Prediction of Drug Permeability to the Blood-Brain Barrier using Deep Learning

The blood–brain barrier plays a crucial role in regulating the passage of 98% of the compounds that enter the central nervous system (CNS). Compounds with high permeability must be identified to enable the synthesis of brain medications for the treatment of various brain diseases, such as Parkinson’s, Alzheimer’s, and brain tumors. Throughout the years, several models have been developed to solve this problem and have achieved acceptable accuracy scores in predicting compounds that penetrate the blood–brain barrier. However, predicting compounds with “low” permeability has been a challenging task. In this study, we present a deep learning (DL) classification model to predict blood–brain barrier permeability. The proposed model addresses the fundamental issues presented in former models: high dimensionality, class imbalances, and low specificity scores. We address these issues to enhance the high-dimensional, imbalanced dataset before developing the classification model: the imbalanced dataset is addressed using oversampling techniques and the high dimensionality using a non-linear dimensionality reduction technique known as kernel principal component analysis (KPCA). This technique transforms the high-dimensional dataset into a low-dimensional Euclidean space while retaining invaluable information. For the classification task, we developed an enhanced feed-forward deep learning model and a convolutional neural network model. In terms of specificity scores (i.e., predicting compounds with low permeability), the results obtained by the enhanced feed-forward deep learning model outperformed those obtained by other models in the literature that were developed using the same technique. In addition, the proposed convolutional neural network model surpassed models used in other studies in multiple accuracy measures, including overall accuracy and specificity. The proposed approach solves the problem inevitably faced with obtaining low specificity resulting in high false positive rate.

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A machine learning model for the prediction of drug permeability across the Blood-Brain Barrier: a comparative approach

10.21203/rs.3.rs-29117/v1 ◽

2020 ◽

Author(s):

Ralph Saber ◽

Rami Mhanna ◽

Sandy Rihana

Keyword(s):

Genetic Algorithm ◽

Genetic Algorithms ◽

Feature Selection ◽

Blood Brain Barrier ◽

In Silico ◽

Molecular Descriptors ◽

Brain Barrier ◽

Drug Permeability ◽

Blood Brain ◽

Sequential Feature Selection

Abstract Background: Drug permeability across the blood-brain barrier (BBB) is a critical challenge for successful drug discovery which has led to multiple efforts to develop in silico predictive models. Most of the in silico models are based on the molecular descriptors of the drugs. In this work, we compare the ability of sequential feature selection and genetic algorithms in selecting the most relevant descriptors and hence enhancing the permeability prediction accuracy.Methods: Five different classifiers were initially trained on a dataset using eight molecular descriptors. Then, sequential feature selection and genetic algorithms were performed separately and the same classifiers were trained using the descriptors chosen by each algorithm.Results: The highest overall accuracy obtained without feature selection was 94.98%. This accuracy increased with sequential feature selection and genetic algorithms on multiple classifiers. However, the highest accuracy (96.23%) was obtained after performing genetic algorithm on the feature vector. Moreover, genetic algorithm with a fitness function based on the performance of a support vector machine led to an increase in the accuracy of all the tested classifiers unlike sequential feature selection.Conclusions: The findings show that genetic algorithm is a more robust approach than sequential feature selection in choosing the most relevant molecular descriptors involved in the permeability across the blood-brain barrier. The results also highlight the importance of the polar surface area of drugs in crossing the BBB.

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