Computational drug repurposing aims at finding new medical uses for existing drugs. The identification of novel drug-target interactions (DTIs) can be a useful part of such a task. Computational determination of DTIs is a convenient strategy for systematic screening of a large number of drugs in the attempt to identify new DTIs at low cost and with reasonable accuracy. This necessitates development of accurate computational methods that can help focus on the follow-up experimental validation on a smaller number of highly likely targets for a drug. Although many methods have been proposed for computational DTI prediction, they suffer the high false positive prediction rate or they do not predict the effect that drugs exert on targets in DTIs. In this report, first, we present a comprehensive review of the recent progress in the field of DTI prediction from data-centric and algorithm-centric perspectives. The aim is to provide a comprehensive review of computational methods for identifying DTIs, which could help in constructing more reliable methods. Then, we present DDR, an efficient method to predict the existence of DTIs. DDR achieves significantly more accurate results compared to the other state-of-theart methods. As supported by independent evidences, we verified as correct 22 out of the top 25 DDR DTIs predictions. This validation proves the practical utility of DDR, suggesting that DDR can be used as an efficient method to identify 5 correct DTIs. Finally, we present DDR-FE method that predicts the effect types of a drug on its target. On different representative datasets, under various test setups, and using different performance measures, we show that DDR-FE achieves extremely good performance. Using blind test data, we verified as correct 2,300 out of 3,076 DTIs effects predicted by DDR-FE. This suggests that DDR-FE can be used as an efficient method to identify correct effects of a drug on its target.

The rapidly increasing number of existing drugs with genomic, biomedical, and pharmacological data make computational analyses possible, which reduces the search space for drugs and facilitates drug repositioning (DR). Thus, artificial intelligence, machine learning, and data mining have been used to identify biological interactions such as drug-target interactions (DTI), drug-disease associations, and drug-response. The prediction of these biological interactions is seen as a critical phase needed to make drug development more sustainable. Furthermore, late-stage drug development failures are usually a consequence of ineffective targets. Thus, proper target identification is needed. In this dissertation, we tried to address three crucial problems associated with the DR pipeline and presents several novel computational methods developed for DR. First, we developed three network-based DTI prediction methods using machine learning, graph embedding, and graph mining. These methods significantly improved prediction performance, and the best-performing method reduces the error rate by more than 33% across all datasets compared to the best state-of-the-art method. Second, because it is more insightful to predict continuous values that indicate how tightly the drug binds to a specific target, we conducted a comparison study of current regression-based methods that predict drug-target binding affinities (DTBA). We discussed how to develop more robust DTBA methods and subsequently developed Affinity2Vec, the first regression-based method that formulates the entire task as a graph-based method and combines several computational techniques from feature representation learning, graph mining, and machine learning with no 3D structural data of proteins. Affinity2Vec outperforms the state-of-the-art methods. Finally, since drug development failure is associated with sub-optimal target identification, we developed the first DL-based computational method (OncoRTT) to identify cancer-specific therapeutic targets for the ten most common cancers worldwide. Implementing our approach required creating a suitable dataset that could be used by the computational method to identify oncology-related DTIs. Thus, we created the OncologyTT datasets to build and evaluate our OncoRTT method. Our methods demonstrated their efficiency by achieving high prediction performance and identifying therapeutic targets for several cancer types. Overall, in this dissertation, we developed several computational methods to solve biomedical domain problems, specifically drug repositioning, and demonstrated their efficiencies and capabilities.