DiffuNovo

algorithm · Diffusion

DiffuNovo: algorithm · Diffusion. Regressor-guided diffusion

Regressor-guided diffusion

Kind algorithm
Deep learning yes
Acquisition DDA
Family Diffusion

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

Also used, purpose not recorded

Reported comparisons (6)

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.

Table 3 (HC-PT)

Regressor-guided Diffusion Model for De Novo Peptide Sequencing with Explicit Mass Control, page 7: Empirical comparison of PTM identification. We evaluate the ability of DiffuNovo and other models to identify Post-Translational Modifications (PTMs) on the benchmark datasets. The best results and the second best are highlighted with bold and underline, respectively.

ProteomeTools
HC-PT (NovoBench) 		Ptm precision
DeepNovo 0.626
PointNovo 0.676
AdaNovo 0.552
Casanovo 0.501
π-HelixNovo 0.568
DiffuNovo 0.705

Table 3 (Nine-species)

Regressor-guided Diffusion Model for De Novo Peptide Sequencing with Explicit Mass Control, page 7: Empirical comparison of PTM identification. We evaluate the ability of DiffuNovo and other models to identify Post-Translational Modifications (PTMs) on the benchmark datasets. The best results and the second best are highlighted with bold and underline, respectively.

Nine-species benchmark Ptm precision
DeepNovo 0.576
PointNovo 0.629
AdaNovo 0.652
Casanovo 0.706
π-HelixNovo 0.680
DiffuNovo 0.822

Table 3 (Seven-species)

Regressor-guided Diffusion Model for De Novo Peptide Sequencing with Explicit Mass Control, page 7: Empirical comparison of PTM identification. We evaluate the ability of DiffuNovo and other models to identify Post-Translational Modifications (PTMs) on the benchmark datasets. The best results and the second best are highlighted with bold and underline, respectively.

Seven-species benchmark
NovoBench split 		Ptm precision
DeepNovo 0.391
PointNovo 0.117
AdaNovo 0.448
Casanovo 0.360
π-HelixNovo 0.473
DiffuNovo 0.515

Table2 (HC-PT)

Regressor-guided Diffusion Model for De Novo Peptide Sequencing with Explicit Mass Control, page 7: The comparison of de novo peptide sequencing performance between our proposed model, DiffuNovo, and other stateof-the-art methods on the three benchmark datasets. We report precision and AUC at the peptide level, and precision and recall at the amino acid level. 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
AdaNovo 0.212 0.178 0.442 0.451
π-HelixNovo 0.356 0.318 0.588 0.582
DiffuNovo Logits 0.485 0.324 0.648 0.648
DiffuNovo MBR 0.458 0.434 0.654 0.654

In the paper: column amino acid precision: the original table underlined PointNovo (0.623).
In the paper: column peptide precision: the original table underlined PointNovo (0.419).
In the paper: column amino acid recall: the original table underlined PointNovo (0.622).

Table2 (Nine-species)

Regressor-guided Diffusion Model for De Novo Peptide Sequencing with Explicit Mass Control, page 7: The comparison of de novo peptide sequencing performance between our proposed model, DiffuNovo, and other stateof-the-art methods on the three benchmark datasets. We report precision and AUC at the peptide level, and precision and recall at the amino acid level. 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
AdaNovo 0.505 0.469 0.698 0.709
π-HelixNovo 0.517 0.453 0.765 0.758
DiffuNovo Logits 0.572 0.413 0.785 0.783
DiffuNovo MBR 0.565 0.536 0.791 0.789

In the paper: column amino acid precision: the original table underlined π-HelixNovo (0.765).
In the paper: column peptide precision: the original table underlined π-HelixNovo (0.517).
In the paper: column amino acid recall: the original table underlined π-HelixNovo (0.758).

Table2 (Seven-species)

Regressor-guided Diffusion Model for De Novo Peptide Sequencing with Explicit Mass Control, page 7: The comparison of de novo peptide sequencing performance between our proposed model, DiffuNovo, and other stateof-the-art methods on the three benchmark datasets. We report precision and AUC at the peptide level, and precision and recall at the amino acid level. 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
AdaNovo 0.174 0.135 0.379 0.385
π-HelixNovo 0.234 0.173 0.481 0.472
DiffuNovo Logits 0.233 0.104 0.430 0.428
DiffuNovo MBR 0.193 0.162 0.437 0.435

Compared against in other papers (1)

3 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.

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).

Papers describing it (2)

Authors (3)

Shaorong Chen, Jingbo Zhou, Jun Xia

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