Resampling combined with stacking learning for prediction of blood-brain barrier permeability of compounds

如何改善中枢神经系统（CNS）药物的血脑屏障（BBB）透过率，是CNS药物研发中面临的重要挑战。相较于传统的药代动力学性质测试，机器学习技术已被证实可以有效、低成本地预测CNS药物的BBB透过率。本文提出一种基于均衡化堆叠学习（SL）的BBB透过率预测模型（BSL-B3PP），首先分别从药物化学背景角度以及机器学习角度，筛选出对BBB透过率有关键影响的特征集，并总结可穿透BBB（BBB+）量化区间；然后融合重采样方法与堆叠学习算法，进行CNS药物BBB透过率预测。BSL-B3PP模型基于较大规模的BBB数据集（B3DB）构建，经实验验证，曲线下面积（AUC）达97.8%，马修斯相关系数（MCC）为85.5%。研究结果说明，本模型具有较好的BBB透过率预测能力，尤其对于不能穿透BBB的药物也能保持较高的预测准确度，有助于降低CNS药物研发成本，加快CNS药物研发进程。.; It is a significant challenge to improve the blood-brain barrier (BBB) permeability of central nervous system (CNS) drugs in their development. Compared with traditional pharmacokinetic property tests, machine learning techniques have been proven to effectively and cost-effectively predict the BBB permeability of CNS drugs. In this study, we introduce a high-performance BBB permeability prediction model named balanced-stacking-learning based BBB permeability predictor(BSL-B3PP). Firstly, we screen out the feature set that has a strong influence on BBB permeability from the perspective of medicinal chemistry background and machine learning respectively, and summarize the BBB positive(BBB+) quantification intervals. Then, a combination of resampling algorithms and stacking learning(SL) algorithm is used for predicting the BBB permeability of CNS drugs. The BSL-B3PP model is constructed based on a large-scale BBB database (B3DB). Experimental validation shows an area under curve (AUC) of 97.8% and a Matthews correlation coefficient (MCC) of 85.5%. This model demonstrates promising BBB permeability prediction capability, particularly for drugs that cannot penetrate the BBB, which helps reduce CNS drug development costs and accelerate the CNS drug development process.