Predicting Affinity Through Homology (PATH): Interpretable binding affinity prediction with persistent homology.

ABSTRACT: Accurate binding affinity prediction (BAP) is crucial to structure-based drug design. We present PATH+, a novel, generalizable machine learning algorithm for BAP that exploits recent advances in computational topology. Compared to current binding affinity prediction algorithms, PATH+ shows similar or better accuracy and is more generalizable across orthogonal datasets. PATH+ is not only one of the most accurate algorithms for BAP, it is also the first algorithm that is inherently interpretable. Interpretability is a key factor of trust for an algorithm and alongside generalizability, which allows PATH+ to be trusted in critical applications, such as inhibitor design. We visualized the features captured by PATH+ for two clinically relevant protein-ligand complexes and find that PATH+ captur

SUBMITTER: Long Y 

PROVIDER: S-EPMC12226026 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature