Deep Geometry Handling and Fragment-wise Molecular 3D Graph Generation
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866914735771877376 |
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| author | Zhang, Odin Huang, Yufei Cheng, Shichen Yu, Mengyao Zhang, Xujun Lin, Haitao Zeng, Yundian Wang, Mingyang Wu, Zhenxing Zhao, Huifeng Zhang, Zaixi Hua, Chenqing Kang, Yu Cui, Sunliang Pan, Peichen Hsieh, Chang-Yu Hou, Tingjun |
| author_facet | Zhang, Odin Huang, Yufei Cheng, Shichen Yu, Mengyao Zhang, Xujun Lin, Haitao Zeng, Yundian Wang, Mingyang Wu, Zhenxing Zhao, Huifeng Zhang, Zaixi Hua, Chenqing Kang, Yu Cui, Sunliang Pan, Peichen Hsieh, Chang-Yu Hou, Tingjun |
| contents | Most earlier 3D structure-based molecular generation approaches follow an atom-wise paradigm, incrementally adding atoms to a partially built molecular fragment within protein pockets. These methods, while effective in designing tightly bound ligands, often overlook other essential properties such as synthesizability. The fragment-wise generation paradigm offers a promising solution. However, a common challenge across both atom-wise and fragment-wise methods lies in their limited ability to co-design plausible chemical and geometrical structures, resulting in distorted conformations. In response to this challenge, we introduce the Deep Geometry Handling protocol, a more abstract design that extends the design focus beyond the model architecture. Through a comprehensive review of existing geometry-related models and their protocols, we propose a novel hybrid strategy, culminating in the development of FragGen - a geometry-reliable, fragment-wise molecular generation method. FragGen marks a significant leap forward in the quality of generated geometry and the synthesis accessibility of molecules. The efficacy of FragGen is further validated by its successful application in designing type II kinase inhibitors at the nanomolar level. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_00014 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep Geometry Handling and Fragment-wise Molecular 3D Graph Generation Zhang, Odin Huang, Yufei Cheng, Shichen Yu, Mengyao Zhang, Xujun Lin, Haitao Zeng, Yundian Wang, Mingyang Wu, Zhenxing Zhao, Huifeng Zhang, Zaixi Hua, Chenqing Kang, Yu Cui, Sunliang Pan, Peichen Hsieh, Chang-Yu Hou, Tingjun Chemical Physics Artificial Intelligence Biomolecules Most earlier 3D structure-based molecular generation approaches follow an atom-wise paradigm, incrementally adding atoms to a partially built molecular fragment within protein pockets. These methods, while effective in designing tightly bound ligands, often overlook other essential properties such as synthesizability. The fragment-wise generation paradigm offers a promising solution. However, a common challenge across both atom-wise and fragment-wise methods lies in their limited ability to co-design plausible chemical and geometrical structures, resulting in distorted conformations. In response to this challenge, we introduce the Deep Geometry Handling protocol, a more abstract design that extends the design focus beyond the model architecture. Through a comprehensive review of existing geometry-related models and their protocols, we propose a novel hybrid strategy, culminating in the development of FragGen - a geometry-reliable, fragment-wise molecular generation method. FragGen marks a significant leap forward in the quality of generated geometry and the synthesis accessibility of molecules. The efficacy of FragGen is further validated by its successful application in designing type II kinase inhibitors at the nanomolar level. |
| title | Deep Geometry Handling and Fragment-wise Molecular 3D Graph Generation |
| topic | Chemical Physics Artificial Intelligence Biomolecules |
| url | https://arxiv.org/abs/2404.00014 |