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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2310.00115 |
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| _version_ | 1866916338413338624 |
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| author | Zhu, Yanqiao Hwang, Jeehyun Adams, Keir Liu, Zhen Nan, Bozhao Stenfors, Brock Du, Yuanqi Chauhan, Jatin Wiest, Olaf Isayev, Olexandr Coley, Connor W. Sun, Yizhou Wang, Wei |
| author_facet | Zhu, Yanqiao Hwang, Jeehyun Adams, Keir Liu, Zhen Nan, Bozhao Stenfors, Brock Du, Yuanqi Chauhan, Jatin Wiest, Olaf Isayev, Olexandr Coley, Connor W. Sun, Yizhou Wang, Wei |
| contents | Molecular Representation Learning (MRL) has proven impactful in numerous biochemical applications such as drug discovery and enzyme design. While Graph Neural Networks (GNNs) are effective at learning molecular representations from a 2D molecular graph or a single 3D structure, existing works often overlook the flexible nature of molecules, which continuously interconvert across conformations via chemical bond rotations and minor vibrational perturbations. To better account for molecular flexibility, some recent works formulate MRL as an ensemble learning problem, focusing on explicitly learning from a set of conformer structures. However, most of these studies have limited datasets, tasks, and models. In this work, we introduce the first MoleculAR Conformer Ensemble Learning (MARCEL) benchmark to thoroughly evaluate the potential of learning on conformer ensembles and suggest promising research directions. MARCEL includes four datasets covering diverse molecule- and reaction-level properties of chemically diverse molecules including organocatalysts and transition-metal catalysts, extending beyond the scope of common GNN benchmarks that are confined to drug-like molecules. In addition, we conduct a comprehensive empirical study, which benchmarks representative 1D, 2D, and 3D molecular representation learning models, along with two strategies that explicitly incorporate conformer ensembles into 3D MRL models. Our findings reveal that direct learning from an accessible conformer space can improve performance on a variety of tasks and models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_00115 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Learning Over Molecular Conformer Ensembles: Datasets and Benchmarks Zhu, Yanqiao Hwang, Jeehyun Adams, Keir Liu, Zhen Nan, Bozhao Stenfors, Brock Du, Yuanqi Chauhan, Jatin Wiest, Olaf Isayev, Olexandr Coley, Connor W. Sun, Yizhou Wang, Wei Machine Learning Molecular Representation Learning (MRL) has proven impactful in numerous biochemical applications such as drug discovery and enzyme design. While Graph Neural Networks (GNNs) are effective at learning molecular representations from a 2D molecular graph or a single 3D structure, existing works often overlook the flexible nature of molecules, which continuously interconvert across conformations via chemical bond rotations and minor vibrational perturbations. To better account for molecular flexibility, some recent works formulate MRL as an ensemble learning problem, focusing on explicitly learning from a set of conformer structures. However, most of these studies have limited datasets, tasks, and models. In this work, we introduce the first MoleculAR Conformer Ensemble Learning (MARCEL) benchmark to thoroughly evaluate the potential of learning on conformer ensembles and suggest promising research directions. MARCEL includes four datasets covering diverse molecule- and reaction-level properties of chemically diverse molecules including organocatalysts and transition-metal catalysts, extending beyond the scope of common GNN benchmarks that are confined to drug-like molecules. In addition, we conduct a comprehensive empirical study, which benchmarks representative 1D, 2D, and 3D molecular representation learning models, along with two strategies that explicitly incorporate conformer ensembles into 3D MRL models. Our findings reveal that direct learning from an accessible conformer space can improve performance on a variety of tasks and models. |
| title | Learning Over Molecular Conformer Ensembles: Datasets and Benchmarks |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2310.00115 |