Abstract
Blood-Brain-Barrier (BBB) is a strict permeability barrier for maintaining the Central Nervous System (CNS) homeostasis. One of the most important conditions to judge a CNS drug is to figure out whether it has BBB permeability or not. In the past 20 years, the existing prediction approaches are usually based on the data of the physical characteristics and chemical structure of drugs. However, these methods are usually only applicable to small molecule compounds based on passive diffusion through BBB. To deal this problem, one of the most famous methods is multi-core SVM method, which is based on clinical phenotypes about Drug Side Effects and Drug Indications to predict drug penetration of BBB. This paper proposed a Deep Learning method to predict the Blood-Brain-Barrier permeability based on the clinical phenotypes data. The validation result on three datasets proved that Deep Learning method achieves better performance than the other existing methods. The average accuracy of our method reaches 0.97, AUC reaches 0.98, and the F1 score is 0.92. The results proved that Deep Learning methods can significantly improve the prediction accuracy of drug BBB permeability and it can help researchers to reduce clinical trials and find new CNS drugs.
Highlights
At present, the most widely used predictive methods are physical and chemical approaches, which mainly include topological polar surface area, hydrogen bond donors and acceptors, acidic and basic atomic number, ionization potential, silico methods and so on[14,15,16,17,18,19]
Compared with the existing methods, our method has the following advantages: (i) The average prediction accuracy of experiments with three datasets already achieved 0.97, the average AUC is 0.98, F1 score is 0.91. It significantly performed better than the multi-core Support Vector Machine (SVM) method, Decision Tree and the K-Nearest Neighbor (KNN) method, which can help researchers save experiment time and discover new drugs. (ii) The accuracy, AUC and F1 scores of SVM methods with different datasets are fluctuated greatly, but the accuracy of the Deep Learning method, which proposed in this paper, is very stable and adaptable. (iii) The Deep Learning method can be applied in both simple diffusions of small molecule compounds and other compounds that diffuse through complex pathways
This paper proposes the Deep Learning method to predict the permeability of Blood-Brain-Barrier based on clinical phenotype
Summary
The most widely used predictive methods are physical and chemical approaches, which mainly include topological polar surface area, hydrogen bond donors and acceptors, acidic and basic atomic number, ionization potential, silico methods and so on[14,15,16,17,18,19]. This paper proposes a Deep Learning method in predicting the drug permeability of BBB which is based on clinical features. Compared with the existing methods, our method has the following advantages: (i) The average prediction accuracy of experiments with three datasets already achieved 0.97, the average AUC is 0.98, F1 score is 0.91 It significantly performed better than the multi-core SVM method, Decision Tree and the KNN method, which can help researchers save experiment time and discover new drugs. This paper proposes a Deep Learning method in drug prediction of BBB permeability which is based on the clinical features and our results are better than the previous researches’ results like multi-core SVM methods.
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