Generation of 3D Molecules in Pockets via Language Model

Wei Feng; Lvwei Wang; Zaiyun Lin; Yanhao Zhu; Han Wang; Jianqiang Dong; Rong Bai; Huting Wang; Jielong Zhou; Wei Peng; Bo Huang; Wenbiao Zhou

arXiv · 2305.10133

Generation of 3D Molecules in Pockets via Language Model

Wei Feng·Lvwei Wang·Zaiyun Lin·Yanhao Zhu·Han Wang·Jianqiang Dong·Rong Bai·Huting Wang·Jielong Zhou·Wei Peng·Bo Huang·Wenbiao Zhou

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Abstract

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.

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BibTeX RIS

Wei Feng, Lvwei Wang, Zaiyun Lin, Yanhao Zhu, Han Wang, Jianqiang Dong, Rong Bai, Huting Wang, Jielong Zhou, Wei Peng, Bo Huang, Wenbiao Zhou. 2023-12-11. Generation of 3D Molecules in Pockets via Language Model. https://arxiv.org/abs/2305.10133

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