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. 2021 Oct 12;22(20):10995.
doi: 10.3390/ijms222010995.

Quantum Artificial Neural Network Approach to Derive a Highly Predictive 3D-QSAR Model for Blood-Brain Barrier Passage

Taeho Kim  1 , Byoung Hoon You  2 , Songhee Han  2 , Ho Chul Shin  2 , Kee-Choo Chung  1 , Hwangseo Park  1
Affiliations
Free PMC article

Quantum Artificial Neural Network Approach to Derive a Highly Predictive 3D-QSAR Model for Blood-Brain Barrier Passage

Taeho Kim et al. Int J Mol Sci. .
Free PMC article

Abstract

A successful passage of the blood-brain barrier (BBB) is an essential prerequisite for the drug molecules designed to act on the central nervous system. The logarithm of blood-brain partitioning (LogBB) has served as an effective index of molecular BBB permeability. Using the three-dimensional (3D) distribution of the molecular electrostatic potential (ESP) as the numerical descriptor, a quantitative structure-activity relationship (QSAR) model termed AlphaQ was derived to predict the molecular LogBB values. To obtain the optimal atomic coordinates of the molecules under investigation, the pairwise 3D structural alignments were conducted in such a way to maximize the quantum mechanical cross correlation between the template and a target molecule. This alignment method has the advantage over the conventional atom-by-atom matching protocol in that the structurally diverse molecules can be analyzed as rigorously as the chemical derivatives with the same scaffold. The inaccuracy problem in the 3D structural alignment was alleviated in a large part by categorizing the molecules into the eight subsets according to the molecular weight. By applying the artificial neural network algorithm to associate the fully quantum mechanical ESP descriptors with the extensive experimental LogBB data, a highly predictive 3D-QSAR model was derived for each molecular subset with a squared correlation coefficient larger than 0.8. Due to the simplicity in model building and the high predictability, AlphaQ is anticipated to serve as an effective computational screening tool for molecular BBB permeability.

Keywords: 3D-QSAR; artificial neural network; blood–brain barrier; molecular ESP descriptor; structural alignment.

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1
Schematic diagram of N × M × 1 neural network to derive a 3D-QSAR model for LogBB prediction. Columns I, H, and O indicate the input, hidden, and output layers, respectively. Neurons in the three layers are mutually related with the weighting matrices wki and wij.
Figure 2
Figure 2
The results of 3D structural alignments among the molecules in (a) Subset 1, (b) Subset 2, (c) Subset 3, (d) Subset 4, (e) Subset 5, (f) Subset 6, (g) Subset 7, and (h) Subset 8. Carbon, hydrogen, nitrogen, and oxygen are indicated in green, gray, blue, and red, respectively.
Figure 2
Figure 2
The results of 3D structural alignments among the molecules in (a) Subset 1, (b) Subset 2, (c) Subset 3, (d) Subset 4, (e) Subset 5, (f) Subset 6, (g) Subset 7, and (h) Subset 8. Carbon, hydrogen, nitrogen, and oxygen are indicated in green, gray, blue, and red, respectively.
Figure 3
Figure 3
Linear correlation diagram between the experimental and calculated LogBB values for (a) Subset 1, (b) Subset 2, (c) Subset 3, (d) Subset 4, (e) Subset 5, (f) Subset 6, (g) Subset 7, and (h) Subset 8. Indicated in black and red circles are the molecules in the training and test sets, respectively.
Figure 3
Figure 3
Linear correlation diagram between the experimental and calculated LogBB values for (a) Subset 1, (b) Subset 2, (c) Subset 3, (d) Subset 4, (e) Subset 5, (f) Subset 6, (g) Subset 7, and (h) Subset 8. Indicated in black and red circles are the molecules in the training and test sets, respectively.
Figure 4
Figure 4
Linear diagram showing correlation between the experimental and calculated LogBB values for a total of 406 molecules in the whole dataset. Indicated in black and red circles are the molecules in the training and test sets, respectively.
Figure 5
Figure 5
(a) Molecular structure of 1 and (b) schematic representation of the tautomeric transformation of 2. Relative electronic energies (ΔErel) calculated at the RHF/6-31G** level of theory are given in kcal/mol. Indicated in red are the atoms involved in the tautomeric change.
Figure 6
Figure 6
William plots of the LogBB predictions for (a) Subset 1, (b) Subset 2, (c) Subset 3, (d) Subset 4, (e) Subset 5, (f) Subset 6, (g) Subset 7, and (h) Subset 8. Indicated in black circles and red triangles are the molecules in the training and test sets, respectively. The dashed lines indicate the boundaries of the applicability realm. Warning leverage values (h*’s) are indicated in numbers instead of vertical lines because they reside too far from the data points.
Figure 6
Figure 6
William plots of the LogBB predictions for (a) Subset 1, (b) Subset 2, (c) Subset 3, (d) Subset 4, (e) Subset 5, (f) Subset 6, (g) Subset 7, and (h) Subset 8. Indicated in black circles and red triangles are the molecules in the training and test sets, respectively. The dashed lines indicate the boundaries of the applicability realm. Warning leverage values (h*’s) are indicated in numbers instead of vertical lines because they reside too far from the data points.
Figure 7
Figure 7
Linear diagram showing correlation between the experimental and calculated LogBB values of the ten drug candidate molecules in comparison with 1 and 2.
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