Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis; improving peptide and protein identification and quantification; characterizing protein-protein interactions and protein complexes; advancing spatial and perturbation proteomics; integrating multi-omics data; and ultimately enabling AI-empowered virtual cells.
View on arXiv@article{sun2025_2502.15867,
title={ Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence },
author={ Yingying Sun and Jun A and Zhiwei Liu and Rui Sun and Liujia Qian and Samuel H. Payne and Wout Bittremieux and Markus Ralser and Chen Li and Yi Chen and Zhen Dong and Yasset Perez-Riverol and Asif Khan and Chris Sander and Ruedi Aebersold and Juan Antonio Vizcaíno and Jonathan R Krieger and Jianhua Yao and Han Wen and Linfeng Zhang and Yunping Zhu and Yue Xuan and Benjamin Boyang Sun and Liang Qiao and Henning Hermjakob and Haixu Tang and Huanhuan Gao and Yamin Deng and Qing Zhong and Cheng Chang and Nuno Bandeira and Ming Li and Weinan E and Siqi Sun and Yuedong Yang and Gilbert S. Omenn and Yue Zhang and Ping Xu and Yan Fu and Xiaowen Liu and Christopher M. Overall and Yu Wang and Eric W. Deutsch and Luonan Chen and Jürgen Cox and Vadim Demichev and Fuchu He and Jiaxing Huang and Huilin Jin and Chao Liu and Nan Li and Zhongzhi Luan and Jiangning Song and Kaicheng Yu and Wanggen Wan and Tai Wang and Kang Zhang and Le Zhang and Peter A. Bell and Matthias Mann and Bing Zhang and Tiannan Guo },
journal={arXiv preprint arXiv:2502.15867},
year={ 2025 }
}