In silico prediction of drugtarget interactions from heterogeneous biological data can advance our systemlevel 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 erformance of the derived models was evaluated and verified with internally fivefold crossvalidation and four external independent validations.The optimal models show impressive performance of prediction for drugtarget interactions, with a concordance of 82.83％, a sensitivity of 81.33％, and a specificity of 93.62％, respectively.The consistence of the performances of the RF and SVM models demonstrates the reliability and robustness of the obtained models.In addition, the validated models were employed to systematically predict known/unknown drugs and targets involving the enzymes, ion channels, GPCRs, and nuclear receptors, which can be further mapped to functional ontologies such as targetdisease associations and targettarget interaction networks.This approach is expected to help fill the existing gap between chemical genomics and network pharmacology and thus accelerate the drug discovery processes.