Identifying Novel Drug Targets by iDTPnd: A Case Study of Kinase Inhibitors.

ABSTRACT: Current FDA-approved kinase inhibitors cause diverse adverse effects, some of which are due to the mechanism-independent effects of these drugs. Identifying these mechanism-independent interactions could improve drug safety and support drug repurposing. Here, we develop iDTPnd (integrated Drug Target Predictor with negative dataset), a computational approach for large-scale discovery of novel targets for known drugs. For a given drug, we construct a positive structural signature as well as a negative structural signature that captures the weakly conserved structural features of drug-binding sites. To facilitate assessment of unintended targets, iDTPnd also provides a docking-based interaction score and its statistical significance. We confirm the interactions of sorafenib, imatinib, dasati

SUBMITTER: Naveed H 

PROVIDER: S-EPMC9403029 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature