  Deep learning has been a prevalence in computational chemistry and widely
implemented in molecule property predictions. Recently, self-supervised
learning (SSL), especially contrastive learning (CL), gathers growing attention
for the potential to learn molecular representations that generalize to the
gigantic chemical space. Unlike supervised learning, SSL can directly leverage
large unlabeled data, which greatly reduces the effort to acquire molecular
property labels through costly and time-consuming simulations or experiments.
However, most molecular SSL methods borrow the insights from the machine
learning community but neglect the unique cheminformatics (e.g., molecular
fingerprints) and multi-level graphical structures (e.g., functional groups) of
molecules. In this work, we propose iMolCLR: improvement of Molecular
Contrastive Learning of Representations with graph neural networks (GNNs) in
two aspects, (1) mitigating faulty negative contrastive instances via
considering cheminformatics similarities between molecule pairs; (2)
fragment-level contrasting between intra- and inter-molecule substructures
decomposed from molecules. Experiments have shown that the proposed strategies
significantly improve the performance of GNN models on various challenging
molecular property predictions. In comparison to the previous CL framework,
iMolCLR demonstrates an averaged 1.3% improvement of ROC-AUC on 7
classification benchmarks and an averaged 4.8% decrease of the error on 5
regression benchmarks. On most benchmarks, the generic GNN pre-trained by
iMolCLR rivals or even surpasses supervised learning models with sophisticated
architecture designs and engineered features. Further investigations
demonstrate that representations learned through iMolCLR intrinsically embed
scaffolds and functional groups that can reason molecule similarities.
