Data-driven score tuning for ChooseLD: A structure-based drug design algorithm with empirical scoring and evaluation of ligand-protein docking predictability.

ABSTRACT: Computerized molecular docking methodologies are pivotal in in-silico screening, a crucial facet of modern drug design. ChooseLD, a docking simulation software, combines structure- and ligand-based drug design methods with empirical scoring. Despite advancements in computerized molecular docking methodologies, there remains a gap in optimizing the predictive capabilities of docking simulation software. Accordingly, using the docking scores output by ChooseLD, we evaluated its performance in predicting the bioactivity of G-protein coupled receptor (GPCR) and kinase bioactivity, specifically focusing on Ki and IC₅₀ values. We evaluated the accuracy of our algorithm through a comparative analysis using force-field-based predictions from AutoDock Vina. Our findings suggested

SUBMITTER: Masuda A 

PROVIDER: S-EPMC11718169 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature