CRCM, CNRS, Inserm, Institut Paoli-Calmettes , Aix-Marseille University , 13009 Marseille , France.
Department of Organic Chemistry , Lobachevsky State University of Nizhni Novgorod , 23 Gagarin Avenue , 603950 Nizhni Novgorod , Russia.
J Med Chem. 2018 Jul 12;61(13):5719-5732. doi: 10.1021/acs.jmedchem.8b00653. Epub 2018 Jun 22.
Over the past few decades, hit identification has been greatly facilitated by advances in high-throughput and fragment-based screenings. One major hurdle remaining in drug discovery is process automation of hit-to-lead (H2L) optimization. Here, we report a time- and cost-efficient integrated strategy for H2L optimization as well as a partially automated design of potent chemical probes consisting of a focused-chemical-library design and virtual screening coupled with robotic diversity-oriented de novo synthesis and automated in vitro evaluation. The virtual library is generated by combining an activated fragment, corresponding to the substructure binding to the target, with a collection of functionalized building blocks using in silico encoded chemical reactions carefully chosen from a list of one-step organic transformations relevant in medicinal chemistry. The proof of concept was demonstrated using the optimization of bromodomain inhibitors as a test case, leading to the validation of several compounds with improved affinity by several orders of magnitude.
在过去的几十年中，高通量和基于片段的筛选技术的进步极大地促进了命中鉴定。药物发现中仍然存在一个主要障碍，即命中至先导（H2L）优化的过程自动化。在这里，我们报告了一种用于 H2L 优化的省时、高效的综合策略，以及一种由聚焦化学文库设计和虚拟筛选与机器人导向的多样性从头合成和自动化体外评估相结合的有效化学探针的部分自动化设计。虚拟库是通过将与靶标结合的亚结构相对应的活性片段与一组功能化构建块组合生成的，使用计算机编码的化学反应，这些反应是从与药物化学相关的一步有机转化列表中精心选择的。使用溴结构域抑制剂的优化作为测试用例证明了该概念的可行性，导致了几个化合物的验证，其亲和力提高了几个数量级。
