用三维药效团直接生成可合成的药物文库，无需后续设计
JEDEL: Zero-Shot DNA-Encoded Library Design for Early-Stage Drug Discovery
- 基于药效团几何结构生成可实验合成的分子库
- 在18个靶点上优于随机和多样性基线，且无需重新训练
- 适合早期药物发现中快速构建高潜力化合物库
我们提出JEDEL，一种从活性配体的三维药效团表征直接生成可合成DNA编码文库（DELs）的框架。JEDEL是首个将药效团相互作用模式映射为可操作、可扩展合成指令的模型，能够设计包含数百万种分子的靶向文库。与现有生成方法不同，JEDEL在可采购起始物料和已验证反应范围内运行，确保每种输出均实验可实现。该模型学习药效团几何与分子结构间的预测对齐，并规模化解码为组合合成路径。在18个蛋白靶点上，其生成的聚焦文库在预测结合亲和力、药效团恢复率和样本效率方面均优于随机和多样性基线，且无需针对特定靶点再训练。JEDEL实现了从虚拟分子生成到实验可部署文库设计的转变。
原文摘要 · Abstract (English)
We present JEDEL, a framework for generating synthesis-ready DNA-encoded libraries (DELs) directly from three-dimensional pharmacophore representations of active ligands. JEDEL is the first model to map pharmacophore interaction patterns to actionable, scalable synthesis instructions, enabling the design of targeted libraries comprising potentially millions of molecules. Unlike existing generative approaches that produce virtual compounds requiring downstream synthesis planning, JEDEL operates within the space of purchasable building blocks and validated reactions, ensuring that every output is experimentally realizable by construction. JEDEL learns a predictive alignment between pharmacophore geometry and molecular structure and decodes this into combinatorial synthesis routes at scale. Across 18 protein targets, it generates focused libraries that outperform random and diversity-based baselines in predicted binding affinity, pharmacophore recovery, and sample efficiency, without target-specific retraining. JEDEL enables a shift from virtual molecule generation to experimentally deployable library design.
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