Predicting protein-ligand interactions based on bow-pharmacological space and Bayesian additive regression trees.

ABSTRACT: Identifying potential protein-ligand interactions is central to the field of drug discovery as it facilitates the identification of potential novel drug leads, contributes to advancement from hits to leads, predicts potential off-target explanations for side effects of approved drugs or candidates, as well as de-orphans phenotypic hits. For the rapid identification of protein-ligand interactions, we here present a novel chemogenomics algorithm for the prediction of protein-ligand interactions using a new machine learning approach and novel class of descriptor. The algorithm applies Bayesian Additive Regression Trees (BART) on a newly proposed proteochemical space, termed the bow-pharmacological space. The space spans three distinctive sub-spaces that cover the protein space, the ligand spa

SUBMITTER: Li L 

PROVIDER: S-EPMC6531441 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature