通过使用机器学习在各种温度下预测二元溶剂混合物中的药物溶解度
Zeqing Bao1, Gary Tom2,3,4, Austin Cheng2,3,4
1Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON, M5S 3M2, Canada.
Journal of cheminformatics
|October 29, 2024
概括
机器学习模型准确地预测药物在各种溶剂混合物和温度中的溶解度,克服了先前研究的局限性. 这一进步有助于药物开发,为新分子和配方提供可靠的可溶性预测.
科学领域:
- 计算化学是一种计算化学.
- 药物的发现和开发.
- 机器学习应用程序 机器学习应用程序
背景情况:
- 药物溶解度在药物开发中至关重要,但测量却很难.
- 现有的机器学习模型主要预测固定温度下的水溶性.
- 这限制了它们在现实世界药物配方和分析中的适用性.
研究的目的:
- 开发和验证机器学习模型,用于在不同温度的二元溶剂混合物中预测药物溶解度.
- 为培训和验证创建一个包含27,000个可溶性数据点的综合数据集.
- 为制药研究提供一个更实用和多功能工具.
主要方法:
- 编制了一个数据集,包括在不同温度下在二元溶剂混合物中对小分子的27,000次溶解度测量.
- 使用贝叶斯优化训练和优化各种机器学习模型,包括渐变增强决策树 (LightGBM,XGBoost),使用贝叶斯优化.
- 通过一项前性研究与内部可溶性实验验验证模型性能.
主要成果:
- 性能最好的模型在持久套件上的LogS实现了0.33的平均绝对误差 (MAE).
- 前性验证证实了在不同温度的二元溶剂混合物中精确的溶解性预测 (LogS的MAE<0.5).
- 模型对具有与训练数据集中相似特征的药物表现出特别高的准确性.
结论:
- 在全面数据集上训练的机器学习模型可以准确地预测各种溶剂系统和温度范围中的药物溶解度.
- 这种方法为实验测量提供了有价值的替代方案,加速了药物发现和配方.
- 数据集和代码的开放可用性将促进预测可溶性建模的进一步研究和开发.
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