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基于机器学习的非共价布鲁顿氨酸激酶抑制剂的分类模型:预测能力和可解释性

Guo Li¹, Jiaxuan Li¹, Yujia Tian¹

¹State Key Laboratory of Chemical Resource Engineering, Department of Pharmaceutical Engineering, Beijing University of Chemical Technology, Beijing, People's Republic of China.

Molecular diversity

|July 21, 2023

概括

机器学习模型准确地预测了非共价布鲁顿氨酸激酶 (BTK) 抑制剂的生物活性. 可解释的人工智能方法,如SHAP,提供洞察力,帮助新药设计.

科学领域:

计算化学是一种计算化学.
药物发现药物发现
生物信息学是一种生物信息学.

背景情况:

布鲁顿氨酸激酶 (BTK) 是治疗各种癌症和自身免疫性疾病的关键标.
开发选择性非共价BTK抑制剂需要有效预测其生物活性.
机器学习为预测分子特性和指导药物设计提供了一种强大的方法.

研究的目的:

开发和验证机器学习模型,用于预测非共价BTK抑制剂的生物活性.
使用可解释的AI技术,为模型预测提供可解释的解释.
促进新型BTK抑制剂的设计,以提高有效性和选择性.

主要方法:

从Reaxys和ChEMBL数据库收集了3895种非共价BTK抑制剂的数据集.
利用MACCS和摩根分子指纹进行特征表示.
训练并评估传统的 (DT,RF,SVM,XGBoost) 和深度学习 (DNN) 分类模型.
应用SHAP用于模型可解释性和K-means/层次聚类用于结构可视化.

主要成果:

最好的模型 (XGBoost与MACCS指纹) 实现了94.1%的准确性和0.75.7的MCC.
SHAP分析成功分解了预测,突出了促进生物活性的关键分子特征.
聚类分析揭示了不同的抑制剂组,与晶体结构相互作用一致.

结论:

机器学习模型显示了对非共价BTK抑制剂生物活性的高预测性能.
可解释的人工智能 (SHAP) 提高了模型的透明度,并有助于理解结构-活动关系.
开发的模型和见解对于加速BTK抑制剂的发现和优化非常有价值.

关键词:

分类模型的分类模型.机器学习方法机器学习方法非共价布鲁顿斯氨酸激酶 (BTK) 抑制剂这就是 SHAP SHAP 的意思.结构聚类结构聚类.

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