Quantum Artificial Neural Network Approach to Derive a Highly Predictive 3D-QSAR Model for Blood–Brain Barrier Passage
Abstract
:1. Introduction
2. Computational and Experimental Methods
2.1. Preparation of the Experimental Dataset to Derive and Validate the LogBB Prediction Model
2.2. Calculations of the 3D Molecular Descriptors
2.3. Pairwise 3D Molecular Alignments
2.4. Determination of the Template for Multiple Pairwise Alignments
2.5. Derivation of the LogBB Prediction Models with ANN Algorithm
2.6. Experimental Determination of Molecular LogBB Values Using Mouse Models
3. Results and Discussion
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
BBB | blood–brain barrier |
CNS | central nervous system |
LogBB | logarithm of the blood–brain partitioning |
QSAR | quantitative structure-activity relationship |
CoMFA | comparative molecular field analysis |
CoMSIA | comparative molecular similarity index analysis |
AI | artificial intelligence |
ESP | electrostatic potential |
MW | molecular weight |
amu | atomic mass unit |
PCA | principal component analysis |
ANN | artificial neural network |
GPU | graphics processing unit |
AUC | area under the time-versus-concentration curve |
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Molecular Subset | MW Range | LogBB Range | No. of Training-Set Molecules | No. of Test-Set Molecules |
---|---|---|---|---|
Subset 1 | 200–250 | −2.09–1.40 | 57 | 14 |
Subset 2 | 251–275 | −1.57–1.64 | 39 | 9 |
Subset 3 | 276–301 | −1.42–1.30 | 40 | 10 |
Subset 4 | 302–323 | −1.70–1.60 | 40 | 10 |
Subset 5 | 324–360 | −1.48–1.44 | 40 | 10 |
Subset 6 | 361–398 | −2.69–1.33 | 40 | 10 |
Subset 7 | 399–453 | −1.30–1.51 | 40 | 9 |
Subset 8 | 454–600 | −2.15–1.10 | 32 | 6 |
Subset 1 | Subset 2 | Subset 3 | Subset 4 | Subset 5 | Subset 6 | Subset 7 | Subset 8 | |
---|---|---|---|---|---|---|---|---|
R2train | 0.998 | 0.997 | 0.998 | 0.998 | 0.997 | 0.999 | 0.998 | 0.997 |
R2test | 0.592 | 0.785 | 0.601 | 0.748 | 0.656 | 0.749 | 0.692 | 0.703 |
r2pred | 0.559 | 0.694 | 0.597 | 0.714 | 0.655 | 0.659 | 0.663 | 0.658 |
Molecules | Chemical Formula | MW | Calculated LogBB | Experimental LogBB |
---|---|---|---|---|
1 | C15H10Cl2N2O2 | 321.2 | −0.16 | 0.44 |
2 | C10H12N4O3 | 236.2 | −0.77 | −1.30 |
carbamazepine | C15H12N2O | 236.3 | −0.04 | −0.14 |
3 | C17H17ClN8O | 384.8 | −0.02 | −0.25 |
4 | C22H25ClN6O3 | 456.9 | −0.67 | −0.39 |
5 | C23H27ClN6O3 | 471.0 | −0.24 | −0.44 |
6 | C25H32ClN7O2 | 498.0 | 0.26 | −0.09 |
7 | C19H21F4N5O3 | 443.4 | −0.21 | −0.07 |
8 | C25H26F3N5O3 | 501.5 | −0.74 | −0.21 |
9 | C24H20F3N5O3 | 483.5 | −2.07 | −1.70 |
10 | C28H31N7O3 | 513.6 | −0.25 | −0.38 |
11 | C17H23N5O2 | 329.4 | 0.46 | 0.54 |
12 | C24H31N3O2 | 393.5 | −0.73 | −0.38 |
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Kim, T.; You, B.H.; Han, S.; Shin, H.C.; Chung, K.-C.; Park, H. Quantum Artificial Neural Network Approach to Derive a Highly Predictive 3D-QSAR Model for Blood–Brain Barrier Passage. Int. J. Mol. Sci. 2021, 22, 10995. https://0-doi-org.brum.beds.ac.uk/10.3390/ijms222010995
Kim T, You BH, Han S, Shin HC, Chung K-C, Park H. Quantum Artificial Neural Network Approach to Derive a Highly Predictive 3D-QSAR Model for Blood–Brain Barrier Passage. International Journal of Molecular Sciences. 2021; 22(20):10995. https://0-doi-org.brum.beds.ac.uk/10.3390/ijms222010995
Chicago/Turabian StyleKim, Taeho, Byoung Hoon You, Songhee Han, Ho Chul Shin, Kee-Choo Chung, and Hwangseo Park. 2021. "Quantum Artificial Neural Network Approach to Derive a Highly Predictive 3D-QSAR Model for Blood–Brain Barrier Passage" International Journal of Molecular Sciences 22, no. 20: 10995. https://0-doi-org.brum.beds.ac.uk/10.3390/ijms222010995