Turning high-throughput structural biology into predictive inhibitor design.

ABSTRACT: A common challenge in drug design pertains to finding chemical modifications to a ligand that increases its affinity to the target protein. An underutilized advance is the increase in structural biology throughput, which has progressed from an artisanal endeavor to a monthly throughput of hundreds of different ligands against a protein in modern synchrotrons. However, the missing piece is a framework that turns high-throughput crystallography data into predictive models for ligand design. Here, we designed a simple machine learning approach that predicts protein-ligand affinity from experimental structures of diverse ligands against a single protein paired with biochemical measurements. Our key insight is using physics-based energy descriptors to represent protein-ligand complexes and a le

SUBMITTER: Saar KL 

PROVIDER: S-EPMC10089178 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature