Towards accurate high-throughput ligand affinity prediction by exploiting structural ensembles, docking metrics and ligand similarity.

ABSTRACT:

Motivation

Nowadays, virtual screening (VS) plays a major role in the process of drug development. Nonetheless, an accurate estimation of binding affinities, which is crucial at all stages, is not trivial and may require target-specific fine-tuning. Furthermore, drug design also requires improved predictions for putative secondary targets among which is Estrogen Receptor alpha (ERα).

Results

VS based on combinations of Structure-Based VS (SBVS) and Ligand-Based VS (LBVS) is gaining momentum to improve VS performances. In this study, we propose an integrated approach using ligand docking on multiple structural ensembles to reflect receptor flexibility. Then, we investigate the impact of the two different types of features (structure-based and ligand molecular descriptors) on

SUBMITTER: Schneider M 

PROVIDER: S-EPMC6956784 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature