دورية أكاديمية
Machine learning driven web-based app platform for the discovery of monoamine oxidase B inhibitors
| العنوان: | Machine learning driven web-based app platform for the discovery of monoamine oxidase B inhibitors |
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| المؤلفون: | Sunil Kumar, Ratul Bhowmik, Jong Min Oh, Mohamed A. Abdelgawad, Mohammed M. Ghoneim, Rasha Hamed Al‑Serwi, Hoon Kim, Bijo Mathew |
| المصدر: | Scientific Reports, Vol 14, Iss 1, Pp 1-20 (2024) |
| بيانات النشر: | Nature Portfolio, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Medicine LCC:Science |
| مصطلحات موضوعية: | Monoamine oxidase B, ML-QSAR, PubChem fingerprints, Substructure fingerprints, 1D and 2D molecular descriptors, Prediction models, Medicine, Science |
| الوصف: | Abstract Monoamine oxidases (MAOs), specifically MAO-A and MAO-B, play important roles in the breakdown of monoamine neurotransmitters. Therefore, MAO inhibitors are crucial for treating various neurodegenerative disorders, including Parkinson's disease (PD), Alzheimer’s disease (AD), and amyotrophic lateral sclerosis (ALS). In this study, we developed a novel cheminformatics pipeline by generating three diverse molecular feature-based machine learning-assisted quantitative structural activity relationship (ML-QSAR) models concerning MAO-B inhibition. PubChem fingerprints, substructure fingerprints, and one-dimensional (1D) and two-dimensional (2D) molecular descriptors were implemented to unravel the structural insights responsible for decoding the origin of MAO-B inhibition in 249 non-reductant molecules. Based on a random forest ML algorithm, the final PubChem fingerprint, substructure fingerprint, and 1D and 2D molecular descriptor prediction models demonstrated significant robustness, with correlation coefficients of 0.9863, 0.9796, and 0.9852, respectively. The significant features of each predictive model responsible for MAO-B inhibition were extracted using a comprehensive variance importance plot (VIP) and correlation matrix analysis. The final predictive models were further developed as a web application, MAO-B-pred ( https://mao-b-pred.streamlit.appTest/ ), to allow users to predict the bioactivity of molecules against MAO-B. Molecular docking and dynamics studies were conducted to gain insight into the atomic-level molecular interactions between the ligand-receptor complexes. These findings were compared with the structural features obtained from the ML-QSAR models, which supported the mechanistic understanding of the binding phenomena. The presented models have the potential to serve as tools for identifying crucial molecular characteristics for the rational design of MAO-B target inhibitors, which may be used to develop effective drugs for neurodegenerative disorders. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2045-2322 |
| العلاقة: | https://doaj.org/toc/2045-2322Test |
| DOI: | 10.1038/s41598-024-55628-y |
| الوصول الحر: | https://doaj.org/article/9e575b3ed40547a2878ec981ba070d6eTest |
| رقم الانضمام: | edsdoj.9e575b3ed40547a2878ec981ba070d6e |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 20452322 |
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| DOI: | 10.1038/s41598-024-55628-y |