Drug-target continuous binding affinity prediction using multiple sources of information

Abstract

Drug-target binding affinity prediction has a significant role in the search for new drugs or novel targets for existing drugs. The vast majority of recent computational approaches, presented for the task of drug-target binding affinity prediction, make use of a single source to measure drug-drug or protein-protein similarities. Incorporating various information sources is of the essence for improving the accuracy of drug-target prediction. The main objective of this research is to propose a method for combining the information provided from various similarity measures for drug-drug and protein-protein similarities and to show that this leads to better prediction performance. For this purpose, we propose a method that makes use of five drug-drug and five protein-protein similarity measures simultaneously to predict the binding affinity value of an input query drug-protein interaction. In the proposed method, using each pair of drug-drug and protein-protein similarity measures, k-nearest neighbor algorithm is used to find k drug-protein pairs most similar to the input interaction. The information regarding the binding affinity values of neighbors and their similarities are fed as features to a gradient boosting machine to construct the regression model. To assess the performance of our method in comparison with state-of-the-art methods in the literature, three related benchmark datasets were used. The experimental results in various settings (pairwise, new drug, and new target scenarios) indicate the superiority of the proposed method in comparison with other methods proposed in the literature.