A systematic prediction of multiple drug-target interactions from chemical, genomic, and pharmacological data.

ABSTRACT: In silico prediction of drug-target interactions from heterogeneous biological data can advance our system-level search for drug molecules and therapeutic targets, which efforts have not yet reached full fruition. In this work, we report a systematic approach that efficiently integrates the chemical, genomic, and pharmacological information for drug targeting and discovery on a large scale, based on two powerful methods of Random Forest (RF) and Support Vector Machine (SVM). The performance of the derived models was evaluated and verified with internally five-fold cross-validation and four external independent validations. The optimal models show impressive performance of prediction for drug-target interactions, with a concordance of 82.83%, a sensitivity of 81.33%, and a specificity of 93

SUBMITTER: Yu H 

PROVIDER: S-EPMC3364341 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature