Target Fishing for Chemical Compounds Using Target-Ligand Activity Data and Ranking Based Methods

摘要

In recent years, the development of computational techniques that identify all the likely targets for a given chemical compound, also termed as the problem of Target Fishing, has been an active area of research. Identification of likely targets of a chemical compound in the early stages of drug discovery helps to understand issues such as selectivity, off-target pharmacology, and toxicity. In this paper, we present a set of techniques whose goal is to rank or prioritize targets in the context of a given chemical compound so that most targets against which this compound may show activity appear higher in the ranked list. These methods are based on our extensions to the SVM and ranking perceptron algorithms for this problem. Our extensive experimental study shows that the methods developed in this work outperform previous approaches 2% to 60% under different evaluation criterions.