LIPNovo+

algorithm · Latent imputation

LIPNovo+: algorithm · Latent imputation. Self-reflective extension of LIPNovo that finds the fragmentation sites where latent imputation is unreliable and refocuses training on them through a reflection-guided…

Self-reflective extension of LIPNovo that finds the fragmentation sites where latent imputation is unreliable and refocuses training on them through a reflection-guided curriculum, so the model is not limited to the missingness patterns seen during training.

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.

Reported comparisons (8)

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.

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 (1)

6 tables from 1 paper 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

Paper describing it

Authors (5)

Ye Du, Qian Niu, Chen Yang, Nanxi Yu, Shujun Wang

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