ReNovo

algorithm · Transformer (AR)

ReNovo: algorithm · Transformer (AR). Retrieval-based sequencing

Retrieval-based sequencing

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

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 (3)

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)

ReNovo: Retrieval-Based De Novo Mass Spectrometry Peptide Sequencing, page 7: Empirical comparison of de novo peptide sequencing models. The best and the second best are highlighted with bold and underline, respectively.

ProteomeTools
HC-PT (NovoBench) 		Peptide precision Peptide AUC Amino acid precision Amino acid recall
DeepNovo 0.313 0.255 0.531 0.534
PointNovo 0.419 0.373 0.623 0.622
Casanovo 0.211 0.177 0.442 0.453
π-HelixNovo 0.356 0.318 0.588 0.582
AdaNovo 0.212 0.178 0.442 0.451
ReNovo 0.467 0.436 0.651 0.648

Table1 (Nine-species)

ReNovo: Retrieval-Based De Novo Mass Spectrometry Peptide Sequencing, page 7: Empirical comparison of de novo peptide sequencing models. The best and the second best are highlighted with bold and underline, respectively.

Nine-species benchmark Peptide precision Peptide AUC Amino acid precision Amino acid recall
DeepNovo 0.428 0.376 0.696 0.638
PointNovo 0.480 0.436 0.740 0.671
Casanovo 0.481 0.439 0.697 0.696
π-HelixNovo 0.517 0.453 0.765 0.758
AdaNovo 0.505 0.469 0.698 0.709
ReNovo 0.568 0.528 0.770 0.769

Table1 (Seven-species)

ReNovo: Retrieval-Based De Novo Mass Spectrometry Peptide Sequencing, page 7: Empirical comparison of de novo peptide sequencing models. The best and the second best are highlighted with bold and underline, respectively.

Seven-species benchmark
NovoBench split 		Peptide precision Peptide AUC Amino acid precision Amino acid recall
DeepNovo 0.204 0.136 0.492 0.433
PointNovo 0.022 0.007 0.196 0.169
Casanovo 0.119 0.084 0.322 0.327
π-HelixNovo 0.234 0.173 0.481 0.472
AdaNovo 0.174 0.135 0.379 0.385
ReNovo 0.278 0.228 0.512 0.514

Compared against in other papers (2)

6 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.

Discrete Diffusion with Physical Mass Constraints for De Novo Peptide Sequencing (2026) · 3 tables

Table1 (Seven-species)

Discrete Diffusion with Physical Mass Constraints for De Novo Peptide Sequencing, page 7: Comparison with state-of-the-art methods on Nine-species, Seven-species, and HC-PT datasets in amino acid-level and peptidelevel performance. The best results are marked in bold, and the second best are underlined.

Seven-species benchmark
NovoBench split 		Amino acid precision Amino acid recall Peptide precision Peptide AUC
DeepNovo · retrained 0.492 0.433 0.204 0.136
PointNovo · retrained 0.196 0.169 0.022 0.007
InstaNovo · retrained 0.192 0.176 0.031 0.009
Casanovo · retrained 0.322 0.327 0.119 0.084
AdaNovo · retrained 0.379 0.385 0.174 0.135
π-HelixNovo · retrained 0.481 0.472 0.234 0.173
LIPNovo · retrained 0.557 0.560 0.327 0.281
ReNovo 0.512 0.514 0.278 0.228
PhysNovo 0.568 0.569 0.346 0.290

Table1 (Nine-species)

Discrete Diffusion with Physical Mass Constraints for De Novo Peptide Sequencing, page 7: Comparison with state-of-the-art methods on Nine-species, Seven-species, and HC-PT datasets in amino acid-level and peptidelevel performance. The best results are marked in bold, and the second best are underlined.

Nine-species benchmark
revised (balanced) 		Amino acid precision Amino acid recall Peptide precision Peptide AUC
PEAKS 0.748 0.428
DeepNovo · retrained 0.696 0.638 0.428 0.376
PointNovo · retrained 0.740 0.671 0.480 0.436
InstaNovo · retrained 0.420 0.395 0.164 0.123
Casanovo · retrained 0.697 0.696 0.481 0.439
AdaNovo · retrained 0.698 0.709 0.505 0.469
π-HelixNovo · retrained 0.765 0.758 0.517 0.453
LIPNovo · retrained 0.797 0.797 0.582 0.547
π-PrimeNovo 0.790
ReNovo 0.770 0.769 0.568 0.528
PhysNovo 0.815 0.814 0.588 0.553

Table1 (HC-PT)

Discrete Diffusion with Physical Mass Constraints for De Novo Peptide Sequencing, page 7: Comparison with state-of-the-art methods on Nine-species, Seven-species, and HC-PT datasets in amino acid-level and peptidelevel performance. The best results are marked in bold, and the second best are underlined.

ProteomeTools
HC-PT (NovoBench) 		Amino acid precision Amino acid recall Peptide precision Peptide AUC
DeepNovo · retrained 0.531 0.534 0.313 0.255
PointNovo · retrained 0.623 0.622 0.419 0.373
InstaNovo · retrained 0.289 0.285 0.057 0.034
Casanovo · retrained 0.442 0.453 0.211 0.177
AdaNovo · retrained 0.442 0.451 0.212 0.178
π-HelixNovo · retrained 0.588 0.582 0.356 0.318
LIPNovo · retrained 0.637 0.643 0.458 0.427
ReNovo 0.651 0.648 0.467 0.436
PhysNovo 0.649 0.652 0.473 0.444
LIPNovo+: Self-Reflective Latent Imputation for Robust De Novo Peptide Sequencing (2026) · 3 tables

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).

Paper describing it

Authors (9)

Shaorong Chen, Jun Xia, Jingbo Zhou, Lecheng Zhang, Zhangyang Gao, Bozhen Hu, Cheng Tan, Wenjie Du, Stan Z. Li

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