Two different types of approaches: (a) approaches that combine quantitative structure activity relationships, quantum mechanical electronic structure methods, and machine-learning and, (b) electronic structure vertical solvation approaches, were used to predict the logP coefficients of 11 molecules as part of the SAMPL6 logP blind prediction challenge. Using electronic structures optimized with density functional theory (DFT), several molecular descriptors were calculated for each molecule, including van der Waals areas and volumes, HOMO/LUMO energies, dipole moments, polarizabilities, and electrophilic and nucleophilic superdelocalizabilities. A multilinear regression model and a partial least squares model were used to train a set of 97 molecules. As well, descriptors were generated using the molecular operating environment and used to create additional machine learning models. Electronic structure vertical solvation approaches considered include DFT and the domain-based local pair natural orbital methods combined with the solvated variant of the correlation consistent composite approach.

中文翻译：

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SAMPL6 logP 挑战：机器学习和量子力学方法。


两种不同类型的方法：(a) 结合定量结构活性关系、量子力学电子结构方法和机器学习的方法，以及 (b) 电子结构垂直溶剂化方法，用于预测 11 个分子的 logP 系数作为一部分SAMPL6 logP 盲预测挑战。使用密度泛函理论 (DFT) 优化的电子结构，计算了每个分子的几个分子描述符，包括范德华面积和体积、HOMO/LUMO 能量、偶极矩、极化率以及亲电和亲核超离域性。使用多线性回归模型和偏最小二乘模型来训练一组 97 个分子。此外，描述符是使用分子操作环境生成的，并用于创建其他机器学习模型。考虑的电子结构垂直溶剂化方法包括 DFT 和基于域的局部对自然轨道方法与相关一致复合方法的溶剂化变体相结合。