自由空间--一组新的商业化分子,用于发现新药。

IF 2.8 4区 医学 Q3 CHEMISTRY, MEDICINAL
Mykola V Protopopov, Valentyna V Tararina, Fanny Bonachera, Igor M Dzyuba, Anna Kapeliukha, Serhii Hlotov, Oleksii Chuk, Gilles Marcou, Olga Klimchuk, Dragos Horvath, Erik Yeghyan, Olena Savych, Olga O Tarkhanova, Alexandre Varnek, Yurii S Moroz
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引用次数: 0

摘要

如今,高性能虚拟筛选技术的出现使药物设计人员能够探索超大规模的候选化合物集,寻找具有预期特性的分子。然而,这种努力的成功在很大程度上依赖于这些候选化合物的相关性(药物相似性,最重要的是化学可行性),否则,根据垃圾进/垃圾出原则,虚拟筛选将返回无价值的 "命中"。经过审慎枚举的 Enamine REAL Space 的大受欢迎充分证明了大数据趋势在药物发现中的优势。在此,我们将介绍一个名为 "自由空间"(Freedom space)的按需制造化合物新数据集。它遵循恩胺真实空间的原则,包含高度可行的分子(合成成功率超过 75%)。然而,支架和化学分析显示,它与 REAL 和 ChEMBL 数据库中的生物注释化合物存在显著差异。自由空间是 REAL 空间的重要扩展,可用于在寻找新药和新药先导活动中更全面地探索合成上可行的化学空间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The freedom space - a new set of commercially available molecules for hit discovery.

The advent of high-performance virtual screening techniques nowadays allows drug designers to explore ultra-large sets of candidate compounds in search of molecules predicted to have desired properties. However, the success of such an endeavor heavily relies on the pertinence (drug-likeness and, foremost, chemical feasibility) of these candidates, or otherwise, virtual screening will return valueless "hits", by the garbage in/garbage out principle. The huge popularity of the judiciously enumerated Enamine REAL Space is clear proof of the strength of this Big Data trend in drug discovery. Here we describe a new dataset of make-on-demand compounds called the Freedom space. It follows the principles of Enamine REAL Space and contains highly feasible molecules (synthesis success rate over 75 percent). However, the scaffold and chemography analysis revealed significant differences to both the REAL and biologically annotated compounds from the ChEMBL database. The Freedom Space is a significant extension of the REAL Space and can be utilized for a more comprehensive exploration of the synthetically feasible chemical space in hit finding and hit-to-lead campaigns.

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来源期刊
Molecular Informatics
Molecular Informatics CHEMISTRY, MEDICINAL-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
7.30
自引率
2.80%
发文量
70
审稿时长
3 months
期刊介绍: Molecular Informatics is a peer-reviewed, international forum for publication of high-quality, interdisciplinary research on all molecular aspects of bio/cheminformatics and computer-assisted molecular design. Molecular Informatics succeeded QSAR & Combinatorial Science in 2010. Molecular Informatics presents methodological innovations that will lead to a deeper understanding of ligand-receptor interactions, macromolecular complexes, molecular networks, design concepts and processes that demonstrate how ideas and design concepts lead to molecules with a desired structure or function, preferably including experimental validation. The journal''s scope includes but is not limited to the fields of drug discovery and chemical biology, protein and nucleic acid engineering and design, the design of nanomolecular structures, strategies for modeling of macromolecular assemblies, molecular networks and systems, pharmaco- and chemogenomics, computer-assisted screening strategies, as well as novel technologies for the de novo design of biologically active molecules. As a unique feature Molecular Informatics publishes so-called "Methods Corner" review-type articles which feature important technological concepts and advances within the scope of the journal.
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