Wale, NikilKarypis, George2020-09-022020-09-022008-10-29https://hdl.handle.net/11299/215778Drug discovery is an expensive process. It has been estimated that a new drug compound that is introduced in the market after FDA approval carries a cost of approximately $800 million from the conception of target implicated for a disease to successful identification of chemical entity or drug that is successful in human trials. There is a need to cut the cost of developing new drugs (to bring overall cost lower for the producers and consumers alike) by identifying promising candidate targets as well as compounds and to tackle problems such an toxicity, lack of efficacy in humans, and poor physical properties in the early stages of drug discovery. In order to achieve this objective, in recent years the development of computational techniques that identify all the likely targets for a given chemical compound has been an active area of research. Identification of all the potential targets for a chemical compound provides insights into its potential toxicity, helps in repositioning it, and also provides insights into the behavior and relation among targets themselves from the perspective of small molecules. In this paper we address this problem of target identification in the context of small molecule. We present a set of techniques whose goal is to rank or prioritize the targets in the context of a given chemical compound such that most targets that have a potential to show activity to this compound appear higher in the ranked list. These methods are motivated by recent advances in category ranking and protein secondary structure prediction approaches and utilize target-ligand activity data to prioritize targets. Our extensive experimental evaluation shows that most of the methods developed in this work are either competitive or substantially outperform previously developed approaches to solve the above problem in drug discovery.en-USReport