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LogD Contributions of Substituents Commonly Used in Medicinal Chemistry.
ACS Medicinal Chemistry Letters ( IF 3.5 ) Pub Date : 2019-12-11 , DOI: 10.1021/acsmedchemlett.9b00489 Matthew L Landry 1 , James J Crawford 1
ACS Medicinal Chemistry Letters ( IF 3.5 ) Pub Date : 2019-12-11 , DOI: 10.1021/acsmedchemlett.9b00489 Matthew L Landry 1 , James J Crawford 1
Affiliation
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The importance of physicochemical property calculation and measurement is well-established in drug discovery. In particular, lipophilicity predictions play a central role in target design and prioritization. While significant progress has been made in our ability to calculate both logP and logD, the quality of these predictions is limited by the size and diversity of the underlying data set. Access to diverse data sets and advanced models is often limited to large organizations or consortia, and they are not available to many students and practitioners of medicinal chemistry. A molecular matched pair analysis of median ΔlogD 7.4 contributions for substituents commonly used in drug discovery programs at Genentech is reported. The results of this ΔlogD analysis are compiled into a single table, which we anticipate will be of use to practicing medicinal chemists.
中文翻译：
药物化学常用取代基的LogD贡献。
理化性质计算和测量的重要性已在药物发现中广为接受。特别是，亲脂性预测在目标设计和优先级划分中起着核心作用。尽管我们在计算logP和logD的能力方面已取得了重大进展，但这些预测的质量受到基础数据集的大小和多样性的限制。通常，只有大型组织或财团才能访问各种数据集和高级模型，并且许多医学化学的学生和从业人员都无法使用它们。报道了在Genentech的药物发现计划中常用的取代基的中值ΔlogD7.4贡献的分子匹配对分析。此ΔlogD分析的结果汇总到一个表中，
更新日期：2019-12-23
中文翻译：
药物化学常用取代基的LogD贡献。
理化性质计算和测量的重要性已在药物发现中广为接受。特别是，亲脂性预测在目标设计和优先级划分中起着核心作用。尽管我们在计算logP和logD的能力方面已取得了重大进展，但这些预测的质量受到基础数据集的大小和多样性的限制。通常，只有大型组织或财团才能访问各种数据集和高级模型，并且许多医学化学的学生和从业人员都无法使用它们。报道了在Genentech的药物发现计划中常用的取代基的中值ΔlogD7.4贡献的分子匹配对分析。此ΔlogD分析的结果汇总到一个表中，
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