دورية أكاديمية
Tora3D: an autoregressive torsion angle prediction model for molecular 3D conformation generation
| العنوان: | Tora3D: an autoregressive torsion angle prediction model for molecular 3D conformation generation |
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| المؤلفون: | Zimei Zhang, Gang Wang, Rui Li, Lin Ni, RunZe Zhang, Kaiyang Cheng, Qun Ren, Xiangtai Kong, Shengkun Ni, Xiaochu Tong, Li Luo, Dingyan Wang, Xiaojie Lu, Mingyue Zheng, Xutong Li |
| المصدر: | Journal of Cheminformatics, Vol 15, Iss 1, Pp 1-14 (2023) |
| بيانات النشر: | BMC, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Information technology LCC:Chemistry |
| مصطلحات موضوعية: | Conformations generation, Autoregressive, Transformer, Deep learning, Small molecules, Information technology, T58.5-58.64, Chemistry, QD1-999 |
| الوصف: | Abstract Three-dimensional (3D) conformations of a small molecule profoundly affect its binding to the target of interest, the resulting biological effects, and its disposition in living organisms, but it is challenging to accurately characterize the conformational ensemble experimentally. Here, we proposed an autoregressive torsion angle prediction model Tora3D for molecular 3D conformer generation. Rather than directly predicting the conformations in an end-to-end way, Tora3D predicts a set of torsion angles of rotatable bonds by an interpretable autoregressive method and reconstructs the 3D conformations from them, which keeps structural validity during reconstruction. Another advancement of our method over other conformational generation methods is the ability to use energy to guide the conformation generation. In addition, we propose a new message-passing mechanism that applies the Transformer to the graph to solve the difficulty of remote message passing. Tora3D shows superior performance to prior computational models in the trade-off between accuracy and efficiency, and ensures conformational validity, accuracy, and diversity in an interpretable way. Overall, Tora3D can be used for the quick generation of diverse molecular conformations and 3D-based molecular representation, contributing to a wide range of downstream drug design tasks. Graphical Abstract |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1758-2946 |
| Relation: | https://doaj.org/toc/1758-2946 |
| DOI: | 10.1186/s13321-023-00726-8 |
| URL الوصول: | https://doaj.org/article/7084674fa08a498ca5be300cb2521cbb |
| رقم الأكسشن: | edsdoj.7084674fa08a498ca5be300cb2521cbb |
| قاعدة البيانات: | Directory of Open Access Journals |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 17582946 |
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| DOI: | 10.1186/s13321-023-00726-8 |