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arXiv · 2401.16771

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
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Abstract

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

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Mogan Gim, Jueon Park, Soyon Park, Sanghoon Lee, Seungheun Baek, Junhyun Lee, Ngoc-Quang Nguyen, Jaewoo Kang. 2024-01-30. MolPLA: A Molecular Pretraining Framework for Learning Cores, R-Groups and their Linker Joints. https://arxiv.org/abs/2401.16771

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