Accurately predicting molecular properties can identify more promising drug candidates and facilitate the process of drug discovery. There are advances in methods for molecular property prediction using self-supervised learning. However, most methods tend to focus only on a certain representation, resulting in reduced information diversity. Moreover, less attention is paid to the functional groups in the molecule, which are highly related to the molecular properties. To solve the problem, a multimodal contrastive learning framework called MMCL for molecular property prediction is proposed, which learns common features among similar molecules by utilizing different views of molecules for predicting molecular properties. In addition, by explicitly representing functional groups as nodes in the molecular graph, the model is prompted to learn features related to molecular properties. Prediction results on benchmark datasets and a series of case studies show that MMCL has optimal performance in most property prediction tasks and can capture information related to chemical semantics. Overall, MMCL can be used as an efficient deep learning tool for molecular-related tasks to facilitate the drug discovery process.
Gao et al. (Thu,) studied this question.