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用于预测三嗪大环化合物logD的经验方法的适应性

Adaptation of Empirical Methods to Predict the LogD of Triazine Macrocycles.

作者信息

Patterson-Gardner Casey J, Pavelich Gretchen M, Cannon April T, Menke Alexander J, Simanek Eric E

机构信息

Department of Chemistry & Biochemistry, Texas Christian University, Fort Worth, Texas 76129, United States.

出版信息

ACS Med Chem Lett. 2023 Sep 8;14(10):1378-1382. doi: 10.1021/acsmedchemlett.3c00290. eCollection 2023 Oct 12.

Abstract

Octanol/water partition coefficients guide drug design, but algorithms do not always accurately predict these values. For cationic triazine macrocycles that adopt a conserved folded shape in solution, common algorithms fall short. Here, the logD values for 12 macrocycles differing in amino acid choice were predicted and then measured experimentally. On average, AlogP, XlogP, and ChemAxon predictions deviate by 0.9, 2.8, and 3.9 log units, with XlogP overestimating lipophilicity and AlogP and ChemAxon underestimating lipophilicity. Importantly, however, a linear relationship ( > 0.98) exists between the values predicted by AlogP and the experimentally determined logD values, thus enabling more accurate predictions.

摘要

正辛醇/水分配系数指导药物设计,但算法并不总是能准确预测这些值。对于在溶液中采用保守折叠形状的阳离子三嗪大环化合物,常见算法并不适用。在此,预测了12种氨基酸选择不同的大环化合物的logD值,然后进行了实验测量。平均而言,AlogP、XlogP和ChemAxon预测值的偏差分别为0.9、2.8和3.9个对数单位,其中XlogP高估了亲脂性,而AlogP和ChemAxon低估了亲脂性。然而,重要的是,AlogP预测值与实验测定的logD值之间存在线性关系(>0.98),从而能够进行更准确的预测。

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