614 0.598 0.610 0.600 0.603 0.611 0.585 0.583 0.601
Nine-species benchmark Peptide AUC
Bacillus subtilis Candidatus Thiodiazotropha endoloripes Apis mellifera Homo sapiens Methanosarcina mazei Mus musculus Vigna mungo Solanum lycopersicum Mean
LIPNovo · retrained 0.581 0.563 0.577 0.567 0.565 0.579 0.545 0.544 0.565
PhysNovo 0.589 0.571 0.584 0.575 0.568 0.592 0.549 0.550 0.572
Nine-species benchmark Ptm precision
Bacillus subtilis Candidatus Thiodiazotropha endoloripes Apis mellifera Homo sapiens Methanosarcina mazei Mus musculus Vigna mungo Solanum lycopersicum Mean
LIPNovo · retrained 0.855 0.764 0.769 0.772 0.812 0.803 0.764 0.777 0.790
PhysNovo 0.878 0.794 0.800 0.797 0.839 0.828 0.793 0.799 0.816
Nine-species benchmark Ptm recall
Bacillus subtilis Candidatus Thiodiazotropha endoloripes Apis mellifera Homo sapiens Methanosarcina mazei Mus musculus Vigna mungo Solanum lycopersicum Mean
LIPNovo · retrained 0.739 0.647 0.675 0.672 0.630 0.667 0.634 0.627 0.661
PhysNovo 0.765 0.685 0.699 0.701 0.664 0.694 0.669 0.658 0.692

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

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Ye Du, Chen Yang, Nanxi Yu, Wanyu Lin, Qian Zhao, Shujun Wang, Qian Niu

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