Resampling combined with stacking learning for prediction of blood-brain barrier permeability of compounds.
10.7507/1001-5515.202210067
- Author:
Qing SU
1
;
Ganyao XIAO
1
;
Wei ZHOU
2
;
Zhiyun DU
2
Author Information
1. School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, P. R. China.
2. School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, Guangzhou 510006, P. R. China.
- Publication Type:Journal Article
- Keywords:
Blood-brain barrier permeability;
Blood-brain barrier positive quantification intervals;
Machine learning;
Resampling algorithms;
Stacking learning algorithm
- MeSH:
Blood-Brain Barrier;
Algorithms;
Area Under Curve;
Databases, Factual;
Permeability
- From:
Journal of Biomedical Engineering
2023;40(4):753-761
- CountryChina
- Language:Chinese
-
Abstract:
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.
