Computational models for the prediction of adverse cardiovascular drug reactions.

ABSTRACT: BACKGROUND:Predicting adverse drug reactions (ADRs) has become very important owing to the huge global health burden and failure of drugs. This indicates a need for prior prediction of probable ADRs in preclinical stages which can improve drug failures and reduce the time and cost of development thus providing efficient and safer therapeutic options for patients. Though several approaches have been put forward for in silico ADR prediction, there is still room for improvement. METHODS:In the present work, we have used machine learning based approach for cardiovascular (CV) ADRs prediction by integrating different features of drugs, biological (drug transporters, targets and enzymes), chemical (substructure fingerprints) and phenotypic (therapeutic indications and other identified ADRs), and

SUBMITTER: Jamal S 

PROVIDER: S-EPMC6530172 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature