{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T09:47:31Z","timestamp":1785923251615,"version":"3.56.0"},"reference-count":36,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T00:00:00Z","timestamp":1785888000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Mol. Sci.","IJMS"],"abstract":"<jats:p>Parkinson\u2019s disease (PD) is characterised by the pathological aggregation of \u03b1-synuclein (\u03b1-syn) into Lewy body inclusions, yet no disease-modifying therapy exists. To address this, we developed an integrated computational pipeline combining quantitative structure\u2013activity relationship (QSAR) modelling, structure-based virtual screening, molecular dynamics (MD) simulation, and molecular mechanics Poisson\u2013Boltzmann surface area (MM-PBSA) binding free energy calculations to repurpose FDA-approved drugs as \u03b1-syn fibril inhibitors. Two complementary QSAR model families were trained on 501 \u03b1-syn binding affinity records from BindingDB: Morgan extended-connectivity fingerprint (ECFP4) classifiers and a frozen ChemBERTa-77M-MLM transformer encoder, each using Random Forest and Logistic Regression. The applicability domain (AD) was assessed using Morgan\u2013Tanimoto similarity (Tc \u2265 0.40) and calibrated ChemBERTa cosine distance (\u03b8 \u2264 0.367). A three-stage funnel applying central nervous system (CNS) permeability filters, a consensus QSAR probability threshold (\u22650.80), and AD gating reduced 2241 FDA-approved drugs to 205 candidates for AutoDock Vina 1.2.6 docking against two sites on the cryo-electron microscopy (cryo-EM) \u03b1-syn fibril structure, PDB 6SSX: the inter-protofilament cleft (Site 1) and the non-amyloid-beta component (NAC) groove (Site 2). The Morgan fingerprint models achieved an area under the receiver operating characteristic curve (AUROC) of up to 0.940 and a balanced accuracy of 0.810; the ChemBERTa models achieved an AUROC of 0.785 and a balanced accuracy of 0.728. Notably, ChemBERTa AD covered 76.8% of the FDA drugs versus only 5.5% for Morgan\u2013Tanimoto, enabling broad-spectrum screening. The top docking candidates were Olaparib (\u22127.91 kcal\/mol), Paliperidone (\u22127.75 kcal\/mol), Niraparib (\u22127.18 kcal\/mol), Dordaviprone (\u22127.06 kcal\/mol), and Parecoxib (\u22126.89 kcal\/mol). The MD simulations over 200 ns across three independent replicates confirmed stable NAC groove binding, and replicate-averaged MM-PBSA calculations yielded \u0394G = \u221220.6 \u00b1 1.9 kcal\/mol for Olaparib at Site 2, \u221217.1 \u00b1 0.9 kcal\/mol for Risperidone, and \u221216.9 \u00b1 0.8 kcal\/mol for Paliperidone, reported as the mean \u00b1 standard error of the mean (SEM) across replicates. Olaparib additionally formed five hydrogen bonds in the representative pose, while MD trajectories maintained approximately 2\u20135 hydrogen bonds, together with a halogen bond within the NAC groove, the largest contact count of any screened compound. These findings identify Olaparib as a novel high-affinity repurposing lead, while Paliperidone and Risperidone are reported as chemically informative secondary NAC\u2013groove binders rather than proposed antiparkinsonian therapeutics, given that their dopamine D2-antagonist pharmacology is clinically associated with drug-induced parkinsonism. All of the candidates warrant experimental validation via thioflavin-T fluorescence or nuclear magnetic resonance (NMR) spectroscopy.<\/jats:p>","DOI":"10.3390\/ijms27157025","type":"journal-article","created":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T09:16:22Z","timestamp":1785921382000},"page":"7025","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["QSAR-Guided Virtual Screening and Molecular Dynamics Reveal Olaparib as a Repurposing Lead Against \u03b1-Synuclein Aggregation"],"prefix":"10.3390","volume":"27","author":[{"given":"Mena","family":"Abdelsayed","sequence":"first","affiliation":[{"name":"Lankenau Institute for Medical Research, Philadelphia, PA 19096, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2939-7772","authenticated-orcid":false,"given":"Yassir","family":"Boulaamane","sequence":"additional","affiliation":[{"name":"Laboratory of Innovative Technologies, National School of Applied Sciences of Tangier, Abdelmalek Essaadi University, Tetouan 93000, Morocco"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"896","DOI":"10.1016\/S0140-6736(14)61393-3","article-title":"Parkinson\u2019s disease","volume":"386","author":"Kalia","year":"2015","journal-title":"Lancet"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1016\/j.neurol.2019.11.002","article-title":"Parkinson\u2019s disease: From bench to bedside","volume":"176","author":"Draoui","year":"2020","journal-title":"Rev. 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