AI Proteomics Review: From Protein Identification to Virtual Cells

A new review published in Nature Methods surveys the state of AI-driven proteomics, tracing progress from basic protein identification tasks to the emerging goal of building virtual cell models that simulate cellular behavior computationally. The piece covers how large-scale deep learning models have transformed mass spectrometry data analysis, protein structure prediction, and protein-protein interaction modeling. For AI developers working in biotech or adjacent fields, this provides a comprehensive map of where the field stands and which subproblems remain open research targets. The virtual cell framing is particularly significant: it represents a convergence point for multiple AI research threads — structure prediction, generative biology, and systems modeling — that could define the next major application frontier for foundation models. The review is directly relevant to teams building AI tooling for drug discovery, genomics platforms, or biological simulation.