发表机构
University of Vienna; Christian Doppler Laboratory for Molecular Informatics in the Biosciences（维也纳大学; 克里斯蒂安·多普勒生物科学分子信息学实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
NEAT-POCKET是基于口袋条件的三维分子生成框架，在CrossDocked和SPINDR数据集上性能有竞争力且采样更快，可用于全分子生成及口袋条件下的片段补全，适用于基于结构的药物设计。
AI 中文摘要
AI驱动的从头分子设计为加速早期药物发现提供了可行途径，可直接在目标蛋白结合口袋内生成新型配体。我们提出NEAT-POCKET，它是自回归NEAT模型的口袋条件扩展，用于三维分子生成。NEAT-POCKET在蛋白口袋环境中逐原子生成分子，同时保持原子排列不变性并显式建模氢原子。在CrossDocked和SPINDR数据集上的基准测试显示，NEAT-POCKET在基于结构的生成任务中达到了有竞争力的性能，且采样速度明显快于现有基线方法。除了全分子生成外，NEAT-POCKET还自然支持口袋条件下的片段补全，该任务与先导化合物优化和骨架细化直接相关。这些结果表明，NEAT-POCKET是一种快速、灵活且实用的基于结构的药物设计框架。
英文摘要
AI-driven de novo molecular design offers a promising route to accelerate early-stage drug discovery by generating novel ligands directly within target protein binding pockets. We present NEAT-POCKET, a pocket-conditioned extension of the autoregressive NEAT model for 3D molecular generation. NEAT-POCKET generates molecules atom by atom in protein pocket environments while preserving atom permutation invariance and explicitly modeling hydrogen atoms. Benchmarks on the CrossDocked and SPINDR datasets show that NEAT-POCKET achieves competitive structure-based generation performance while sampling substantially faster than existing baselines. Beyond full-molecule generation, NEAT-POCKET naturally enables pocket-conditioned fragment completion, a task directly relevant to lead optimization and scaffold elaboration. These results position NEAT-POCKET as a fast, flexible, and practical framework for structure-based drug design.
