Seven-species benchmark

benchmark · DDA · 2 versions · 17 papers

DeepNovo’s LOW-resolution evaluation set, assembled from seven earlier publications and scored leave-one-out: train on six species, test on the seventh. Distinct data from the high-resolution nine-species benchmark, not a version of it, though both…

Kind	benchmark
Acquisition	DDA

DeepNovo’s LOW-resolution evaluation set, assembled from seven earlier publications and scored leave-one-out: train on six species, test on the seventh. Distinct data from the high-resolution nine-species benchmark, not a version of it, though both come from Tran et al. 2017 and papers name them side by side.

Versions

original (DeepNovo, 2017)

The low-resolution seven-species set as DeepNovo defined and used it, scored leave-one-out over the seven species. No single accession is published for it: the seven constituent datasets come from seven separate prior publications.

introduced by De novo peptide sequencing by deep learning

No public address: this version is named in the literature but cannot be downloaded.

NovoBench split

NovoBench’s fixed split: yeast held out as the test species and the other six used for training, 3 PTMs, mean peptide length 15.79. This is the split NovoBench retrains every architecture on, so a number reported “on seven-species” by a NovoBench-derived paper means this and not DeepNovo’s own leave-one-out.

train 317,009 · validation 17,740 · test 17,094 · introduced by NovoBench: Benchmarking Deep Learning-based De Novo Peptide Sequencing Methods in Proteomics

No public address: this version is named in the literature but cannot be downloaded.

Deposited by (3)

More than one paper here means one piece of work published twice, a preprint and its version of record; a deposit itself happens once.

• NovoBench: Benchmarking Deep Learning-based De Novo Sequencing Methods in Proteomics (2024) ML conference NovoBench split
• NovoBench: Benchmarking Deep Learning-based De Novo Peptide Sequencing Methods in Proteomics (2024) preprint NovoBench split
• De novo peptide sequencing by deep learning (2017) peer-reviewed original (DeepNovo, 2017)

Trained on by (9)

• GyroNovo: Error-Guided Fragment Imputation with Mass-Aware Attention for De Novo Peptide Sequencing (2026) preprint NovoBench split
• Discrete Diffusion with Physical Mass Constraints for De Novo Peptide Sequencing (2026) ML conference NovoBench split
• LIPNovo+: Self-Reflective Latent Imputation for Robust De Novo Peptide Sequencing (2026) preprint NovoBench split
• Regressor-guided Diffusion Model for De Novo Peptide Sequencing with Explicit Mass Control (2026) preprint NovoBench split
• CausalNovo: Advancing De Novo Peptide Sequencing via a Causality-Informed Framework (2026) preprint NovoBench split, original (DeepNovo, 2017)
• Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing (2025) ML conference NovoBench split
• Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing (2025) preprint NovoBench split
• ReNovo: Retrieval-Based De Novo Mass Spectrometry Peptide Sequencing (2024) ML conference NovoBench split
• De novo peptide sequencing by deep learning (2017) peer-reviewed original (DeepNovo, 2017)

Evaluated on by (11)

• GyroNovo: Error-Guided Fragment Imputation with Mass-Aware Attention for De Novo Peptide Sequencing (2026) preprint NovoBench split
• Discrete Diffusion with Physical Mass Constraints for De Novo Peptide Sequencing (2026) ML conference NovoBench split
• LIPNovo+: Self-Reflective Latent Imputation for Robust De Novo Peptide Sequencing (2026) preprint NovoBench split
• Regressor-guided Diffusion Model for De Novo Peptide Sequencing with Explicit Mass Control (2026) preprint NovoBench split
• CausalNovo: Advancing De Novo Peptide Sequencing via a Causality-Informed Framework (2026) preprint NovoBench split, original (DeepNovo, 2017)
• Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing (2025) ML conference NovoBench split
• Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing (2025) ML conference NovoBench split
• Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing (2025) preprint NovoBench split
• Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing (2025) preprint NovoBench split
• ReNovo: Retrieval-Based De Novo Mass Spectrometry Peptide Sequencing (2024) ML conference NovoBench split
• De novo peptide sequencing by deep learning (2017) peer-reviewed original (DeepNovo, 2017)

Used by (4)

Papers that ran on it where the catalog has not recorded whether for training or for evaluation.

• Regressor-guided Diffusion Model for De Novo Peptide Sequencing with Explicit Mass Control (2026) peer-reviewed NovoBench split
• Bridging the Gap between Database Search and De Novo Peptide Sequencing with SearchNovo (2025) ML conference NovoBench split
• Bridging the Gap between Database Search and De Novo Peptide Sequencing with SearchNovo (2024) preprint NovoBench split
• Enhancing Peptide Mass Spectra Encoder through Pretraining using Contrastive Predictive Coding (2024) thesis NovoBench split

Methods on these papers (12)

• CPC spectrum encoder pretraining adjacent
• CausalNovo algorithm
• DeepNovo algorithm
• DiffuNovo algorithm
• GyroNovo algorithm
• LIPNovo algorithm
• LIPNovo+ algorithm
• NovoBench benchmark
• PhysNovo algorithm
• ReNovo algorithm
• RefineNovo algorithm
• SearchNovo adjacent

Taken from the describing links only, so a paper that merely ran a tool on this data does not make that tool a method of it.

Seen in the charts

• The data underneath

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