Computational-experimental approach to drug-target interaction mapping: A case study on kinase inhibitors.

ABSTRACT: Due to relatively high costs and labor required for experimental profiling of the full target space of chemical compounds, various machine learning models have been proposed as cost-effective means to advance this process in terms of predicting the most potent compound-target interactions for subsequent verification. However, most of the model predictions lack direct experimental validation in the laboratory, making their practical benefits for drug discovery or repurposing applications largely unknown. Here, we therefore introduce and carefully test a systematic computational-experimental framework for the prediction and pre-clinical verification of drug-target interactions using a well-established kernel-based regression algorithm as the prediction model. To evaluate its performance, we

SUBMITTER: Cichonska A 

PROVIDER: S-EPMC5560747 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature