SearchNovo

adjacent · Transformer (AR)

SearchNovo: adjacent · Transformer (AR). DB-search + de novo fusion

DB-search + de novo fusion

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

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.

Also used, purpose not recorded

Compared against in other papers (3)

15 tables from 3 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.

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).

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
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

Papers describing it (2)

Authors (8)

Jun Xia, Sizhe Liu, Jingbo Zhou, Shaorong Chen, Hongxin Xiang, Zicheng Liu, Yue Liu, Stan Z. Li

Seen in the charts

Back to the full map

Back to top