ArticleScientific reports2024
Machine learning driven web-based app platform for the discovery of monoamine oxidase B inhibitors.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 20 citations in OpenAlex.
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- Machine Learning-Driven Bioprospecting of Cholinergic Medicinal Plants for Putative Leads Against Butyrylcholinesterase Towards Alzheimer's Care.Biochemistry research international · 2026Article
- Cholinesterase Inhibition and Anticancer Properties of [4-(Benzyloxy) phenyl]{Methylidene}hydrazinylidene]-1,3-dihydro-2H-Indol-2-ones Using Swiss Target-guided Prediction.Current computer-aided drug design · 2026Article
- Key Chemotypes for the Rational Design of Dual AChE/BACE-1 Inhibitors.Current medicinal chemistry · 2026Review
- Development of QSAR Models and Web Applications for Predicting hDHFR Inhibitor Bioactivity Using Machine Learning.Molecules (Basel, Switzerland) · 2025Article
- Chemical probes for enzyme imaging: challenges in design, synthesis and biomedical applications.Chemical science · 2025Review
- Qsarna: An Online Tool for Smart Chemical Space Navigation in Drug Design.Journal of chemical information and modeling · 2025Article
- Dual inhibition of AChE and MAO-B in Alzheimer's disease: machine learning approaches and model interpretations.Molecular diversity · 2025Article
- Machine Learning-Based High-Throughput Screening, Molecular Modeling and Quantum Chemical Analysis to Investigate Mycobacterium tuberculosis MetRS Inhibitors.ChemistryOpen · 2025Article
- New class of thio/semicarbazide-based benzyloxy derivatives as selective class of monoamine oxidase-B inhibitors.Scientific reports · 2024Article
- Exploring putative drug properties associated with TNF-alpha inhibition and identification of potential targets in cardiovascular disease using machine learning-assisted QSAR modeling and virtual reverse pharmacology approach.Molecular diversity · 2024Article
Corrections and comments
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Authors and funding
8 authors at 6 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
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.app/ ), 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.