82
Views
0
CrossRef citations to date
0
Altmetric
Research Article

ANN multi-layer perceptron for prediction of blood–brain barrier permeable compounds for central nervous system therapeutics

, , &
Received 18 Apr 2023, Accepted 28 Feb 2024, Published online: 18 Mar 2024

References

  • Abbott, N. J., Rönnbäck, L., & Hansson, E. (2006). Astrocyte–endothelial interactions at the blood–brain barrier. Nature Reviews Neuroscience, 7(1), 41–53. https://doi.org/10.1038/nrn1824
  • Adenot, M., & Lahana, R. (2004). Blood-brain barrier permeation models: Discriminating between potential CNS and non-CNS drugs including P-glycoprotein substrates. Journal of Chemical Information and Computer Sciences, 44(1), 239–248. https://doi.org/10.1021/ci034205d
  • Agatonovic-Kustrin, S., & Beresford, R. (2000). Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. Journal of Pharmaceutical and Biomedical Analysis, 22(5), 717–727. https://doi.org/10.1016/s0731-7085(99)00272-1
  • Bradley, A. P. (1997). The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition, 30(7), 1145–1159.
  • Daneman, R., & Prat, A. (2015). The blood-brain barrier. Cold Spring Harbor Perspectives in Biology, 7(1), a020412. https://doi.org/10.1101/cshperspect.a020412
  • Davson, H. (1989). History of the blood-brain barrier concept. Implications of the blood-brain barrier and its manipulation. In E. A. Neuwelt (Ed.), Basic science aspects (Vol. 1, pp. 27–52). Springer.
  • Gao, Z., Chen, Y., Cai, X., & Xu, R. (2017). Predict drug permeability to blood–brain-barrier from clinical phenotypes: Drug side effects and drug indications. Bioinformatics (Oxford, England), 33(6), 901–908. https://doi.org/10.1093/bioinformatics/btw713
  • Ghose, A. K., Viswanadhan, V. N., & Wendoloski, J. J. (1999). A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of known drug databases. Journal of Combinatorial Chemistry, 1(1), 55–68. https://doi.org/10.1021/cc9800071
  • Hendricks, B. K., Cohen-Gadol, A. A., & Miller, J. C. (2015). Novel delivery methods bypassing the blood-brain and blood-tumor barriers. Neurosurgical Focus, 38(3), E10. https://doi.org/10.3171/2015.1.FOCUS14767
  • Hong, H., Xie, Q., Ge, W., Qian, F., Fang, H., Shi, L., Su, Z., Perkins, R., & Tong, W. (2008). Mold2, molecular descriptors from 2D structures for chemoinformatics and toxicoinformatics. Journal of Chemical Information and Modeling, 48(7), 1337–1344. https://doi.org/10.1021/ci800038f
  • Jung, E., Kim, J., Kim, M., Jung, D. H., Rhee, H., Shin, J.-M., Choi, K., Kang, S.-K., Kim, M.-K., Yun, C.-H., Choi, Y.-J., & Choi, S.-H. (2007). Artificial neural network models for prediction of intestinal permeability of oligopeptides. BMC Bioinformatics, 8(1), 1–9. https://doi.org/10.1186/1471-2105-8-245
  • Martins, I. F., Teixeira, A. L., Pinheiro, L., & Falcao, A. O. (2012). A Bayesian approach to in silico blood-brain barrier penetration modeling. Journal of Chemical Information and Modeling, 52(6), 1686–1697. https://doi.org/10.1021/ci300124c
  • Meng, C., Wei, L., & Zou, Q. (2019). SecProMTB: Support vector machine‐based classifier for secretory proteins using imbalanced data sets applied to Mycobacterium tuberculosis. Proteomics, 19(17), e1900007. https://doi.org/10.1002/pmic.201900007
  • Plisson, F., & Piggott, A. M. (2019). Predicting blood–brain barrier permeability of marine-derived kinase inhibitors using ensemble classifiers reveals potential hits for neurodegenerative disorders. Marine Drugs, 17(2), 81. https://doi.org/10.3390/md17020081
  • Shaker, B., Yu, M. S., Song, J. S., Ahn, S., Ryu, J. Y., Oh, K. S., & Na, D. (2021). LightBBB: Computational prediction model of blood–brain-barrier penetration based on LightGBM. Bioinformatics (Oxford, England), 37(8), 1135–1139. https://doi.org/10.1093/bioinformatics/btaa918
  • Shen, J., Du, Y., Zhao, Y., Liu, G., & Tang, Y. (2008). In silico prediction of blood–brain partitioning using a chemometric method called genetic algorithm based variable selection. QSAR & Combinatorial Science, 27(6), 704–717. https://doi.org/10.1002/qsar.200710129
  • Singh, M., Divakaran, R., Konda, L. S. K., & Kristam, R. (2020). A classification model for blood brain barrier penetration. Journal of Molecular Graphics & Modelling, 96, 107516. https://doi.org/10.1016/j.jmgm.2019.107516
  • Srinivasan, B., Kolli, A. R., Esch, M. B., Abaci, H. E., Shuler, M. L., & Hickman, J. J. (2015). TEER measurement techniques for in vitro barrier model systems. Journal of Laboratory Automation, 20(2), 107–126. https://doi.org/10.1177/2211068214561025
  • Suykens, J. A., & Vandewalle, J. (1999). Least squares support vector machine classifiers. Neural Processing Letters, 9(3), 293–300. https://doi.org/10.1023/A:1018628609742
  • Tang, Q., Nie, F., Zhao, Q., & Chen, W. (2022). A merged molecular representation deep learning method for blood-brain barrier permeability prediction. Briefings in Bioinformatics, 23(5), bbac357. https://doi.org/10.1093/bib/bbac357
  • Wang, Z., Yang, H., Wu, Z., Wang, T., Li, W., Tang, Y., & Liu, G. (2018). In silico prediction of blood–brain barrier permeability of compounds by machine learning and resampling methods. ChemMedChem. 13(20), 2189–2201. https://doi.org/10.1002/cmdc.201800533
  • Weininger, D. (1988). SMILES, a chemical language, and information system. 1. Introduction to methodology and encoding rules. Journal of Chemical Information and Computer Sciences, 28(1), 31–36. https://doi.org/10.1021/ci00057a005
  • Wu, Z., Ramsundar, B., Feinberg, E. N., Gomes, J., Geniesse, C., Pappu, A. S., Leswing, K., & Pande, V. (2018). MoleculeNet: A benchmark for molecular machine learning. Chemical Science, 9(2), 513–530. https://doi.org/10.1039/c7sc02664a
  • Yap, C. W. (2011). PaDEL-descriptor: An open source software to calculate molecular descriptors and fingerprints. Journal of Computational Chemistry, 32(7), 1466–1474.
  • Yuan, Y., Zheng, F., & Zhan, C. G. (2018). Improved prediction of blood–brain barrier permeability through machine learning with combined use of molecular property-based descriptors and fingerprints. The AAPS Journal, 20(3), 54. https://doi.org/10.1208/s12248-018-0215-8

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.