PERMEABILITY FORECAST OF BLOOD–BRAIN BARRIER (BBB) IN THE CENTRAL NERVOUS SYSTEM USING ML
DOI:
https://doi.org/10.17605/OSF.IO/87MBEKeywords:
blood brain barrier, CNS , SVC, XGBoost, classifiers, Neural network.Abstract
98 percent of the substances that enter the central nervous system are controlled by the blood-brain barrier (BBB) (CNS). Compounds with high permeability must be discovered to enable the production of brain medications for the treatment of various brain conditions including Parkinson's, Alzheimer's, and brain malignancies. To address this issue, a number of models have been developed over time, with respectable accuracy ratings in forecasting substances that penetrate the blood-brain barrier. Forecasting molecules with "poor" permeability, however, has proven challenging. Several machine learning classifiers, including Principal Component Analysis PCA, Neural Network SVC, and XGBoost, have been compared using Molecule Net in this research study and are shown in the outcome section. Several concerns should be addressed before creating the classification model in order to enhance the high-dimensional, unbalanced dataset: the Oversampling methods are used to handle the unbalanced dataset, while kernel principal component analysis, a non-linear dimensionality reduction method, is used to solve the excessive dimensionality. The accuracy of a 500-epoch neural network is about 98%, which is far higher than that of earlier studies.
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