机器学习辅助预测工具的初步开发,用于阿里碳化合物受体激活器
Paulina Anna Wojtyło1, Natalia Łapińska2, Lucia Bellagamba1
1Department of Pharmaceutical Sciences, University of Perugia, via del Liceo 1, 06123 Perugia, Italy.
Pharmaceutics
|November 27, 2024
概括
开发了定量结构-活性关系 (QSAR) 模型,以预测基碳化合物受体 (AhR) 活性. 这些模型有助于理解联体结构如何影响药物发现的AhR调制.
科学领域:
- 药用化学 医学化学
- 计算化学计算化学
- 药理学 药理学是指药理学的学科.
背景情况:
- 基碳化合物受体 (AhR) 对于免疫和代谢功能至关重要.
- 设计强大的AhR调节器是具有挑战性的,因为连体的多样性.
- 药物发现需要新的工具,目标是AhR.
研究的目的:
- 开发和比较定量结构-活动关系 (QSAR) 模型来预测AhR活动.
- 为AhR调制确定最有效的QSAR建模方法.
- 为未来针对AhR的药物设计提供基础.
主要方法:
- 结合了ChEMBL和WIPO数据库的978个分子,具有EC50值.
- 使用mljar平台开发分类和回归QSAR模型.
- 采用十倍交叉验证和SHAP用于模型解释.
主要成果:
- 分类模型实现了0.760准确度和0.789F1得分.
- 回归模型的结果是RMSE为5444和R2为0.208.
- 使用最佳分类模型开发了一个在线AhR网络应用程序.
结论:
- QSAR模型可以预测AhR活动,有助于理解结构-活动关系.
- 开发的Web应用程序作为研究人员的实用工具.
- 这些发现支持进一步开发用于AhR配体设计的QSAR模型.
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