Towards Deep Neural Network Models for the Prediction of the Blood-Brain Barrier Permeability for Diverse Organic Compounds

Abstract

Permeation through the blood-brain barrier (BBB) is among the most important processes controlling the pharmacokinetic properties of drugs and other bioactive compounds. Using the fragmental (substructural) descriptors representing the occurrence number of various substructures, as well as the artificial neural network approach and the double cross-validation procedure, we have developed a predictive in silico LogBB model based on an extensive and verified dataset (529 compounds), which is applicable to diverse drugs and drug-like compounds. The model has good predictivity parameters (Q2=0.815, RMSEcv=0.318) that are similar to or better than those of the most reliable models available in the literature. Larger datasets, and perhaps more sophisticated network architectures, are required to realize the full potential of deep neural networks. The analysis of fragment contributions reveals patterns of influence consistent with the known concepts of structural characteristics that affect the BBB permeability of organic compounds. The external validation of the model confirms good agreement between the predicted and experimental LogBB values for most of the compounds. The model enables the evaluation and optimization of the BBB permeability of potential neuroactive agents and other drug compounds.

Keywords: ADMET; blood–brain barrier; distribution; permeability; pharmacokinetics; prediction.

Figure 1

Distribution of the LogBB values in the modeling dataset.

Figure 2

General modeling workflow.

Figure 3

Comparison of the experimental LogBB values and the values predicted during double cross-validation.

Figure 4

Fragments having the strongest negative (a) and positive (b) effect on the predicted value of BBB permeability of compounds. Fragments are highlighted in blue for negative and red for positive. Asterisk denotes any atom type; standalone atom symbol means any atom subtype compatible with the specified bond pattern. For more complex fragments, the examples of their occurrence in a structure are shown.

Figure 5

Comparison of the experimental and predicted LogBB values for the external validation dataset. The compounds overlapping with our training set are shown as blue diamonds and the non-overlapping compounds are shown as red circles.

Figure 6

Correlation between the ensemble standard deviations of predicted LogBB values and the resulting absolute prediction errors for the external validation dataset compounds.