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

arXiv: https://arxiv.org/abs/2312.11584 · HTML: https://mlsanity.com/paper/2312.11584/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 29 references that are in the map.
- First posted on arXiv 18 Dec 2023.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Beam Search Strategies for Neural Machine Translation](https://mlsanity.com/paper/1702.01806/) (2017)
- [Sensitivity and Generalization in Neural Networks: an Empirical Study](https://mlsanity.com/paper/1802.08760/) (2018)
- [BRIO: Bringing Order to Abstractive Summarization](https://mlsanity.com/paper/2203.16804/) (2022)

## Built on by (most cited first, 9 of 9)

- [Counting Ability of Large Language Models and Impact of Tokenization](https://mlsanity.com/paper/2410.19730/) (2024)
- [Multi2: Multi-Agent Test-Time Scalable Framework for Multi-Document Processing](https://mlsanity.com/paper/2502.20592/) (2025)
- [PosterGen: Aesthetic-Aware Multi-Modal Paper-to-Poster Generation via Multi-Agent LLMs](https://mlsanity.com/paper/2508.17188/) (2025)
- [Universal Biological Sequence Reranking for Improved De Novo Peptide Sequencing](https://mlsanity.com/paper/2505.17552/) (2025)
- [Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing](https://mlsanity.com/paper/2506.13485/) (2025)
- [Bidirectional Representations Augmented Autoregressive Biological Sequence Generation](https://mlsanity.com/paper/2510.08169/) (2025)
- [Reflection Pretraining Enables Token-Level Self-Correction in Biological Sequence Models](https://mlsanity.com/paper/2512.20954/) (2025)
- [MemNovo: Look Back at the Spectrum for Balanced De Novo Peptide Sequencing from Mass Spectrometry](https://mlsanity.com/paper/2606.11868/) (2026)
- [GyroNovo: Error-Guided Fragment Imputation with Mass-Aware Attention for De Novo Peptide Sequencing](https://mlsanity.com/paper/2609.30542/) (2026)

## Abstract (arXiv)

De novo peptide sequencing from mass spectrometry (MS) data is a critical task in proteomics research. Traditional de novo algorithms have encountered a bottleneck in accuracy due to the inherent complexity of proteomics data. While deep learning-based methods have shown progress, they reduce the problem to a translation task, potentially overlooking critical nuances between spectra and peptides. In our research, we present ContraNovo, a pioneering algorithm that leverages contrastive learning to extract the relationship between spectra and peptides and incorporates the mass information into peptide decoding, aiming to address these intricacies more efficiently. Through rigorous evaluations on two benchmark datasets, ContraNovo consistently outshines contemporary state-of-the-art solutions, underscoring its promising potential in enhancing de novo peptide sequencing. The source code is available at https://github.com/BEAM-Labs/ContraNovo.