ContraDTI: Improved drug–target interaction prediction via multi-view contrastive learning

Abstract

Drug–target interaction (DTI) identification is one of the crucial issues in the field of drug discovery. Machine learning approaches offer efficient ways to address this issue, reducing expensive and time-consuming laboratory experiments. However, the scarcity of annotated drug data with labels restricts supervised machine learning applications to DTI prediction. Drawing inspiration from recent advances in contrastive learning, we present ContraDTI—a novel framework that adopts multi-view contrastive learning to overcome data limitations in this paper. Our model considers the molecular graph of a drug as the main view and the SMILES string of a drug as the side view, employing two types of loss functions for the contrast of the main view and the cross-view alignment between the main and the side views. Extensive experiments on both single-target and multi-target DTI datasets demonstrate that ContraDTI enhances the classification performance of DTI prediction, particularly when labeled data is scarce. ContraDTI can be a powerful tool for DTI prediction in data-limited scenarios. The code of this paper is available at https://github.com/zhiruiliao/ContraDTI.