Multiple linear regression models for predicting the n-octanol/water partition coefficients in the SAMPL7 blind challenge

Lopez Kenneth; Pinheiro Silvana; Zamora William J.

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Multiple linear regression models for predicting the n-octanol/water partition coefficients in the SAMPL7 blind challenge

机译：多元线性回归模型预测SAMPL7盲挑战中正辛醇/水分配系数

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A multiple linear regression model called MLR-3 is used for predicting the experimental n-octanol/water partition coefficient (log P-N) of 22 N-sulfonamides proposed by the organizers of the SAMPL7 blind challenge. The MLR-3 method was trained with 82 molecules including drug-like sulfonamides and small organic molecules, which resembled the main functional groups present in the challenge dataset. Our model, submitted as "TFE-MLR", presented a root-mean-square error of 0.58 and mean absolute error of 0.41 in log P units, accomplishing the highest accuracy, among empirical methods and also in all submissions based on the ranked ones. Overall, the results support the appropriateness of multiple linear regression approach MLR-3 for computing the n-octanol/water partition coefficient in sulfonamide-bearing compounds. In this context, the outstanding performance of empirical methodologies, where 75 of the ranked submissions achieved root-mean-square errors < 1 log P units, support the suitability of these strategies for obtaining accurate and fast predictions of physicochemical properties as partition coefficients of bioorganic compounds.

机译：一种称为 MLR-3 的多元线性回归模型用于预测 SAMPL7 盲挑战组织者提出的 22 种 N-磺酰胺的实验正辛醇/水分配系数（log P-N）。MLR-3 方法使用 82 个分子进行训练，包括类药物磺胺类药物和有机小分子，它们类似于挑战数据集中存在的主要官能团。我们的模型以“TFE-MLR”的形式提交，在对数 P 单位中呈现出 0.58 的均方根误差和 0.41 的平均绝对误差，在经验方法和基于排名方法的所有提交中实现了最高的准确性。综上所述，多元线性回归方法MLR-3在含磺胺化合物中正辛醇/水分配系数的计算是适用的。在这种情况下，经验方法的出色表现，其中75%的排名提交实现了均方根误差<1 log P单位，支持这些策略的适用性，以获得准确和快速的物理化学性质预测作为生物有机化合物的分配系数。

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来源
《Journal of Computer-Aided Molecular Design》 |2021年第8期|923-931|共9页
作者
Lopez Kenneth; Pinheiro Silvana; Zamora William J.;
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作者单位

Univ Costa Rica, Sch Chem, San Jose, Costa Rica;

Fed Univ Para, Inst Exact & Nat Sci, BR-66075110 Belem, Para, Brazil;

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正文语种英语
中图分类电子计算机在化学中的应用;结构化学;
关键词
Multiple linear regression; Empirical methods; n-Octanol; water partition coefficients; N-sulfonamides; SAMPL7 blind challenge;

机译：多元线性回归;实证方法;正辛醇;水分配系数;N-磺酰胺类药物;SAMPL7 盲人挑战;

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Multiple linear regression models for predicting the n-octanol/water partition coefficients in the SAMPL7 blind challenge

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