Random-forest model for drug-target interaction prediction via Kullbeck-Leibler divergence.

ABSTRACT: Virtual screening has significantly improved the success rate of early stage drug discovery. Recent virtual screening methods have improved owing to advances in machine learning and chemical information. Among these advances, the creative extraction of drug features is important for predicting drug-target interaction (DTI), which is a large-scale virtual screening of known drugs. Herein, we report Kullbeck-Leibler divergence (KLD) as a DTI feature and the feature-driven classification model applicable to DTI prediction. For the purpose, E3FP three-dimensional (3D) molecular fingerprints of drugs as a molecular representation allow the computation of 3D similarities between ligands within each target (Q-Q matrix) to identify the uniqueness of pharmacological targets and those between a quer

SUBMITTER: Ahn S 

PROVIDER: S-EPMC9531514 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature