Future Directions in Protein Affinity Selection-Mass Spectrometry for High-Throughput Protein-Ligand Recognition

The rapid proliferation of synthetic chemicals has far outpaced the ability of traditional toxicological methods, underscoring the urgent need for efficient strategies to identify bioactive contaminants. While previous approaches have contributed significantly to the discovery of bioactive chemicals, they remain limited by low efficiency and substantial operational complexity. To meet this challenge, protein affinity selection-mass spectrometry (AS-MS) uses proteins as molecular "baits" to selectively capture bioactive chemicals from environmental samples. Its integration with high-resolution mass spectrometry (HRMS) facilitates the high-throughput identification of bioactive chemicals. In this review, we summarize recent advances in AS-MS and highlight its unique strengths in uncovering novel protein targets, elucidating unexplained toxicological mechanisms, and identifying bioactive transformation products and endogenous ligands. We critically examine the key methodological bottlenecks that currently limit its broader application and propose targeted strategies to mitigate these challenges. Particular emphasis is placed on the role of machine learning (ML) in overcoming the hurdles of structural elucidation and direct transcriptional activity prediction. AS-MS integrated with ML-driven analytics holds substantial promise as a cornerstone platform for next-generation environmental bioactive chemical screening and regulatory prioritization.