基于机器学习的口服生物可用性预测模型的构建,用于评估分子修饰
Qi Yang1, Lili Fan1, Erwei Hao2
1School of Pharmacy, Guangxi University of Chinese Medicine, Nanning 530200, China.
机器学习通过分析ADME属性,准确地预测药物的口服生物可用性 (OB). 修改柏柏林和阿提诺洛尔结构增强了他们的OB,指导未来的药物设计以改善药物输送.
科学领域:
- 计算化学的计算化学
- 药理动力学 药理动力学
- 机器学习在药物发现中的作用
背景情况:
- 口服生物可用性 (OB) 是药物疗效的一个关键因素.
- 了解吸收,分布,新陈代谢和分泌 (ADME) 特性对OB的影响对于药物开发至关重要.
- 预测建模可以加快药物候选人的识别,并有有利的OB.
研究的目的:
- 研究ADME特征对药物的口服生物利用性的影响.
- 开发和验证用于预测OB的机器学习模型.
- 应用该模型来优化柏柏林和阿诺洛尔衍生物的OB.
主要方法:
- 建立了药物OB数据库 (386种药物),并收集了ADME数据.
- 使用摩根指纹作为分子描述符的机器学习算法 (随机森林,XGBoost,CatBoost,LightGBM).
- 通过单基和二基替代修改了柏柏林和阿提诺洛尔结构,以预测OB变化.
主要成果:
- 确定较小的分子量和更多可旋转的键 (≤10) 与较高的OB相关.
- 随机森林模型在OB预测中表现出卓越的性能.
- 结构修改显著改善了Berberine和Atenolol的口服生物可用性.
结论:
- 机器学习模型,特别是随机森林,可以准确地预测药物OB.
- 建立的数据库和模型为指导药物设计提供了有价值的工具.
- 化学修饰,特别是替代,可以有效地提高药物分子的口服生物可用性.
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