Mass spectrometry‑based chemoproteomics for modern drug screening: A critical review and the 3M framework (Match-Map-Mine).

Abstract

Modern drug screening leans heavily on mass spectrometry-based chemoproteomics. However, widespread adoption has not cured the field's fundamental fragmentation: component identification and target deconvolution, not to mention safety evaluation, remain disconnected disciplines, each operating within its own literature, expert circles, and technical routines. This separation carries a real cost, as discoveries rarely travel from bench to clinic. We address this gap with the Match-Map-Mine (3 M) framework, which folds these stages into a single workflow that sharpens with every cycle. We then examine how three recent advances-ion mobility-HRMS, data-independent acquisition, and AI-driven structure elucidation-fit into this pipeline to speed up next-generation drug discovery. The 3 M Framework has three working parts. Match starts by selecting bioactive components from complex mixtures. Map then deconvolutes drug-target interactions, drawing on both label-free and affinity-based chemoproteomic strategies. Safety and mechanism fall to Mine, which pulls together multi-omics, reactive metabolite trapping, and systems toxicology. The framework's defining feature is its conditional feedback loops. When safety or target-binding discrepancies exceed a preset threshold, the pipeline triggers a targeted reanalysis and returns to an earlier module. In that sense, 3 M is no longer a one-pass pipeline but an iterative workflow. The loops are not automatic, though: each re-entry requires orthogonal analytical confirmation before the workflow returns to a previous module. We put current MS platforms, acquisition modes, and chemoproteomic approaches side by side, zeroing in on two emerging twists: collision cross‑section (CCS) as an orthogonal identifier and AI‑assisted spectral interpretation. On the bottleneck side, we use a Technology Readiness Level (TRL) heuristic to assess where things stand, define where the 3 M framework hits its limits, and lay out a conditional 5‑year view-one that asks what it would take to move toward fully autonomous, AI‑driven 3 M workflows. Taken together, this review offers a practical guide for implementing chemoproteomics workflows across the full drug-screening pipeline-from fractionating complex natural product mixtures to establishing validated target‑safety profiles.