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

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.