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ChemoDOTS:一个用于设计化学驱动的聚焦文库的网络服务器。

ChemoDOTS: a web server to design chemistry-driven focused libraries.

机构信息

CRCM, CNRS, Inserm, Institut Paoli-Calmettes, Aix-Marseille Univ, Marseille 13273, France.

出版信息

Nucleic Acids Res. 2024 Jul 5;52(W1):W461-W468. doi: 10.1093/nar/gkae326.

DOI:10.1093/nar/gkae326
PMID:38686808
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11223810/
Abstract

In drug discovery, the successful optimization of an initial hit compound into a lead molecule requires multiple cycles of chemical modification. Consequently, there is a need to efficiently generate synthesizable chemical libraries to navigate the chemical space surrounding the primary hit. To address this need, we introduce ChemoDOTS, an easy-to-use web server for hit-to-lead chemical optimization freely available at https://chemodots.marseille.inserm.fr/. With this tool, users enter an activated form of the initial hit molecule then choose from automatically detected reactive functions. The server proposes compatible chemical transformations via an ensemble of encoded chemical reactions widely used in the pharmaceutical industry during hit-to-lead optimization. After selection of the desired reactions, all compatible chemical building blocks are automatically coupled to the initial hit to generate a raw chemical library. Post-processing filters can be applied to extract a subset of compounds with specific physicochemical properties. Finally, explicit stereoisomers and tautomers are computed, and a 3D conformer is generated for each molecule. The resulting virtual library is compatible with most docking software for virtual screening campaigns. ChemoDOTS rapidly generates synthetically feasible, hit-focused, large, diverse chemical libraries with finely-tuned physicochemical properties via a user-friendly interface providing a powerful resource for researchers engaged in hit-to-lead optimization.

摘要

在药物发现中,将初始命中化合物成功优化为先导分子需要多次化学修饰循环。因此,需要有效地生成可合成的化学文库,以探索主要命中周围的化学空间。为了满足这一需求,我们引入了 ChemoDOTS,这是一个易于使用的网页服务器,用于命中到先导的化学优化,可在 https://chemodots.marseille.inserm.fr/ 免费获得。使用此工具,用户输入初始命中分子的活化形式,然后从自动检测到的反应性功能中进行选择。该服务器通过在命中到先导优化期间在制药行业广泛使用的一组编码化学反应提出相容的化学转化。选择所需的反应后,所有相容的化学构建块都自动与初始命中连接,以生成原始化学文库。可以应用后处理过滤器来提取具有特定物理化学性质的化合物子集。最后,计算出明确的立体异构体和互变异构体,并为每个分子生成 3D 构象。生成的虚拟文库与大多数对接软件兼容,可用于虚拟筛选活动。ChemoDOTS 通过用户友好的界面快速生成具有精细调整的物理化学性质的合成可行、命中聚焦、大型、多样化的化学文库,为从事命中到先导优化的研究人员提供了强大的资源。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cbac/11223810/dc9145d160ae/gkae326fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cbac/11223810/467c5daaa57f/gkae326figgra1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cbac/11223810/1f64f59ce7bc/gkae326fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cbac/11223810/927bf4f057fc/gkae326fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cbac/11223810/dc9145d160ae/gkae326fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cbac/11223810/467c5daaa57f/gkae326figgra1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cbac/11223810/1f64f59ce7bc/gkae326fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cbac/11223810/927bf4f057fc/gkae326fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cbac/11223810/dc9145d160ae/gkae326fig3.jpg

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本文引用的文献

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PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences.PoseBusters:基于人工智能的对接方法无法生成符合物理原理的构象,也无法推广到新序列。
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DFRscore: Deep Learning-Based Scoring of Synthetic Complexity with Drug-Focused Retrosynthetic Analysis for High-Throughput Virtual Screening.
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Nat Commun. 2023 May 29;14(1):3079. doi: 10.1038/s41467-023-38668-2.
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Fragment-based drug discovery supports drugging 'undruggable' protein-protein interactions.基于片段的药物发现支持对“不可成药”的蛋白-蛋白相互作用进行药物研发。
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