使用基于度的拓指数和机器学习算法的抗高血压药物的物理化学性质的预测建模
Saood Azam1, Sadia Noureen1, Tasra Yaqoob1
1Department of Mathematics, Faculty of Science, University of Gujrat, Gujrat, Pakistan.
Journal of molecular graphics & modelling
|October 11, 2025
概括
新的分子图形理论指数准确地预测药物特性,如沸点和LogP. 这种化学信息学方法通过提供具有成本效益的,可解释的分子行为见解来加速药物设计.
科学领域:
- 化学信息学 化学信息学
- 数学化学 数学化学
- 药物发现 药物发现 药物发现
背景情况:
- 在化学信息学中,对物理化学性质的定量预测至关重要.
- 分子图理论为理解化学结构提供了一个强大的框架.
- 开发准确的药物特性预测模型对于合理的药物设计至关重要.
研究的目的:
- 引入新的基于度的拓指数,用于建模物理化学性质.
- 为抗高血压药物开发一个定量结构与属性关系 (QSPR) 框架.
- 评估这些指数和模型的预测性能和可解释性.
主要方法:
- 使用的以度为基础的拓指数 (ABC,ABS,MMR,SDD,SI,SO,SO3,SO4).
- 开发使用线性回归,随机森林和XGBoost的QSPR模型.
- 通过MAE,MSE,RMSE和R2评估模型性能;使用基尼,排列和SHAP分析特征重要性.
主要成果:
- 拟议的指数与沸点,点,临界体积,LogP,摩尔折射率和CLogP有很强的相关性.
- 在所有预测属性中,XGBoost模型实现了R2>0.99.
- 基于程度的指数有效地捕捉了结构特征,并提供了可解释的见解.
结论:
- 基于度的拓指数对于预测药物的物理化学性质是有效的.
- 开发的QSPR框架,特别是XGBoost,提供了高的预测准确性和可解释性.
- 这些图形理论描述符可以作为实验分析的经济有效替代品,加速药物发现工作流程.
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