对分子皮肤透性的预测建模:使用各种AI算法研究FDA批准的药物透性
Rami M Abdallah1, Hisham E Hasan1, Ahmad Hammad2
1Department of Pharmaceutical Sciences, Faculty of Pharmacy, Zarqa University, Zarqa, Jordan.
PLOS digital health
|April 3, 2024
概括
准确的皮肤透性预测对于通过皮肤递送药物 (TDD) 至关重要. 先进的机器学习模型,特别是梯度增强,在预测药物LogKp值方面表现出卓越的性能,有助于药物设计.
科学领域:
- 药理学和化学信息学
- 计算机化药物发现技术
背景情况:
- 通过皮肤递送药物 (TDD) 提供了方便,避免了第一通代谢.
- 准确预测皮肤透性对于成功的TDD至关重要.
研究的目的:
- 通过精确的皮肤透性预测来增强通过皮肤递送药物 (TDD).
- 确定影响皮肤透性的关键分子描述因素.
- 为了分析FDA批准的药物的透性特征.
主要方法:
- 使用了441个分子的数据集,具有不同的LogKp值.
- 计算了145个相关的分子描述符.
- 在回归分析中使用并比较各种机器学习模型 (MLR,RF,XGBoost,CatBoost,LGBM,ANN).
- 对FDA批准的药物进行了集群分析,并调查了ATC药物类别的透性.
主要成果:
- 渐变增强模型 (LGBM,XGBoost) 显示了皮肤透性的卓越预测准确性.
- 确定了疏水性,键捐赠者/接受者和TPSA作为关键描述符.
- 集群分析显示,在FDA批准的药物中,具有不同透性的四个不同的组.
- 在83个ATC药物类别中观察到皮肤透性的显著差异.
结论:
- 准确的皮肤透性预测对于TDD至关重要,非线性机器学习模型显示出高效率.
- 关键的分子描述符和药物集群为透性变化提供了洞察力.
- 这些发现为药物设计,配方和优先级制定提供了宝贵的指导,有可能降低实验成本.
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