New machine learning and physics-based scoring functions for drug discovery.

ABSTRACT: Scoring functions are essential for modern in silico drug discovery. However, the accurate prediction of binding affinity by scoring functions remains a challenging task. The performance of scoring functions is very heterogeneous across different target classes. Scoring functions based on precise physics-based descriptors better representing protein-ligand recognition process are strongly needed. We developed a set of new empirical scoring functions, named DockTScore, by explicitly accounting for physics-based terms combined with machine learning. Target-specific scoring functions were developed for two important drug targets, proteases and protein-protein interactions, representing an original class of molecules for drug discovery. Multiple linear regression (MLR), support vector machine

SUBMITTER: Guedes IA 

PROVIDER: S-EPMC7862620 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature