一种利用分子描述符预测血脑屏障通透性的可解释机器学习框架
An Explainable Machine Learning Framework for Predicting Blood-Brain Barrier Permeability Using Molecular Descriptors
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中文总结 AI 辅助
本研究利用分子描述符和四种机器学习算法构建可解释框架预测血脑屏障通透性，其中优化XGBoost性能最佳，并识别出TPSA、HBD和LogP为关键影响因素。
中文摘要 AI 辅助
血脑屏障（BBB）通透性是中枢神经系统治疗药物开发中的关键决定因素，因为它直接影响候选药物到达脑内靶位点的能力。在本研究中，开发了一种可解释的机器学习框架，利用RDKit化学信息学工具包从MoleculeNet BBBP数据集中生成的分子描述符来预测血脑屏障通透性。从2,039种化合物中提取的十五种理化描述符被用于训练四种监督式机器学习算法，包括逻辑回归、支持向量机（SVM）、随机森林和极端梯度提升（XGBoost）。使用GridSearchCV进行超参数优化，同时使用SHapley Additive exPlanations（SHAP）进行模型可解释性研究。在所评估的模型中，优化后的XGBoost分类器取得了最佳的预测性能，准确率为88.97%，精确率为88.92%，召回率为97.76%，F1分数为93.13%，ROC-AUC为0.9282。分层五折交叉验证进一步证明了所提出模型的稳健性，平均ROC-AUC为0.8982 ± 0.0130。特征重要性和SHAP分析一致地识别出TPSA、HBD和LogP是控制血脑屏障通透性预测的最具影响力的分子描述符。总体而言，所提出的框架为血脑屏障通透性预测提供了一种准确、可解释且计算高效的方法，并可作为中枢神经系统候选药物早期筛选的宝贵工具。
英文摘要
Blood-brain barrier (BBB) permeability is a critical determinant in the development of central nervous system therapeutics because it directly influences the ability of drug candidates to reach their target sites within the brain. In this study, an explainable machine learning framework was developed to predict BBB permeability using molecular descriptors generated from the MoleculeNet BBBP dataset with the RDKit cheminformatics toolkit. Fifteen physicochemical descriptors extracted from 2,039 compounds were used to train four supervised machine learning algorithms, including Logistic Regression, Support Vector Machine (SVM), Random Forest, and Extreme Gradient Boosting (XGBoost). Hyperparameter optimization was performed using GridSearchCV, while model interpretability was investigated using SHapley Additive exPlanations (SHAP). Among the evaluated models, the optimized XGBoost classifier achieved the best predictive performance, with an accuracy of 88.97%, a precision of 88.92%, a recall of 97.76%, an F1-score of 93.13%, and a ROC-AUC of 0.9282. Stratified five-fold cross-validation further demonstrated the robustness of the proposed model, yielding a mean ROC-AUC of 0.8982 +/- 0.0130. Feature importance and SHAP analyses consistently identified TPSA, HBD, and LogP as the most influential molecular descriptors governing BBB permeability prediction. Overall, the proposed framework provides an accurate, interpretable, and computationally efficient approach for BBB permeability prediction and may serve as a valuable tool for the early-stage screening of CNS drug candidates.
发表机构
- Sharif University of Technology（谢里夫理工大学）
机构由 AI 辅助整理，请以论文原文为准。
