In Silico Prediction of Blood–Brain Barrier Permeability of Compounds by Machine Learning and Resampling Methods

Abstract

The blood-brain barrier (BBB) as a part of absorption protects the central nervous system by separating the brain tissue from the bloodstream. In recent years, BBB permeability has become a critical issue in chemical ADMET prediction, but almost all models were built using imbalanced data sets, which caused a high false-positive rate. Therefore, we tried to solve the problem of biased data sets and built a reliable classification model with 2358 compounds. Machine learning and resampling methods were used simultaneously for the refinement of models with both 2 D molecular descriptors and molecular fingerprints to represent the chemicals. Through a series of evaluation, we realized that resampling methods such as Synthetic Minority Oversampling Technique (SMOTE) and SMOTE+edited nearest neighbor could effectively solve the problem of imbalanced data sets and that MACCS fingerprint combined with support vector machine performed the best. After the final construction of a consensus model, the overall accuracy rate was increased to 0.966 for the final external data set. Also, the accuracy rate of the model for the test set was 0.919, with an excellent balanced capacity of 0.925 (sensitivity) to predict BBB-positive compounds and of 0.899 (specificity) to predict BBB-negative compounds. Compared with other BBB classification models, our models reduced the rate of false positives and were more robust in prediction of BBB-positive as well as BBB-negative compounds, which would be quite helpful in early drug discovery.

Predicting the cytotoxicity of chemicals using ensemble learning methods and molecular fingerprints

“…To solve this problem, the above‐mentioned researchers have attempted many approaches, such as combining data, applying various thresholds for dividing data class, and using different oversampling ratios. The results have clearly demonstrated that these approaches are not suitable for such research (Wang et al, ).…”

ADME Profiling in Drug Discovery and a New Path Paved on Silica

“…The work of Shen et al developed SVM models using 1593 compounds (1283 BBB + and 310 BBB − ) by using different pattern selection methods and obtained the overall accuracy of 98.2% [80]. Both methods have the limitation of unbalanced datasets (where the number of BBB+ is higher than the BBB− within the training set), which was addressed on the work of Wang et al by using resampling methods coupled with the machine-learning techniques, to achieve accuracy rates of 0.919 in external test data [81]. Wang and collaborators compiled a dataset of 439 unique molecules, which were employed to generate a diverse set of QSAR models and consensus (R 2 = 0.504 for external dataset prediction).…”

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

“…While such models assign speci c logBB/PR values for each drug, binary models have so far reached a higher prediction accuracy and provide a preliminary insight regarding the behavior of candidate drugs which is su cient in early drug discovery stages. Predominantly, binarization of drug permeability across the BBB is performed by setting empirical thresholds to logBB values [5][6][7][8][9]. However, S. Kunwittaya et al [6] have shown that varying logBB thresholds lead to a difference in the prediction accuracy.…”

“…In this context, different types of classi ers were trained in the literature including Support Vector Machines (SVM) [6,8,11,12], Linear Discriminant Analysis (LDA) [13], Arti cial Neural Networks (ANN) [6] and Multi-Layer Perceptron (MLP) [8,9], k-Nearest Neighbors (k-NN) [8], Decision Trees (DT) [6,7] and Random Forests (RF) [5,8,9]. Other studies apply consensus models, by training and combining multiple classi ers [8,9]. While consensus models mitigate the over tting problem of single classi ers, they naturally require high computational power, especially when dealing with high dimensional data.…”

“…The features used to train these classi ers are often molecular descriptors which are chemical properties describing the drugs [3]. Some studies also add the ngerprints of the molecules in order to reach better prediction [8,9,12]. On the other hand, novel approaches apply the drug side effects and indications for BBB penetration prediction [14].…”