RefineNovo

algorithm · Transformer (NAR)

RefineNovo: algorithm · Transformer (NAR). Curriculum learning

Curriculum learning

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

Code

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Checkpoints

Version Trained on Host Licence Size Checked Backup
RefineNovo-30M — Google Drive MIT 388 MB verified 2026-10-02 copy

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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 (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.

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

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.

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

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 (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).

Papers describing it (2)

Authors (7)

Xiang Zhang (Shanghai AI Lab), Jiaqi Wei, Zijie Qiu, Sheng Xu, Nanqing Dong, Zhiqiang Gao, Siqi Sun

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