结合SILCS和人工智能,实现药物分子被动透性的高通量预测
Poonam Pandey1, Alexander D MacKerell1
1Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, 20 Penn St., HSF II-633, Baltimore, Maryland 21201, United States.
Journal of chemical information and modeling
|September 8, 2023
概括
预测药物分子膜透性可以加速药物设计. 通过将基于物理的Site Identification by Ligand Competitive Saturation (SILCS) 与人工智能 (AI) 结合起来,为被动药物透性创建了一个高度准确的预测模型.
科学领域:
- 计算化学的计算化学
- 药用化学 医学化学
- 药理学 药理学是指药理学的学科.
背景情况:
- 膜透性对于开发新药至关重要.
- 预测模型可以通过早期评估候选药物来加速药物发现.
- 目前的方法可能缺乏有效药物设计所需的准确性和速度.
研究的目的:
- 开发一种用于被动药物分子透性的高通量预测模型.
- 将基于物理的模拟与机器学习相结合,以提高预测.
- 提供对影响药物透性的因素的见解.
主要方法:
- 通过合物竞争性和 (SILCS) 方法进行场地识别,以获得合物无电网能量 (LGFE) 配置文件.
- 使用人工智能 (AI),特别是随机森林回归,用于预测建模.
- 在229种类似药物分子的实验PAMPA透性数据上训练模型.
主要成果:
- 随机森林模型在一个独立的数据集上实现了0.81的R平方值.
- 该模型成功地将SILCS衍生的自由能源配置文件作为输入特征集成.
- 结合方法提供了快速而准确的估计,对连接体的透性.
结论:
- 混合SILCS-AI模型在预测药物分子膜透性方面取得了重大进展.
- 这种方法通过提供快速,准确的透率估计来加速药物设计过程.
- 该模型提供了对连接体-膜相互作用的宝贵见解,促进了合理的药物设计.
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