Evaluating the Diversity and Target Addressability of DELs using Scaffold Analysis and Machine Learning.

IF 4 3区医学 Q2 CHEMISTRY, MEDICINAL

ACS Medicinal Chemistry Letters Pub Date : 2025-01-25 eCollection Date: 2025-02-13 DOI:10.1021/acsmedchemlett.4c00505

Yaëlle Fischer, Ruel Cedeno, Dhoha Triki, Bertrand Vivet, Philippe Schambel

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引用次数: 0

Abstract

DELs enable efficient experimental screening of vast combinatorial libraries, offering a powerful platform for drug discovery. Apart from ensuring the druglike physicochemical properties, other key parameters to maximize the success rate of DEL designs include the scaffold diversity and target addressability. While several tools exist to assess chemical diversity, a dedicated computational approach combining both parameters is currently lacking. Here, we present a cheminformatics tool leveraging scaffold analysis and machine learning to evaluate both scaffold diversity and target-orientedness. Using two in-house libraries as a case study, we demonstrate the workflow's ability to distinguish between generalist and focused libraries. This capability can guide medicinal chemists in selecting libraries tailored for specific objectives, such as hit-finding or hit-optimization. To facilitate utilization, this tool is freely available both as a web application and as a Python script at https://github.com/novalixofficial/NovaWebApp.

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利用脚手架分析和机器学习评估DELs的多样性和目标可寻址性。

DELs能够对大量组合文库进行有效的实验筛选，为药物发现提供了强大的平台。除了确保类似药物的物理化学性质外，最大限度地提高DEL设计成功率的其他关键参数包括支架多样性和目标寻址性。虽然存在几种评估化学多样性的工具，但目前缺乏一种结合这两个参数的专用计算方法。在这里，我们提出了一种化学信息学工具，利用支架分析和机器学习来评估支架多样性和靶向性。使用两个内部库作为案例研究，我们演示了工作流区分通才库和重点库的能力。该功能可以指导药物化学家选择针对特定目标定制的库，例如查找命中或命中优化。为了方便使用，这个工具可以作为web应用程序和Python脚本在https://github.com/novalixofficial/NovaWebApp上免费获得。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

ACS Medicinal Chemistry Letters CHEMISTRY, MEDICINAL-

CiteScore

7.30

自引率

2.40%

发文量

328

审稿时长

1 months

期刊介绍： ACS Medicinal Chemistry Letters is interested in receiving manuscripts that discuss various aspects of medicinal chemistry. The journal will publish studies that pertain to a broad range of subject matter, including compound design and optimization, biological evaluation, drug delivery, imaging agents, and pharmacology of both small and large bioactive molecules. Specific areas include but are not limited to: Identification, synthesis, and optimization of lead biologically active molecules and drugs (small molecules and biologics) Biological characterization of new molecular entities in the context of drug discovery Computational, cheminformatics, and structural studies for the identification or SAR analysis of bioactive molecules, ligands and their targets, etc. Novel and improved methodologies, including radiation biochemistry, with broad application to medicinal chemistry Discovery technologies for biologically active molecules from both synthetic and natural (plant and other) sources Pharmacokinetic/pharmacodynamic studies that address mechanisms underlying drug disposition and response Pharmacogenetic and pharmacogenomic studies used to enhance drug design and the translation of medicinal chemistry into the clinic Mechanistic drug metabolism and regulation of metabolic enzyme gene expression Chemistry patents relevant to the medicinal chemistry field.