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Michael R Draper¹, Asa Waterman¹, Jonathan E Dannatt¹

¹Chemistry Department, University of Dallas, Irving, Texas, 75062, USA. jdannatt@udallas.edu.

Physical chemistry chemical physics : PCCP

|February 20, 2024

概括

预测分子分裂系数 (log P) 对于药物设计至关重要. 量子力学方法,特别是使用三次ζ基数集的DFT,在最近的一次盲目的挑战中被证明是最有效的,用于准确计算这些值.

科学领域:

计算化学是一种计算化学.
药物的发现和开发.

背景情况:

分割系数 (log P) 是一个关键的物理化学性质,影响药理动力学,毒性和生物可用性.
准确预测log P可以显著加速药物设计过程.

研究的目的:

评估目前用于预测多烯-水分区系数 (log P_tol/w) 的计算方法.
在一个盲目的预测挑战中,比较量子力学 (QM),分子力学 (MM) 和机器学习 (ML) 方法的性能.

主要方法:

使用了三个不同的计算方法:QM,MM和ML.
这项研究涉及一个盲目的预测挑战 (SAMPL-9),重点是计算16种药物分子的log P_tol/w.

主要成果:

最初的盲目提交显示了1.53-2.93日志P_tol/w单位之间的平均未签名错误 (MUE).
质量管理方法,特别是使用三次ζ基数集的DFT,实现了1.00 log P_tol/w的降低MUE.
与DFT相比,MM和ML方法在较小的分子上表现良好,但在较大的分子上遇到了困难.

结论:

密度函数理论 (DFT) 与三次ζ基数组相结合,成为准确预测分区系数的最有效和最简单的方法.
这些发现突出了不同计算方法的优点和局限性,用于在药物发现背景下进行log P计算.

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