Autor: |
Feng, Wei, Wang, Lvwei, Lin, Zaiyun, Zhu, Yanhao, Wang, Han, Dong, Jianqiang, Bai, Rong, Wang, Huting, Zhou, Jielong, Peng, Wei, Huang, Bo, Zhou, Wenbiao |
Rok vydání: |
2023 |
Předmět: |
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Druh dokumentu: |
Working Paper |
Popis: |
Generative models for molecules based on sequential line notation (e.g. SMILES) or graph representation have attracted an increasing interest in the field of structure-based drug design, but they struggle to capture important 3D spatial interactions and often produce undesirable molecular structures. To address these challenges, we introduce Lingo3DMol, a pocket-based 3D molecule generation method that combines language models and geometric deep learning technology. A new molecular representation, fragment-based SMILES with local and global coordinates, was developed to assist the model in learning molecular topologies and atomic spatial positions. Additionally, we trained a separate noncovalent interaction predictor to provide essential binding pattern information for the generative model. Lingo3DMol can efficiently traverse drug-like chemical spaces, preventing the formation of unusual structures. The Directory of Useful Decoys-Enhanced (DUD-E) dataset was used for evaluation. Lingo3DMol outperformed state-of-the-art methods in terms of drug-likeness, synthetic accessibility, pocket binding mode, and molecule generation speed. |
Databáze: |
arXiv |
Externí odkaz: |
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