基于蛋白口袋生成三维分子，速度快且能补全片段
NEAT-POCKET: Pocket-Conditioned Autoregressive 3D Molecular Generation with a Neighborhood-Guided Set Transformer
- 用邻域引导的集合变换器，原子逐个生成并保持排列不变性
- 在CrossDocked和SPINDR数据集上性能媲美顶尖模型，采样速度更快
- 适合药物先导化合物优化与骨架扩展，可直接用于结构导向设计
基于AI的从头分子设计为加速早期药物发现提供了新路径，可直接在靶点蛋白结合口袋内生成新型配体。本文提出NEAT-POCKET，是自回归模型NEAT的口袋条件化扩展，能够在蛋白口袋环境中逐原子生成3D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表明，NEAT-POCKET实现了具有竞争力的基于结构的分子生成性能，且采样速度显著快于现有基线方法。除了完整分子生成外，该模型还可自然支持口袋条件下的片段补全任务，直接服务于先导化合物优化与骨架拓展。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。
原文摘要 · Abstract (English)
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.
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