MolPLA: A Molecular Pretraining Framework for Learning Cores, R-Groups
  and their Linker Joints
Mogan Gim Jueon Park Soyon Park Sanghoon Lee Seungheun Baek Junhyun Lee Ngoc-Quang Nguyen Jaewoo Kang
Molecular core structures and R-groups are essential concepts in drug
development. Integration of these concepts with conventional graph pre-training
approaches can promote deeper understanding in molecules. We propose MolPLA, a
novel pre-training framework that employs masked graph contrastive learning in
understanding the underlying decomposable parts inmolecules that implicate
their core structure and peripheral R-groups. Furthermore, we formulate an
additional framework that grants MolPLA the ability to help chemists find
replaceable R-groups in lead optimization scenarios. Experimental results on
molecular property prediction show that MolPLA exhibits predictability
comparable to current state-of-the-art models. Qualitative analysis implicate
that MolPLA is capable of distinguishing core and R-group sub-structures,
identifying decomposable regions in molecules and contributing to lead
optimization scenarios by rationally suggesting R-group replacements given
various query core templates. The code implementation for MolPLA and its
pre-trained model checkpoint is available at https://github.com/dmis-lab/MolPLA