动力溶解性:实验和机器学习建模的观点
Shamkhal Baybekov1, Pierre Llompart1,2, Gilles Marcou1
1Laboratoire de Chémoinformatique UMR 7140 CNRS, Institut Le Bel, University of Strasbourg, 4 Rue Blaise Pascal, 67081, Strasbourg, France.
这项研究表明,动力溶解度测试是可重现的,可以建模的. 运动溶解度的预测模型是必不可少的,因为在药物发现中不能用热力学溶解度代替.
科学领域:
- 药用化学 医学化学
- 计算化学计算化学
- 药物发现 药物发现 药物发现
背景情况:
- 在早期药物发现中,动力溶解度对于高通量查至关重要.
- 动力溶解性测试通常由于协议敏感性而表现出低的可重现性.
- 与热力学可溶性相比,对于动态可溶性开发量化结构与属性关系 (QSPR) 模型的努力有限.
研究的目的:
- 调查动力溶解性试验的可复制性和可建模性.
- 分析动力和热力学可溶性之间的关系.
- 评估不同动力溶解性试验数据的一致性.
主要方法:
- 分析动力和热力学可溶性数据之间的相关性.
- 检查各种动力溶解性试验数据的一致性.
- 使用合并数据集开发和评估用于动力溶解性的QSPR模型.
主要成果:
- 观察到的动力和热力学可溶性之间的差异与现有文献一致.
- 与预期相反,来自不同动力溶解度运动的数据之间显示出良好的一致性.
- 使用组合数据集实现了高性能QSPR模型的动力溶解度.
- 展示了热力学可溶性QSPR模型在动力学可溶性数据上的表现不佳,证实了它们缺乏相关性.
结论:
- 动力和热力学可溶性是不同的特性,不能互换使用.
- 动力溶解性测定显示出比通常认为的更好的可重现性.
- 对动力溶解度的预测QSPR模型的开发是可行的和鼓励的.
- 通过预测器网络服务可以获得免费访问的动力溶解度QSPR模型.
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