{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Li L"],"funding":["Swiss National Science Foundation"],"pagination":["7703"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC6531441"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["9(1)"],"pubmed_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"],"journal":["Scientific reports"],"pubmed_title":["Predicting protein-ligand interactions based on bow-pharmacological space and Bayesian additive regression trees."],"pmcid":["PMC6531441"],"funding_grant_id":["168827"],"pubmed_authors":["Reker D","Liow HH","Chen L","Wei DQ","Koh CC","Wang H","Li L","Fan HM","Brown JB","Lee NK","Dai H"],"additional_accession":[]},"is_claimable":false,"name":"Predicting protein-ligand interactions based on bow-pharmacological space and Bayesian additive regression trees.","description":"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","dates":{"release":"2019-01-01T00:00:00Z","publication":"2019 May","modification":"2026-05-06T23:04:02.236Z","creation":"2019-07-01T13:54:31Z"},"accession":"S-EPMC6531441","cross_references":{"pubmed":["31118426"],"doi":["10.1038/s41598-019-43125-6"]}}