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Targeting Monoamine Oxidase B for the Treatment of Alzheimer’s and Parkinson’s Diseases Using Novel Inhibitors Identified Using an Integrated Approach of Machine Learning and Computer-Aided Drug Design

Author

Listed:
  • Arif Jamal Siddiqui

    (Department of Biology, College of Science, University of Ha’il, Ha’il P.O. Box 2440, Saudi Arabia)

  • Sadaf Jahan

    (Department of Medical Laboratory Sciences, College of Applied Medical Sciences, Majmaah University, Al Majmaah 11952, Saudi Arabia)

  • Maqsood Ahmed Siddiqui

    (Department of Zoology, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia)

  • Andleeb Khan

    (Department of Pharmacology and Toxicology, College of Pharmacy, Jazan University, Jazan 45142, Saudi Arabia)

  • Mohammed Merae Alshahrani

    (Department of Clinical Laboratory Sciences, Faculty of Applied Medical Sciences, Najran University, 1988, Najran 61441, Saudi Arabia)

  • Riadh Badraoui

    (Department of Biology, College of Science, University of Ha’il, Ha’il P.O. Box 2440, Saudi Arabia)

  • Mohd Adnan

    (Department of Biology, College of Science, University of Ha’il, Ha’il P.O. Box 2440, Saudi Arabia)

Abstract

Neurological disorders are disorders characterized by progressive loss of neurons leading to disability. Neurotransmitters such as nor-adrenaline, dopamine, and serotonin are partially regulated by the enzyme monoamine oxidase (MAO). Treatments for conditions like Alzheimer’s, Parkinson’s, anxiety, and depression involve the use of MAOIs. To target MAO enzyme inhibition, various scaffolds are prepared and evaluated, including modified coumarins, chromone carboxylic acid substituents, pyridazine derivatives, and indolylmethylamine. The research presented here focuses on combining different computational tools to find new inhibitors of the MAO-B protein. We discovered 5 possible chemical inhibitors using the above computational techniques. We found five molecular inhibitors with high binding affinity using computational methods. These five molecules showed a high binding affinity; they are −10.917, −10.154, −10.223, −10.858, and −9.629 Kcal/mol, respectively. Additionally, the selected inhibitors were further examined by in vitro activity, and their binding affinity was confirmed using an enzyme-based assay. In summary, the computational studies performed here using molecular dynamics and free energy calculations can also be used to design and predict highly potent derivatives as MAO-B inhibitors, and these top inhibitors help in the development of novel drugs for neurological diseases such as Alzheimer’s and Parkinson’s.

Suggested Citation

  • Arif Jamal Siddiqui & Sadaf Jahan & Maqsood Ahmed Siddiqui & Andleeb Khan & Mohammed Merae Alshahrani & Riadh Badraoui & Mohd Adnan, 2023. "Targeting Monoamine Oxidase B for the Treatment of Alzheimer’s and Parkinson’s Diseases Using Novel Inhibitors Identified Using an Integrated Approach of Machine Learning and Computer-Aided Drug Desig," Mathematics, MDPI, vol. 11(6), pages 1-17, March.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:6:p:1464-:d:1100180
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    References listed on IDEAS

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    1. Antonio Mucherino & Petraq J. Papajorgji & Panos M. Pardalos, 2009. "Data Mining in Agriculture," Springer Optimization and Its Applications, Springer, number 978-0-387-88615-2, September.
    2. Jingyi Qu & Shixing Wu & Jinjie Zhang, 2023. "Flight Delay Propagation Prediction Based on Deep Learning," Mathematics, MDPI, vol. 11(3), pages 1-24, January.
    3. Faitouri A. Aboaoja & Anazida Zainal & Abdullah Marish Ali & Fuad A. Ghaleb & Fawaz Jaber Alsolami & Murad A. Rassam, 2023. "Dynamic Extraction of Initial Behavior for Evasive Malware Detection," Mathematics, MDPI, vol. 11(2), pages 1-23, January.
    4. Mumin Zhang & Yuzhi Wang & Haochen Zhang & Zhiyun Peng & Junjie Tang, 2023. "A Novel and Robust Wind Speed Prediction Method Based on Spatial Features of Wind Farm Cluster," Mathematics, MDPI, vol. 11(3), pages 1-17, January.
    5. Antonio Mucherino & Petraq J. Papajorgji & Panos M. Pardalos, 2009. "k-Nearest Neighbor Classification," Springer Optimization and Its Applications, in: Data Mining in Agriculture, chapter 0, pages 83-106, Springer.
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