AI-driven allosteric drug discovery integrates computational modeling, structural biology, and machine learning to identify modulators that fine-tune protein activity. This approach aims to improve selectivity and safety profiles for therapies targeting cardiovascular and cancer pathways. By combining high-throughput screening with predictive algorithms and biophysical validation, researchers prioritize candidates that alter regulatory sites rather than active centers. The strategy supports precision medicine goals by enabling tailored modulation of disease-relevant signaling networks.
