MolPLA: A Molecular Pretraining Framework for Learning Cores, R-Groups and their Linker Joints
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916110218035200 |
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| author | Gim, Mogan Park, Jueon Park, Soyon Lee, Sanghoon Baek, Seungheun Lee, Junhyun Nguyen, Ngoc-Quang Kang, Jaewoo |
| author_facet | Gim, Mogan Park, Jueon Park, Soyon Lee, Sanghoon Baek, Seungheun Lee, Junhyun Nguyen, Ngoc-Quang Kang, Jaewoo |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_16771 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MolPLA: A Molecular Pretraining Framework for Learning Cores, R-Groups and their Linker Joints Gim, Mogan Park, Jueon Park, Soyon Lee, Sanghoon Baek, Seungheun Lee, Junhyun Nguyen, Ngoc-Quang Kang, Jaewoo Machine Learning 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 |
| title | MolPLA: A Molecular Pretraining Framework for Learning Cores, R-Groups and their Linker Joints |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2401.16771 |