Molecular representation learning facilitates multiple downstream tasks such as molecular property prediction (MPP) and drug design. Recent studies have shown great promise in applying self-supervised learning (SSL) to cope with the data scarcity in MPP. Contrastive learning (CL) is a typical SSL method used to learn prior knowledge so that the trained model has better generalization performance on various downstream tasks. One important issue of CL is how to generate enhanced samples that preserve the molecular core semantics for each training sample, which may significantly impact the earnings of the CL strategy. To address this issue, we propose the partial Feature Masking-based molecular Graph Contrastive Learning model (FMGCL). FMGCL constructs the masked molecular graph by masking partial features of each atom and bond in the featured molecular graph. Since the masking molecular graphs preserve the chemical structure of the molecules, they do not violate the chemical semantics of molecules, which is beneficial for capturing valuable prior knowledge of molecules during pre-training. Then, FMGCL fine-tunes the well-trained encoder on the featured molecular graph for downstream tasks. Moreover, we propose using the relative distance between samples within a batch to enhance the performance in regression tasks. Experiments on the 12 benchmark datasets from MoleculeNet and ChEMBL showed the superiority of FMGCL.
