Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence
This work outlines strategic priorities for integrating AI with proteomics to enhance research in biology, but it is incremental as it focuses on identifying opportunities rather than presenting new methods or results.
The paper addresses the challenge of advancing biological discovery by leveraging artificial intelligence in proteomics, highlighting key areas where AI can drive innovation from data analysis to new biological insights.
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.