Title: CLUSMOL: CLUSTER-ASSISTED CONTRASTIVE LEARNING FOR MOLECULAR REPRESENTATIONS
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: Contrastive Learning, Molecular Represention

Abstract: Effective molecular representations are crucial for tasks like molecular property
prediction and de novo molecular design. While deep learning has shifted molec-
ular representation learning from hand-crafted fingerprints to learned features,
the high cost of labeled data has motivated self-supervised learning (SSL) on
large-scale unlabeled datasets, with contrastive learning being a leading approach.
However, existing methods rely on augmented views of the same molecule
as positives, overlooking structural and functional similarities between distinct
molecules. To address this, we propose the Functional Group-Guided Molecular
Graphs (FGMG) algorithm, which constructs molecular graphs with linear com-
plexity while better capturing structure-activity relationships (SAR). Based on
FGMG, we introduce CLUSMOL, a cluster-assisted contrastive learning pretrain-
ing framework that assigns graph-labels via graph clustering and employs a Chem-
ical Semantic-Weighted Contrastive Loss (SemCoL) to treat molecules within the
same cluster as “soft positive” pairs. Pretrained on three million molecules, CLUS-
MOL significantly outperforms state-of-the-art SSL baselines across downstream
tasks, demonstrating the benefit of combining functional group–aware graph con-
struction with cluster-assisted contrastive learning.