PNNL

Abstract

Modern artificial intelligence (AI) has started a transformative era for mass spectrometry (MS) and multiomics. As instruments increase in sensitivity, resolving power, and dimensionality, the computational requirements to interpret their multidimensional data continue to rise. This Young Scientist Perspective reflects on my research trajectory, from early work in data-independent acquisition for proteomics and metabolomics, interference removal, and ion mobility preprocessing, to the development of free or open-source software and AI-powered tools designed to accelerate biological discovery. I discuss computational pillars in the incorporation of AI in MS workflows and emerging foundation models with intrinsic capabilities for MS. I also outline new directions for error rate estimation in molecular annotation, user interfaces for deterministic and agentic AI analyses, and how these advances can revolutionize microbial systems biology, particularly for biotechnology and environmental applications. Finally, I highlight a forward-looking vision for the next decade as MS and AI co-evolve toward autonomous, interoperable, and democratized tools for the scientific community.