  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.
