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

Hammad Naveed, Corinna Reglin, Thomas Schubert, Xin Gao, Stefan T. Arold, Michael L Maitland

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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. We have developed 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 and 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 were able to confirm the interaction of sorafenib, imatinib, dasatinib, sunitinib, and pazopanib with their known targets at a sensitivity and specificity of 52% and 55%, respectively. We have validated 10 predicted novel targets by using in vitro experiments. Our results suggest that proteins other than kinases, such as nuclear receptors, cytochrome P450, or MHC Class I molecules can also be physiologically relevant targets of kinase inhibitors. Our method is general and broadly applicable for the identification of protein-small molecule interactions, when sufficient drug-target 3D data are available. The code for constructing the structural signature is available at
Original languageEnglish (US)
JournalGenomics, Proteomics & Bioinformatics
StatePublished - Apr 1 2021

ASJC Scopus subject areas

  • Biochemistry
  • Genetics
  • Computational Mathematics
  • Molecular Biology


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