Chemical predictive modelling to improve compound quality

被引：218

作者：

Cumming, John G. ^{[1
]}

Davis, Andrew M. ^{[2
]}

Muresan, Sorel ^{[3
]}

Haeberlein, Markus ^{[4
]}

Chen, Hongming ^{[3
]}

机构：

[1] AstraZeneca R&D, Chem Innovat Ctr, Discovery Sci, Macclesfield SK10 4TG, Cheshire, England

[2] AstraZeneca R&D, Resp Inflammat & Autoimmun Innovat Med Unit, S-43183 Molndal, Sweden

[3] AstraZeneca R&D, Chem Innovat Ctr, Discovery Sci, S-43183 Molndal, Sweden

[4] AstraZeneca R&D, CNS & Pain Innovat Med, S-15185 Sodertalje, Sweden

来源：

NATURE REVIEWS DRUG DISCOVERY | 2013年 / 12卷 / 12期

关键词：

MATCHED MOLECULAR PAIRS; METABOLIC STABILITY MEASUREMENTS; DE-NOVO DESIGN; QSAR MODELS; MULTIPARAMETER OPTIMIZATION; COMPUTATIONAL CHEMISTRY; APPLICABILITY DOMAIN; AQUEOUS SOLUBILITY; DRUG DISCOVERY; ADMET RULES;

D O I：

10.1038/nrd4128

中图分类号：

Q81 [生物工程学（生物技术）]; Q93 [微生物学];

学科分类号：

071005 ; 0836 ; 090102 ; 100705 ;

摘要：

The 'quality' of small-molecule drug candidates, encompassing aspects including their potency, selectivity and ADMET (absorption, distribution, metabolism, excretion and toxicity) characteristics, is a key factor influencing the chances of success in clinical trials. Importantly, such characteristics are under the control of chemists during the identification and optimization of lead compounds. Here, we discuss the application of computational methods, particularly quantitative structure-activity relationships (QSARs), in guiding the selection of higher-quality drug candidates, as well as cultural factors that may have affected their use and impact.

引用

页码：948 / 962

页数：15

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