自由能量扰动方法用于准确的水性可溶性预测
Richard S Hong1, Ana V Rojas2, Rajni Miglani Bhardwaj1
1AbbVie Inc., Research & Development, 1 N Waukegan Road, North Chicago, Illinois 60064, United States.
Journal of medicinal chemistry
|November 28, 2023
概括
预测药物溶解度是一个挑战. 基于物理的自由能量扰动 (FEP+) 方法准确计算热力学可溶性,超越机器学习模型,帮助药物设计.
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:
- 准确预测晶体热力学可溶性对于药物发现至关重要,特别是对于难以溶解的化合物.
- 目前的方法难以早期评估溶解度,阻碍了新药候选药物的开发.
研究的目的:
- 评估基于物理的自由能量扰动 (FEP+) 方法来计算热力学水溶性.
- 评估FEP+在各种化学空间中的预测和差异化能力.
- 将FEP+性能与最先进的机器学习方法进行比较.
主要方法:
- 详细评估自由能量扰动 (FEP+) 方法.
- 在药学相关文献化合物和复杂的AbbVie化合物中应用FEP+.
- 将FEP+预测与实验溶解度数据和机器学习模型进行比较.
主要成果:
- FEP+方法实现了0.86的根平均平方误差 (RMSE) 和0.69.6的R平方 (R2).
- 与使用基于量子力学的描述器的机器学习方法相比,FEP+显示出更高的预测和区分能力.
- 发现明确考虑晶体包装对于使用FEP+准确预测溶解度很重要.
结论:
- 基于物理学的FEP+方法为预测热力学水溶性提供了一个强大的工具.
- FEP+为实验溶解度提供了更好的相关性,有助于选择候选药物.
- 来自FEP+的计算能量可以为药物发现周期中的分子设计提供宝贵的见解.
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