Target-driven machine learning-enabled virtual screening (TAME-VS) platform for early-stage hit identification.

ABSTRACT: High-throughput screening (HTS) methods enable the empirical evaluation of a large scale of compounds and can be augmented by virtual screening (VS) techniques to save time and money by using potential active compounds for experimental testing. Structure-based and ligand-based virtual screening approaches have been extensively studied and applied in drug discovery practice with proven outcomes in advancing candidate molecules. However, the experimental data required for VS are expensive, and hit identification in an effective and efficient manner is particularly challenging during early-stage drug discovery for novel protein targets. Herein, we present our TArget-driven Machine learning-Enabled VS (TAME-VS) platform, which leverages existing chemical databases of bioactive molecules to mod

SUBMITTER: Bian Y 

PROVIDER: S-EPMC10040869 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature