使用机器学习和分子描述器进行先进的QSPR模拟,用于NSAID分析
W Eltayeb Ahmed1, Muhammad Farhan Hanif2, Muhammad Kamran Siddiqui3
1Department of Mathematics and Statistics, College of Science, Imam Muhammad Ibn Saud Islamic University (IMSIU), PO Box 90950, Riyadh, Saudi Arabia.
Scientific reports
|July 20, 2025
概括
这项研究引入了一种人工神经网络 (ANN) 模型,使用分子描述器预测药物特性. 该ANN模型显示了高精度,有助于药物设计和虚拟查.
科学领域:
- 计算化学是一种计算化学.
- 药品化学 药品化学 是一个
- 人工智能在药物发现中的作用
背景情况:
- 物理化学性质对于药物的有效性和安全性至关重要.
- 通过计算预测这些特性可以加速药物开发.
- 传统的方法可能是耗时和资源密集的.
研究的目的:
- 开发和验证一个人工神经网络 (ANN) 模型.
- 预测抗炎药物的关键物理化学性质.
- 用拓索引作为分子描述符用于属性预测.
主要方法:
- 从化学结构中计算分子描述符.
- 将规范描述符输入到ANN模型中.
- 使用抗炎药物的数据集训练和测试ANN模型.
主要成果:
- 该ANN模型实现了0.94.2的R2值.
- 该模型在测试组件上显示了0.0087的低平均平方误差 (MSE).
- 观察到物理化学性质的高预测精度.
结论:
- 开发的ANN模型显示出出色的预测性能.
- 机器学习促进了高效的虚拟查和合理的药物设计.
- 这种方法有助于准确预测药物特性.
关键词:
计算化学是一种计算化学.药物设计 药物设计图形理论是指图形的理论.机器学习是机器学习.分子建模分子建模无抗炎药 (NSAIDs) 是一种无抗炎药.预测分析是一种预测分析.这就是QSPR.结构与活动的关系.拓索引 拓索引 拓索引更多相关视频
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