Quantum Artificial Neural Network Approach to Derive a Highly Predictive 3D-QSAR Model for Blood–Brain Barrier Passage
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)...
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MDPI AG
2021-10-01
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| Series: | International Journal of Molecular Sciences |
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| Online Access: | https://www.mdpi.com/1422-0067/22/20/10995 |
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| author | Taeho Kim Byoung Hoon You Songhee Han Ho Chul Shin Kee-Choo Chung Hwangseo Park |
| author_facet | Taeho Kim Byoung Hoon You Songhee Han Ho Chul Shin Kee-Choo Chung Hwangseo Park |
| author_sort | Taeho Kim |
| collection | DOAJ |
| description | 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. |
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| issn | 1661-6596 1422-0067 |
| language | English |
| last_indexed | 2024-03-10T06:30:37Z |
| publishDate | 2021-10-01 |
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| spelling | doaj.art-3dd6829633d9400682c5d66303dfa65c2023-11-22T18:32:02ZengMDPI AGInternational Journal of Molecular Sciences1661-65961422-00672021-10-0122201099510.3390/ijms222010995Quantum Artificial Neural Network Approach to Derive a Highly Predictive 3D-QSAR Model for Blood–Brain Barrier PassageTaeho Kim0Byoung Hoon You1Songhee Han2Ho Chul Shin3Kee-Choo Chung4Hwangseo Park5Department of Bioscience and Biotechnology, Sejong University, Kwangjin-gu, Seoul 05006, KoreaWhan In Pharmaceutical Co., Ltd., 11, Songpa-gu, Seoul 05855, KoreaWhan In Pharmaceutical Co., Ltd., 11, Songpa-gu, Seoul 05855, KoreaWhan In Pharmaceutical Co., Ltd., 11, Songpa-gu, Seoul 05855, KoreaDepartment of Bioscience and Biotechnology, Sejong University, Kwangjin-gu, Seoul 05006, KoreaDepartment of Bioscience and Biotechnology, Sejong University, Kwangjin-gu, Seoul 05006, KoreaA 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.https://www.mdpi.com/1422-0067/22/20/10995blood–brain barrier3D-QSARstructural alignmentmolecular ESP descriptorartificial neural network |
| spellingShingle | Taeho Kim Byoung Hoon You Songhee Han Ho Chul Shin Kee-Choo Chung Hwangseo Park Quantum Artificial Neural Network Approach to Derive a Highly Predictive 3D-QSAR Model for Blood–Brain Barrier Passage International Journal of Molecular Sciences blood–brain barrier 3D-QSAR structural alignment molecular ESP descriptor artificial neural network |
| title | Quantum Artificial Neural Network Approach to Derive a Highly Predictive 3D-QSAR Model for Blood–Brain Barrier Passage |
| title_full | Quantum Artificial Neural Network Approach to Derive a Highly Predictive 3D-QSAR Model for Blood–Brain Barrier Passage |
| title_fullStr | Quantum Artificial Neural Network Approach to Derive a Highly Predictive 3D-QSAR Model for Blood–Brain Barrier Passage |
| title_full_unstemmed | Quantum Artificial Neural Network Approach to Derive a Highly Predictive 3D-QSAR Model for Blood–Brain Barrier Passage |
| title_short | Quantum Artificial Neural Network Approach to Derive a Highly Predictive 3D-QSAR Model for Blood–Brain Barrier Passage |
| title_sort | quantum artificial neural network approach to derive a highly predictive 3d qsar model for blood brain barrier passage |
| topic | blood–brain barrier 3D-QSAR structural alignment molecular ESP descriptor artificial neural network |
| url | https://www.mdpi.com/1422-0067/22/20/10995 |
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