开发和测试高精度的终点自由能量方法. 3:使用Poisson-Boltzmann方法预测分区系数,并与SAMPL挑战的溶剂可访问表面积模型相结合
Taoyu Niu1, Xibing He1, Fengyang Han1
1Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA. juw79@pitt.edu.
Physical chemistry chemical physics : PCCP
|December 6, 2023
概括
这项研究使用Poisson-Boltzmann/Surface Area (PBSA) 方法准确预测药物分区系数,在SAMPL9挑战中实现了最高准确度的溶解自由能量预测. 这种精细的方法对药物分发分析具有前景.
科学领域:
- 计算化学计算化学
- 药物发现 药物发现 药物发现
- 物理化学 物理化学
背景情况:
- 准确预测溶解自由能量对于蛋白质 - 配体结合的自由能量至关重要.
- 分割系数 (log P) 是药物在体内分布的关键物理化学性质.
- 从溶解自由能量中获得的转移自由能量,直接告知log P计算.
研究的目的:
- 应用Poisson-Boltzmann (PB) 表面积 (SA) 方法来预测烯/水转移自由能量和分离系数 (log P) 来自无溶解能量 (SFEs).
- 为改进的非极性SA模型开发和验证与溶剂相关的新型分子表面张力参数.
- 评估开发方法的预测准确性与已确定的挑战和数据集相比.
主要方法:
- 使用Poisson-Boltzmann (PB) 表面积 (SA) 方法,使用Open Babel生成的单一溶液构造.
- 采用使用一般AMBER力场2 (GAFF2) 原子类型基于球体半径的优化边界定义.
- 开发了与溶剂相关的新分子表面张力参数 (γ和b),用于针对实验性SFEs的烯和环素.
主要成果:
- 在SAMPL9挑战中,在18个提交项目中,实现了最高的预测准确性 (RMSE 1.52 kcal mol-1) 转移自由能量.
- 进一步降低预测RMSE到1.33 kcalmol-1使用多重构成策略与分子动力学 (MD) 模拟.
- 在SAMPL5数据集 (RMSEPBSA = 1.88对RMSECOSMO-RS = 2.11日志单位) 上,在预测循环/水分布系数 (log D) 方面表现优于COSMO-RS.
结论:
- 精细的Poisson-Boltzmann/表面积 (PBSA) 方法在预测转移自由能量和分割系数方面表现出卓越的准确性.
- 开发的方法为药物分发属性和错误来源提供了有价值的见解.
- 这种方法为药物发现和开发提供了一个强大的计算工具.
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