A Computational Prediction Model of Blood-Brain Barrier Penetration Based on Machine Learning Approaches

Abstract

Within the field of medical sciences, addressing brain illnesses such as Alzheimer's disease, Parkinson's disease, and brain tumors poses significant difficulties. Despite thorough investigation, the search for truly successful neurotherapies continues to be challenging to achieve. The blood-brain barrier (BBB), which is currently a major area of research, restricts the passage of medicinal substances into the central nervous system (CNS). It is crucial in the field of neuroscience to create drugs that can effectively cross the blood-brain barrier (BBB) and treat cognitive disorders. The objective of this study is to improve the accuracy of machine learning models in predicting BBB permeability, which is a critical factor in medication development. In recent times, a range of machine learning models such as Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Logistic Regression (LR), Artificial Neural Networks (ANN), and Random Forests (RF) have been utilized for BBB. By employing descriptors of varying dimensions (1D, 2D, or 3D), these models demonstrate the potential to make precise predictions. However, the majority of these studies are biased to the nature of datasets. To accomplish our objective, we utilized three BBB datasets for training and testing our model. The Random Forest (RF) model has shown exceptional performance when used on larger datasets and extensive feature sets. The RF model attained an overall accuracy of 90.36% with 10-fold cross-validation. Additionally, it earned an AUC of 0.96, a sensitivity of 77.73%, and a specificity of 94.74%. The assessment of an external dataset resulted in an accuracy rate of 91.89%, an AUC value of 0.94, a sensitivity rate of 91.43%, and a specificity rate of 92.31%.