  Contrastive learning have been widely used as pretext tasks for
self-supervised pre-trained molecular representation learning models in
AI-aided drug design and discovery. However, exiting methods that generate
molecular views by noise-adding operations for contrastive learning may face
the semantic inconsistency problem, which leads to false positive pairs and
consequently poor prediction performance. To address this problem, in this
paper we first propose a semantic-invariant view generation method by properly
breaking molecular graphs into fragment pairs. Then, we develop a
Fragment-based Semantic-Invariant Contrastive Learning (FraSICL) model based on
this view generation method for molecular property prediction. The FraSICL
model consists of two branches to generate representations of views for
contrastive learning, meanwhile a multi-view fusion and an auxiliary similarity
loss are introduced to make better use of the information contained in
different fragment-pair views. Extensive experiments on various benchmark
datasets show that with the least number of pre-training samples, FraSICL can
achieve state-of-the-art performance, compared with major existing counterpart
models.
