Generative models for molecules based on sequential line notation (for example, the simplified molecular-input line-entry system) or graph representation have attracted an increasing interest in the field of structure-based drug design, but they struggle to capture important three-dimensional (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, the fragment-based simplified molecular-input line-entry system with local and global coordinates, was developed to assist the model in learning molecular topologies and atomic spatial positions. Additionally, we trained a separate non-covalent 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 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.

基于连续行符号（例如，简化的分子输入行输入系统）或图形表示的分子生成模型在基于结构的药物设计领域引起了越来越多的兴趣，但它们难以捕获重要的三维模型(3D) 空间相互作用，通常会产生不良的分子结构。为了应对这些挑战，我们推出了Lingo3DMol，一种结合了语言模型和几何深度学习技术的基于袖珍的3D分子生成方法。开发了一种新的分子表示，即具有局部和全局坐标的基于片段的简化分子输入行输入系统，以帮助模型学习分子拓扑和原子空间位置。此外，我们训练了一个单独的非共价相互作用预测器，为生成模型提供必要的结合模式信息。Lingo3DMol 可以有效地穿越类似药物的化学空间，防止异常结构的形成。有用诱饵增强数据集目录用于评估。Lingo3DMol 在药物相似性、合成可及性、口袋结合模式和分子生成速度方面优于最先进的方法。