Development and test of highly accurate endpoint free energy methods. 2: Prediction of logarithm of n ‐octanol–water partition coefficient ( logP ) for druglike molecules using MM‐PBSA method
Development and test of highly accurate endpoint free energy methods. 2: Prediction of logarithm of n ‐octanol–water partition coefficient ( logP ) for druglike molecules using MM‐PBSA method
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高精度端点自由能方法的开发和测试。
DOI：
10.1002/jcc.27086
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发表时间：
2023
影响因子：
3
通讯作者：
Wang, Junmei
中科院分区：
文献类型：
--
作者：
Sun, Yuchen;Hou, Tingjun;He, Xibing;Man, Viet Hoang;Wang, Junmei
正辛醇-水分配系数（logP）的对数常用作药物发现中亲脂性的指标，其对候选药物的吸收、分布、代谢、排泄和毒性具有实质性影响。考虑到该性质的实验测量是昂贵和耗时的，开发可靠的logP预测模型是非常重要的。在这项研究中，我们开发了一个基于转移自由能的logP预测模型-FElogP。FElogP基于简单的原理，即logP由分子从水转移到辛醇的自由能变化决定。计算转移自由能的基本物理方法是分子力学-泊松玻尔兹曼表面积（MM-PBSA），因此这种方法被称为基于自由能的logP（FElogP）。FElogP模型的优越性通过ZINC数据库中大量707种结构不同的分子进行了验证，这些分子的测量质量很高。令人鼓舞的是，FElogP优于几种常用的QSPR或基于机器学习的logP模型，以及一些基于连续溶剂化模型的方法。预测值和测量值之间的均方根误差（RMSE）和皮尔逊相关系数（R）分别为0.91 log单位和0.71，而亚军，OpenBabel中实现的logP模型的RMSE为1.13 log单位，R为0.67。由于FElogP没有直接针对实验logP进行参数化，因此其优异的性能可能扩展到由一般AMBER力场覆盖的任意有机分子。
The logarithm ofn‐octanol–water partition coefficient (logP) is frequently used as an indicator of lipophilicity in drug discovery, which has substantial impacts on the absorption, distribution, metabolism, excretion, and toxicity of a drug candidate. Considering that the experimental measurement of the property is costly and time‐consuming, it is of great importance to develop reliable prediction models for logP. In this study, we developed a transfer free energy‐based logP prediction model‐FElogP. FElogP is based on the simple principle that logP is determined by the free energy change of transferring a molecule from water ton‐octanol. The underlying physical method to calculate transfer free energy is the molecular mechanics‐Poisson Boltzmann surface area (MM‐PBSA), thus this method is named as free energy‐based logP (FElogP). The superiority of FElogP model was validated by a large set of 707 structurally diverse molecules in the ZINC database for which the measurement was of high quality. Encouragingly, FElogP outperformed several commonly‐used QSPR or machine learning‐based logP models, as well as some continuum solvation model‐based methods. The root‐mean‐square error (RMSE) and Pearson correlation coefficient (R) between the predicted and measured values are 0.91 log units and 0.71, respectively, while the runner‐up, the logP model implemented in OpenBabel had an RMSE of 1.13 log units and R of 0.67. Given the fact that FElogP was not parameterized against experimental logP directly, its excellent performance is likely to be expanded to arbitrary organic molecules covered by the general AMBER force fields.