Conf-GEM: A geometric information-assisted direct conformation generation model
Molecular conformations generation (MCG) aims to efficiently obtain reasonable and stable three-dimensional (3D) atomic coordinates of the atoms in the molecule from scratch, providing a structural foundation for molecular representation learning models and advanced downstream molecular design tasks...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
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| Series: | Artificial Intelligence Chemistry |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2949747724000320 |
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| _version_ | 1846137552726130688 |
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| author | Zhijiang Yang Youjun Xu Li Pan Tengxin Huang Yunfan Wang Junjie Ding Liangliang Wang Junhua Xiao |
| author_facet | Zhijiang Yang Youjun Xu Li Pan Tengxin Huang Yunfan Wang Junjie Ding Liangliang Wang Junhua Xiao |
| author_sort | Zhijiang Yang |
| collection | DOAJ |
| description | Molecular conformations generation (MCG) aims to efficiently obtain reasonable and stable three-dimensional (3D) atomic coordinates of the atoms in the molecule from scratch, providing a structural foundation for molecular representation learning models and advanced downstream molecular design tasks such as molecular property prediction, molecular generation, and molecular docking. Existing MCG methods mostly rely on indirect distance-based strategies, which which can result in geometrically unrealistic conformations, or direct coordinate-based methods, which have larger search spaces and are prone to overfitting. Therefore, this study introduces Conf-GEM, a novel geometric information-assisted direct conformation generation model based on E-GeoGNN, a geometrically augmented 3D graph neural network with multiple scales. Pre-training and divide-and-conquer strategies, are integrated into the proposed model. Conf-GEM outperforms RDKit and nine deep-learning-based MCG models on the GEOM-QM9 and GEOM-Drugs datasets, achieving conformational coverage of 96.69% and 96.07%, respectively, without force field optimization. It also excels on the X-ray diffraction crystal structure dataset with up to 97.04% conformational coverage. In conclusion, Conf-GEM provides a novel solution for stabilizing 3D conformations generation. We provide an online prediction service (https://confgem.cmdrg.com) with a user-friendly interface for researchers. |
| format | Article |
| id | doaj-art-0773a5801972491bacf28671f3bf0f96 |
| institution | Kabale University |
| issn | 2949-7477 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Artificial Intelligence Chemistry |
| spelling | doaj-art-0773a5801972491bacf28671f3bf0f962024-12-08T06:13:44ZengElsevierArtificial Intelligence Chemistry2949-74772024-12-0122100074Conf-GEM: A geometric information-assisted direct conformation generation modelZhijiang Yang0Youjun Xu1Li Pan2Tengxin Huang3Yunfan Wang4Junjie Ding5Liangliang Wang6Junhua Xiao7State Key Laboratory of NBC Protection for Civilian, ChinaInfinite Intelligence Pharma, Beijing 100020, ChinaState Key Laboratory of NBC Protection for Civilian, ChinaState Key Laboratory of NBC Protection for Civilian, ChinaState Key Laboratory of NBC Protection for Civilian, ChinaState Key Laboratory of NBC Protection for Civilian, China; Corresponding authors.State Key Laboratory of NBC Protection for Civilian, China; Corresponding authors.State Key Laboratory of NBC Protection for Civilian, China; Corresponding authors.Molecular conformations generation (MCG) aims to efficiently obtain reasonable and stable three-dimensional (3D) atomic coordinates of the atoms in the molecule from scratch, providing a structural foundation for molecular representation learning models and advanced downstream molecular design tasks such as molecular property prediction, molecular generation, and molecular docking. Existing MCG methods mostly rely on indirect distance-based strategies, which which can result in geometrically unrealistic conformations, or direct coordinate-based methods, which have larger search spaces and are prone to overfitting. Therefore, this study introduces Conf-GEM, a novel geometric information-assisted direct conformation generation model based on E-GeoGNN, a geometrically augmented 3D graph neural network with multiple scales. Pre-training and divide-and-conquer strategies, are integrated into the proposed model. Conf-GEM outperforms RDKit and nine deep-learning-based MCG models on the GEOM-QM9 and GEOM-Drugs datasets, achieving conformational coverage of 96.69% and 96.07%, respectively, without force field optimization. It also excels on the X-ray diffraction crystal structure dataset with up to 97.04% conformational coverage. In conclusion, Conf-GEM provides a novel solution for stabilizing 3D conformations generation. We provide an online prediction service (https://confgem.cmdrg.com) with a user-friendly interface for researchers.http://www.sciencedirect.com/science/article/pii/S2949747724000320Molecular conformations generationVariational autoencoderDivide-and-conquer methodPre-trainingGeometric enhanced graph neural network |
| spellingShingle | Zhijiang Yang Youjun Xu Li Pan Tengxin Huang Yunfan Wang Junjie Ding Liangliang Wang Junhua Xiao Conf-GEM: A geometric information-assisted direct conformation generation model Artificial Intelligence Chemistry Molecular conformations generation Variational autoencoder Divide-and-conquer method Pre-training Geometric enhanced graph neural network |
| title | Conf-GEM: A geometric information-assisted direct conformation generation model |
| title_full | Conf-GEM: A geometric information-assisted direct conformation generation model |
| title_fullStr | Conf-GEM: A geometric information-assisted direct conformation generation model |
| title_full_unstemmed | Conf-GEM: A geometric information-assisted direct conformation generation model |
| title_short | Conf-GEM: A geometric information-assisted direct conformation generation model |
| title_sort | conf gem a geometric information assisted direct conformation generation model |
| topic | Molecular conformations generation Variational autoencoder Divide-and-conquer method Pre-training Geometric enhanced graph neural network |
| url | http://www.sciencedirect.com/science/article/pii/S2949747724000320 |
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