Predicting protein-ligand interactions based on bow-pharmacological space and Bayesian additive regression trees.
Published
Journal Article
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 space, and the interaction space. Thereby, the model extends the scope of classical target prediction or chemogenomic modelling that relies on one or two of these subspaces. Our model demonstrated excellent prediction power, reaching accuracies of up to 94.5-98.4% when evaluated on four human target datasets constituting enzymes, nuclear receptors, ion channels, and G-protein-coupled receptors . BART provided a reliable probabilistic description of the likelihood of interaction between proteins and ligands, which can be used in the prioritization of assays to be performed in both discovery and vigilance phases of small molecule development.
Full Text
Duke Authors
Cited Authors
- Li, L; Koh, CC; Reker, D; Brown, JB; Wang, H; Lee, NK; Liow, H-H; Dai, H; Fan, H-M; Chen, L; Wei, D-Q
Published Date
- May 22, 2019
Published In
Volume / Issue
- 9 / 1
Start / End Page
- 7703 -
PubMed ID
- 31118426
Pubmed Central ID
- 31118426
Electronic International Standard Serial Number (EISSN)
- 2045-2322
International Standard Serial Number (ISSN)
- 2045-2322
Digital Object Identifier (DOI)
- 10.1038/s41598-019-43125-6
Language
- eng