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AutoDesigner - 核心设计,一种化学支架的从头设计算法:在新型选择性 Wee1 抑制剂的设计和合成中的应用。

AutoDesigner - Core Design, a De Novo Design Algorithm for Chemical Scaffolds: Application to the Design and Synthesis of Novel Selective Wee1 Inhibitors.

机构信息

Schrödinger, Inc., 1540 Broadway, 24th floor, New York, New York 10036, United States.

出版信息

J Chem Inf Model. 2024 Oct 14;64(19):7513-7524. doi: 10.1021/acs.jcim.4c01031. Epub 2024 Oct 3.

DOI:10.1021/acs.jcim.4c01031
PMID:39360587
Abstract

The hit identification stage of a drug discovery program generally involves the design of novel chemical scaffolds with desired biological activity against the target(s) of interest. One common approach is scaffold hopping, which is the manual design of novel scaffolds based on known chemical matter. One major limitation of this approach is narrow chemical space exploration, which can lead to difficulties in maintaining or improving biological activity, selectivity, and favorable property space. Another limitation is the lack of preliminary structure-activity relationship (SAR) data around these designs, which could lead to selecting suboptimal scaffolds to advance lead optimization. To address these limitations, we propose AutoDesigner - Core Design (CoreDesign), a scaffold design algorithm. Our approach is a cloud-integrated, design algorithm for systematically exploring and refining chemical scaffolds against biological targets of interest. The algorithm designs, evaluates, and optimizes a vast range, from millions to billions, of molecules in silico, following defined project parameters encompassing structural novelty, physicochemical attributes, potency, and selectivity using active-learning FEP. To validate CoreDesign in a real-world drug discovery setting, we applied it to the design of novel, potent Wee1 inhibitors with improved selectivity over PLK1. Starting from a single known ligand and receptor structure, CoreDesign rapidly explored over 23 billion molecules to identify 1,342 novel chemical series with a mean of 4 compounds per scaffold. To rapidly analyze this large amount of data and prioritize chemical scaffolds for synthesis, we utilize t-Distributed Stochastic Neighbor Embedding (t-SNE) plots of in silico properties. The chemical space projections allowed us to rapidly identify a structurally novel 5-5 fused core meeting all the hit-identification requirements. Several compounds were synthesized and assayed from the scaffold, displaying good potency against Wee1 and excellent PLK1 selectivity. Our results suggest that CoreDesign can significantly speed up the hit-identification process and increase the probability of success of drug discovery campaigns by allowing teams to bring forward high-quality chemical scaffolds derisked by the availability of preliminary SAR.

摘要

药物发现项目的命中鉴定阶段通常涉及设计具有针对目标的预期生物活性的新型化学支架。一种常见的方法是支架跳跃,即基于已知化学物质手动设计新型支架。这种方法的一个主要局限性是化学空间探索狭窄,这可能导致难以维持或提高生物活性、选择性和有利的性质空间。另一个限制是缺乏这些设计的初步结构-活性关系 (SAR) 数据,这可能导致选择次优的支架来推进先导优化。为了解决这些限制,我们提出了 AutoDesigner - Core Design (CoreDesign),这是一种支架设计算法。我们的方法是一种云集成的、针对感兴趣的生物靶标的系统探索和细化化学支架的设计算法。该算法使用主动学习 FEP 根据定义的项目参数设计、评估和优化从数百万到数十亿的分子,这些参数包括结构新颖性、物理化学属性、效力和选择性。为了在实际的药物发现环境中验证 CoreDesign,我们将其应用于设计新型、强效的 Wee1 抑制剂,与 PLK1 相比具有更高的选择性。从一个已知的配体和受体结构开始,CoreDesign 迅速探索了超过 230 亿个分子,以识别出 1342 种具有平均 4 种化合物/支架的新型化学系列。为了快速分析大量数据并为合成确定化学支架的优先级,我们利用了虚拟属性的 t-Distributed Stochastic Neighbor Embedding (t-SNE) 图。化学空间投影使我们能够快速识别出符合所有命中鉴定要求的结构新颖的 5-5 融合核心。从支架中合成和测定了几种化合物,显示出对 Wee1 的良好效力和对 PLK1 的优异选择性。我们的结果表明,CoreDesign 通过提供初步 SAR 的可用性来降低风险的高质量化学支架,从而大大加快命中鉴定过程并增加药物发现活动成功的可能性。

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