Automating the molecular design-make-test-analyze cycle accelerates hit and lead finding for drug discovery. Using deep learning for molecular design and a microfluidics platform for on-chip chemical synthesis, liver X receptor (LXR) agonists were generated from scratch. The computational pipeline was tuned to explore the chemical space of known LXRα agonists and generate novel molecular candidates. To ensure compatibility with automated on-chip synthesis, the chemical space was confined to the virtual products obtainable from 17 one-step reactions. Twenty-five de novo designs were successfully synthesized in flow. In vitro screening of the crude reaction products revealed 17 (68%) hits, with up to 60-fold LXR activation. The batch resynthesis, purification, and retesting of 14 of these compounds confirmed that 12 of them were potent LXR agonists. These results support the suitability of the proposed design-make-test-analyze framework as a blueprint for automated drug design with artificial intelligence and miniaturized bench-top synthesis.

自动化分子设计-制造-测试-分析周期可加速药物发现的命中和先导化合物的发现。利用深度学习进行分子设计和微流体平台进行片上化学合成，从头开始生成肝脏 X 受体 (LXR) 激动剂。计算管道经过调整，可探索已知 LXRα 激动剂的化学空间并生成新的候选分子。为了确保与自动片上合成的兼容性，化学空间仅限于从 17 个一步反应获得的虚拟产物。二十五个从头设​​计在流程中成功合成。粗反应产物的体外筛选显示 17 个 (68%) 命中，LXR 激活高达 60 倍。对其中 14 种化合物进行批量再合成、纯化和重新测试，证实其中 12 种是有效的 LXR 激动剂。这些结果支持所提出的设计-制造-测试-分析框架作为人工智能和小型化台式合成的自动化药物设计蓝图的适用性。