3D Interaction Geometric Pre-training for Molecular Relational Learning
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arXiv
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| Format: | Preprint |
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2024
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| author | Lee, Namkyeong Oh, Yunhak Noh, Heewoong Na, Gyoung S. Xu, Minkai Wang, Hanchen Fu, Tianfan Park, Chanyoung |
| author_facet | Lee, Namkyeong Oh, Yunhak Noh, Heewoong Na, Gyoung S. Xu, Minkai Wang, Hanchen Fu, Tianfan Park, Chanyoung |
| contents | Molecular Relational Learning (MRL) is a rapidly growing field that focuses on understanding the interaction dynamics between molecules, which is crucial for applications ranging from catalyst engineering to drug discovery. Despite recent progress, earlier MRL approaches are limited to using only the 2D topological structure of molecules, as obtaining the 3D interaction geometry remains prohibitively expensive. This paper introduces a novel 3D geometric pre-training strategy for MRL (3DMRL) that incorporates a 3D virtual interaction environment, overcoming the limitations of costly traditional quantum mechanical calculation methods. With the constructed 3D virtual interaction environment, 3DMRL trains 2D MRL model to learn the global and local 3D geometric information of molecular interaction. Extensive experiments on various tasks using real-world datasets, including out-of-distribution and extrapolation scenarios, demonstrate the effectiveness of 3DMRL, showing up to a 24.93% improvement in performance across 40 tasks. Our code is publicly available at https://github.com/Namkyeong/3DMRL. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_02957 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | 3D Interaction Geometric Pre-training for Molecular Relational Learning Lee, Namkyeong Oh, Yunhak Noh, Heewoong Na, Gyoung S. Xu, Minkai Wang, Hanchen Fu, Tianfan Park, Chanyoung Machine Learning Artificial Intelligence Molecular Relational Learning (MRL) is a rapidly growing field that focuses on understanding the interaction dynamics between molecules, which is crucial for applications ranging from catalyst engineering to drug discovery. Despite recent progress, earlier MRL approaches are limited to using only the 2D topological structure of molecules, as obtaining the 3D interaction geometry remains prohibitively expensive. This paper introduces a novel 3D geometric pre-training strategy for MRL (3DMRL) that incorporates a 3D virtual interaction environment, overcoming the limitations of costly traditional quantum mechanical calculation methods. With the constructed 3D virtual interaction environment, 3DMRL trains 2D MRL model to learn the global and local 3D geometric information of molecular interaction. Extensive experiments on various tasks using real-world datasets, including out-of-distribution and extrapolation scenarios, demonstrate the effectiveness of 3DMRL, showing up to a 24.93% improvement in performance across 40 tasks. Our code is publicly available at https://github.com/Namkyeong/3DMRL. |
| title | 3D Interaction Geometric Pre-training for Molecular Relational Learning |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2412.02957 |