极端梯度增强与符合预测器相结合,用于信息化溶解度估计.
1Pharmaceutical Manufacturing Technology Centre, Bernal Institute, Department of Chemical Sciences, University of Limerick, V94 T9PX Limerick, Ireland.
Molecules (Basel, Switzerland)
|January 11, 2024
概括
这项研究引入了一种使用极端梯度增强 (XGB) 的新四步方法,以准确预测化学化合物溶解度并评估预测可靠性. 该方法确保超过95%的化合物属于适用性领域,在没有实验数据的情况下提供精确的误差幅度.
科学领域:
- 计算化学计算化学
- 化学信息学 化学信息学
- 机器学习在化学中的应用
背景情况:
- 准确预测化学化合物溶解度对于药物发现和化学过程设计至关重要.
- 现有的方法往往只关注预测的准确性,忽视预测的可靠性和适用性领域.
研究的目的:
- 开发和验证一个全面的四步计算方法来预测实验溶解度.
- 在大型化学数据库中建立可溶性预测的适用性领域.
- 量化化学化合物的预测不确定性和准确度等级.
主要方法:
- 利用极端梯度增强 (XGB) 算法来预测溶解度和识别关键分子描述符.
- 在大型数据集 (Drugbank,PubChem,COCONUT) 上使用精选和未精选的AquaSolDB数据进行了适用性域 (AD) 测试.
- 应用了合规预测来产生狭窄的预测间隔,并根据实验溶解度值验证它们.
主要成果:
- 实现了0.59-0.76Log(S) 的预测准确度 (RMSE) 对于水和0.62-0.79Log(S) 对于有机溶剂.
- 证明了大约50万种化合物的95%以上属于已确定的适用性领域.
- 成功估计了可溶性预测的个别错误率和准确度类.
结论:
- 开发的四步方法为可靠的可溶性预测和不确定性量化提供了强大的框架.
- 这种方法超出了典型的可溶性预测研究的范围,包括适用性领域评估和预测间隔生成.
- 该方法允许对庞大的化学库进行准确的溶解度预测和错误估计,而不需要实验性溶解度数据.
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