# AdaNovo: Adaptive De Novo Peptide Sequencing with Conditional Mutual Information

arXiv: https://arxiv.org/abs/2403.07013 · HTML: https://mlsanity.com/paper/2403.07013/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 25 references that are in the map.
- First posted on arXiv 9 Mar 2024.

## Builds on (most cited first, 4 of 4)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning to Reweight Examples for Robust Deep Learning](https://mlsanity.com/paper/1803.09050/) (2018)
- [Deep Long-Tailed Learning: A Survey](https://mlsanity.com/paper/2110.04596/) (2021)
- [Rethinking Positional Encoding in Language Pre-training](https://mlsanity.com/paper/2006.15595/) (2020)

## Built on by (most cited first, 6 of 6)

- [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)
- [NovoBench: Benchmarking Deep Learning-based De Novo Peptide Sequencing Methods in Proteomics](https://mlsanity.com/paper/2406.11906/) (2024)
- [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)

## Abstract (arXiv)

Tandem mass spectrometry has played a pivotal role in advancing proteomics, enabling the analysis of protein composition in biological samples. Despite the development of various deep learning methods for identifying amino acid sequences (peptides) responsible for observed spectra, challenges persist in de novo peptide sequencing. Firstly, prior methods struggle to identify amino acids with post-translational modifications (PTMs) due to their lower frequency in training data compared to canonical amino acids, further resulting in decreased peptide-level identification precision. Secondly, diverse types of noise and missing peaks in mass spectra reduce the reliability of training data (peptide-spectrum matches, PSMs). To address these challenges, we propose AdaNovo, a novel framework that calculates conditional mutual information (CMI) between the spectrum and each amino acid/peptide, using CMI for adaptive model training. Extensive experiments demonstrate AdaNovo's state-of-the-art performance on a 9-species benchmark, where the peptides in the training set are almost completely disjoint from the peptides of the test sets. Moreover, AdaNovo excels in identifying amino acids with PTMs and exhibits robustness against data noise. The supplementary materials contain the official code.