ArticleMolecular diversity2024
Probing the molecular mechanisms of α-synuclein inhibitors unveils promising natural candidates through machine-learning QSAR, pharmacophore modeling, and molecular dynamics simulations.
Article in Molecular diversity, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- QSAR-Guided Virtual Screening and Molecular Dynamics Reveal Olaparib as a Repurposing Lead Against α-Synuclein Aggregation.International journal of molecular sciences · 2026Article
- Repurposing DrugBank compounds as NAD-dependent deacetylase sirtuin 2 inhibitors via QSAR modelling with gradient boosting algorithms and all-atom molecular simulations.Molecular diversity · 2026Article
- Drug screening for α-synuclein aggregation inhibitors via multimodal graph neural network.Briefings in bioinformatics · 2026Article
- Artificial Intelligence for Natural Products Drug Discovery in Neurodegenerative Therapies: A Review.Biomolecules · 2026Review
- Drug Repurposing for AML: Structure-Based Virtual Screening and Molecular Simulations of FDA-Approved Compounds with Polypharmacological Potential.Biomedicines · 2025Article
- Wild-Type α-Synuclein Structure and Aggregation: A Comprehensive Coarse-Grained and All-Atom Molecular Dynamics Study.Journal of chemical information and modeling · 2024Article
- Article
- Computational exploration of acefylline derivatives as MAO-B inhibitors for Parkinson's disease: insights from molecular docking, DFT, ADMET, and molecular dynamics approaches.Frontiers in chemistry · 2024Article
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4 authors.
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Abstract
Parkinson's disease is characterized by a multifactorial nature that is linked to different pathways. Among them, the abnormal deposition and accumulation of α-synuclein fibrils is considered a neuropathological hallmark of Parkinson's disease. Several synthetic and natural compounds have been tested for their potency to inhibit the aggregation of α-synuclein. However, the molecular mechanisms responsible for the potency of these drugs to further rationalize their development and optimization are yet to be determined. To enhance our understanding of the structural requirements necessary for modulating the aggregation of α-synuclein fibrils, we retrieved a large dataset of α-synuclein inhibitors with their reported potency from the ChEMBL database to explore their chemical space and to generate QSAR models for predicting new bioactive compounds. The best performing QSAR model was applied to the LOTUS natural products database to screen for potential α-synuclein inhibitors followed by a pharmacophore design using the representative compounds sampled from each cluster in the ChEMBL dataset. Five natural products were retained after molecular docking studies displaying a binding affinity of - 6.0 kcal/mol or lower. ADMET analysis revealed satisfactory properties and predicted that all the compounds can cross the blood-brain barrier and reach their target. Finally, molecular dynamics simulations demonstrated the superior stability of LTS0078917 compared to the clinical candidate, Anle138b. We found that LTS0078917 shows promise in stabilizing the α-synuclein monomer by specifically binding to its hairpin-like coil within the N-terminal region. Our dynamic analysis of the inhibitor-monomer complex revealed a tendency towards a more compact conformation, potentially reducing the likelihood of adopting an elongated structure that favors the formation and aggregation of pathological oligomers. These findings offer valuable insights for the development of novel α-synuclein inhibitors derived from natural sources.
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