MolPLA: A Molecular Pretraining Framework for Learning Cores, R-Groups and their Linker Joints
This work addresses the challenge of integrating core and R-group concepts into molecular pretraining for drug development, representing an incremental advancement in domain-specific AI applications.
The authors tackled the problem of molecular understanding in drug development by proposing MolPLA, a pretraining framework that learns core structures and R-groups, achieving predictability comparable to state-of-the-art models on molecular property prediction tasks.
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