Integrating DNA-encoded chemical libraries with virtual combinatorial library screening: Optimizing a PARP10 inhibitor.

Integrating DNA-encoded chemical libraries with virtual combinatorial library screening: Optimizing a PARP10 inhibitor.
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DOI:
10.1016/j.bmcl.2020.127464
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发表时间:
2020-10-01
影响因子:
2.7
通讯作者:
Franzini RM
Franzini RM
中科院分区:
医学4区
文献类型:
--
作者:
Lemke M;Ravenscroft H;Rueb NJ;Kireev D;Ferraris D;Franzini RM

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药物开发的两个关键步骤是 1) 发现对靶标具有所需效果的分子,以及 2) 将此类分子优化为具有翻译所需效力和药代动力学特性的先导化合物。如今,DNA 编码化学文库 (DECL) 可以前所未有地轻松产生命中,而先导化合物优化正在成为限制步骤。在这里,我们将 DECL 筛选与基于结构的计算方法相结合,以简化先导化合物的开发。所提出的工作流程包括枚举源自 DECL 筛选命中的虚拟组合库 (VCL),并使用计算结合预测来识别相对于原始 DECL 命中具有增强特性的分子。作为概念验证演示,我们应用这种方法来鉴定一种 PARP10 抑制剂,它比最初的 DECL 筛选结果更有效且更具药物性。
Two critical steps in drug development are 1) the discovery of molecules that have the desired effects on a target, and 2) the optimization of such molecules into lead compounds with the required potency and pharmacokinetic properties for translation. DNA-encoded chemical libraries (DECLs) can nowadays yield hits with unprecedented ease, and lead-optimization is becoming the limiting step. Here we integrate DECL screening with structure-based computational methods to streamline the development of lead compounds. The presented workflow consists of enumerating a virtual combinatorial library (VCL) derived from a DECL screening hit and using computational binding prediction to identify molecules with enhanced properties relative to the original DECL hit. As proof-of-concept demonstration, we applied this approach to identify an inhibitor of PARP10 that is more potent and druglike than the original DECL screening hit.
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