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The future of therapeutics depends on understanding the interaction between the chemical structure of the drug and the target protein that contributes to the etiology of the disease in order to improve drug discovery. Predicting the target of unknown drugs being investigated from already identified drug data is very important not only for understanding different processes of drug and molecular interactions but also for the development of new drugs. Using machine learning and published drug information we design an easy-to-use tool that predicts biological target proteins for medical drugs. TarDict is based on a chemical-simplified line-entry molecular input system called SMILES. It receives SMILES entries and returns a list of possible similar drugs as well as possible drug-targets. TarDict uses 20442 drug entries that have well-known biological targets to construct a prognostic computational model capable of predicting novel drug targets with an accuracy of 95%. We developed a machine learning approach to recommend target proteins to approved drug targets. We have shown that the proposed method is highly predictive on a testing dataset consisting of 4088 targets and 102 manually entered drugs. The proposed computational model is an efficient and cost-effective tool for drug target discovery and prioritization. Such novel tool could be used to enhance drug design, predict potential target and identify combination therapy crossroads.

TarDict: A RandomForestClassifier based software predicts drug-target interaction using SMILES