News & Updates

Stay updated with the latest developments, publications, and platform enhancements

Platform Updates
Latest updates, model additions, and feature enhancements to AbNovoBench

Comprehensive Evaluation of 13 State-of-the-Art Models

Model Evaluation
2024-12-19
v3.0.0

Updated comprehensive evaluation results for 13 peptide sequencing models including InstaNovo, ContraNovo, CasaNovo et al. ,with enhanced metrics and performance analysis.

Publications
Research papers and publications related to AbNovoBench

Improvements to Casanovo, a deep learning de novo peptide sequencer

preprint

Straub, G., et al.

bioRxiv (2025)DOI: 10.1101/2025.07.25.666826

InstaNovo enables diffusion-powered de novo peptide sequencing in large-scale proteomics experiments

Published

Eloff, K., Kalogeropoulos, K., Mabona, A., et al.

Nature Machine Intelligence (2025)DOI: 10.1038/s42256-025-01019-5

π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing

Published

Zhang, X., Ling, T., Jin, Z., et al.

Nature Communications (2025)DOI: 10.1038/s41467-025-00267-x

AdaNovo: Towards Robust De Novo Peptide Sequencing in Proteomics against Data Biases

Published

Xia, J., Chen, S., Zhou, J., et al.

Advances in Neural Information Processing Systems (NeurIPS) (2024)DOI: 10.48550/arXiv.2024.neurips

Sequence-to-sequence translation from mass spectra to peptides with a transformer model

Published

Yilmaz, M., Fondrie, W.E., Bittremieux, W., et al.

Nature Communications (2024)DOI: 10.1038/s41467-024-06427-x

ContraNovo: A Contrastive Learning Approach to Enhance De Novo Peptide Sequencing

Published

Jin, Z., Xu, S., Zhang, X., et al.

AAAI Conference on Artificial Intelligence (2024)DOI: 10.1609/AAAI.V38I1.27765

Introducing π-HelixNovo for practical large-scale de novo peptide sequencing

Published

Yang, T., Ling, T., Sun, B., et al.

Briefings in Bioinformatics (2024)DOI: 10.1093/bib/bbae021

PGPointNovo: an efficient neural network-based tool for parallel de novo peptide sequencing

Published

Xu, X., Yang, C., He, Q., et al.

Bioinformatics Advances (2023)DOI: 10.1093/bioadv/vbad057

Accurate de novo peptide sequencing using fully convolutional neural networks

Published

Liu, K., Ye, Y., Li, S., Tang, H.

Nature Communications (2023)DOI: 10.1038/s41467-023-33725-4

De novo mass spectrometry peptide sequencing with a transformer model

Published

Yilmaz, M., Fondrie, W., Bittremieux, W., Oh, S., Noble, W.S.

International Conference on Machine Learning (ICML) (2022)DOI: 10.48550/arXiv.2202.08859

Computationally instrument-resolution-independent de novo peptide sequencing for high-resolution devices

Published

Qiao, R., Tran, N.H., Xin, L., Chen, X., Li, M., Shan, B., Ghodsi, A.

Nature Machine Intelligence (2021)DOI: 10.1038/s42256-021-00304-3

pNovo 3: precise de novo peptide sequencing using a learning-to-rank framework

Published

Yang, H., Chi, H., Zeng, W.F., Zhou, W.J., He, S.M.

Bioinformatics (2019)DOI: 10.1093/bioinformatics/btz366

Uncovering Thousands of New Peptides with Sequence-Mask-Search Hybrid De Novo Peptide Sequencing Framework

Published

Karunratanakul, K., Tang, H.Y., Speicher, D.W., Chuangsuwanich, E.

Molecular & Cellular Proteomics (2019)DOI: 10.1074/mcp.TIR119.001656

De novo peptide sequencing by deep learning

Published

Tran, N.H., Zhang, X., Xin, L., Li, M.

Proceedings of the National Academy of Sciences (PNAS) (2017)DOI: 10.1073/pnas.1705691114