Structure-based drug design powered by deep generative models have attracted increasing research interest in recent years. Language models have demonstrated a robust capacity for generating valid molecules in 2D structures, while methods based on geometric deep learning can directly produce molecules with accurate 3D coordinates. Inspired by both methods, this article proposes a pocket-based 3D molecule generation method that leverages the language model with the ability to generate 3D coordinates. High quality protein-ligand complex data are insufficient; hence, a perturbation and restoration pre-training task is designed that can utilize vast amounts of small-molecule data. A new molecular representation, a fragment-based SMILES with local and global coordinates, is also presented, enabling the language model to learn molecular topological structures and spatial position information effectively. Ultimately, CrossDocked and DUD-E dataset is employed for evaluation and additional metrics are introduced. This method achieves state-of-the-art performance in nearly all metrics, notably in terms of binding patterns, drug-like properties, rational conformations, and inference speed. Our model is available as an online service to academic users via sw3dmg.stonewise.cn

翻译：基于深度生成模型的药物设计方法近年来引起了越来越多的研究兴趣。语言模型已展现出生成有效二维结构分子的稳健能力，而基于几何深度学习的方法则能直接生成具有精确三维坐标的分子。受这两种方法的启发，本文提出了一种基于蛋白口袋的三维分子生成方法，该方法利用语言模型生成三维坐标的能力。由于高质量的蛋白-配体复合物数据不足，我们设计了一种扰动-重建预训练任务，可利用海量小分子数据。同时，提出了一种新的分子表示——基于片段的带有局部与全局坐标的SMILES表示，使语言模型能够有效学习分子拓扑结构与空间位置信息。最终，采用CrossDocked和DUD-E数据集进行评估，并引入了额外的评价指标。该方法在几乎所有指标上均达到了当前最优性能，尤其是在结合模式、类药性质、合理构象及推理速度方面表现突出。我们的模型以在线服务形式向学术用户开放，网址为sw3dmg.stonewise.cn。