GraphCL-DTA: a graph contrastive learning with molecular semantics for drug-target binding affinity prediction

TL;DR

GraphCL-DTA introduces a graph contrastive learning framework that enhances drug representation quality by preserving molecular semantics and optimizing representation uniformity, leading to improved drug-target binding affinity prediction accuracy.

Contribution

It proposes a novel contrastive learning approach for molecular graphs and a new loss function to improve drug and target representations without extra supervised data.

Findings

Outperforms state-of-the-art models on KIBA and Davis datasets.

Effectively preserves molecular semantics in drug representations.

Enhances the uniformity and quality of drug-target embeddings.

Abstract

Drug-target binding affinity prediction plays an important role in the early stages of drug discovery, which can infer the strength of interactions between new drugs and new targets. However, the performance of previous computational models is limited by the following drawbacks. The learning of drug representation relies only on supervised data, without taking into account the information contained in the molecular graph itself. Moreover, most previous studies tended to design complicated representation learning module, while uniformity, which is used to measure representation quality, is ignored. In this study, we propose GraphCL-DTA, a graph contrastive learning with molecular semantics for drug-target binding affinity prediction. In GraphCL-DTA, we design a graph contrastive learning framework for molecular graphs to learn drug representations, so that the semantics of molecular graphs are preserved. Through this graph contrastive framework, a more essential and effective drug representation can be learned without additional supervised data. Next, we design a new loss function that can be directly used to smoothly adjust the uniformity of drug and target representations. By directly optimizing the uniformity of representations, the representation quality of drugs and targets can be improved. The effectiveness of the above innovative elements is verified on two real datasets, KIBA and Davis. The excellent performance of GraphCL-DTA on the above datasets suggests its superiority to the state-of-the-art model.