Evolutionary chemical binding similarity approach integrated with 3D-QSAR method for effective virtual screening.

ABSTRACT:

Background

Despite continued efforts using chemical similarity methods in virtual screening, currently developed approaches suffer from time-consuming multistep procedures and low success rates. We recently developed a machine learning-based chemical binding similarity model considering common structural features from molecules binding to the same, or evolutionarily related targets. The chemical binding similarity measures the resemblance of chemical compounds in terms of binding site similarity to better describe functional similarities that arise from target binding. In this study, we have shown how the chemical binding similarity could be used in virtual screening together with the conventional structure-based methods.

Results

The chemical binding similarity, receptor-bas

SUBMITTER: Durai P 

PROVIDER: S-EPMC7362480 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature