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已知药物空间作为探索类药性化学空间边界的一种度量。

Known drug space as a metric in exploring the boundaries of drug-like chemical space.

机构信息

Cancer Research UK Centre for Cancer Therapeutics, Institute of Cancer Research, 15 Cotswold Road, Sutton, Surrey SM2 5NG, UK.

出版信息

Eur J Med Chem. 2009 Dec;44(12):5006-11. doi: 10.1016/j.ejmech.2009.08.014. Epub 2009 Sep 6.

DOI:10.1016/j.ejmech.2009.08.014
PMID:19782440
Abstract

In this work, marketed drug compounds (or known drug space) were used as a metric to test the principles of eliminating parent structures of the nitrenium ion (aryl-amine/nitro compounds) as well as sulphur and halogen containing molecules from screening compound collections. Molecules containing such moieties and/or atoms have biological and physiochemical properties, which possibly make them less attractive as leads in drug development. It was found that precursors to the nitrenium ion were relatively abundant in known drug space at 14%. Thus, their simple elimination from drug-like chemical space is not advisable. Interestingly, the mutagenic potential of the nitrenium ions is linked to their stability and quantum mechanical calculations can be used to estimate it. Furthermore, 24% of drugs investigated contained sulphur atoms and around 28% were halogenated. As some sulphur containing moieties were abundant whilst others were scarce, it was deduced that it would be more effective to eliminate specific molecular scaffolds rather than all sulphur containing molecules. In conclusion, it has been shown that by statistically analysing known drug space a better understanding of the boundaries of drug-like chemical space was established which can help medicinal chemists in finding rewarding regions of chemical space.

摘要

在这项工作中,市售药物化合物(或已知药物空间)被用作一种指标,以测试消除氮烯离子(芳基-胺/硝基化合物)母体结构以及从筛选化合物集中去除含硫和卤素分子的原则。含有此类部分和/或原子的分子具有生物和物理化学特性,这可能使它们在药物开发中作为先导化合物的吸引力降低。研究发现,氮烯离子的前体在已知的药物空间中相对丰富,占 14%。因此,不建议简单地从类似药物的化学空间中消除它们。有趣的是,氮烯离子的诱变潜力与其稳定性有关,量子力学计算可用于估计其稳定性。此外,在所研究的药物中,24%含有硫原子,约 28%被卤化。由于一些含硫部分丰富,而另一些则稀缺,因此可以推断,消除特定的分子骨架而不是所有含硫分子将更有效。总之,通过对已知药物空间进行统计分析,已经建立了对类似药物化学空间边界的更好理解,这有助于药物化学家在寻找有价值的化学空间区域。

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