aceIntegrating graph attention networks and deep learning with multi-stage computational screening: Bisdemethoxycurcumin as a potent A₂A receptor inhibitor for Parkinson's disease
Parkinson's disease (PD) remains a progressive neurodegenerative disorder with limited disease modifying therapies, as current treatments such as L-DOPA offer only symptomatic relief and are associated with severe adverse effects. This study presents an integrated multi-stage computational pipeline that combines quantitative structure activity relationship (QSAR) modelling enhanced by graph attention networks (GATs), molecular docking, density functional theory (DFT), molecular dynamics (MD) simulations, and ADM…
A graph attention network QSAR model combined with docking, simulation and ADMET screening singled out bisdemethoxycurcumin as a predicted stronger A2A receptor binder with a better predicted toxicity profile than L-DOPA.
The evidence is entirely computational and predicted, and the authors state that bisdemethoxycurcumin still requires in-vitro and in-vivo experimental validation.
Evidence
- Peer-reviewedBioorganic Chemistry2026-10-14
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Truvace Impact Record TRV-2026-1990, v1: “Integrating graph attention networks and deep learning with multi-stage computational screening: Bisdemethoxycurcumin as a potent A₂A receptor inhibitor for Parkinson's disease.” Truvace, 2026-10-20. /record/TRV-2026-1990 (accessed at citation time). sha256 be3107af18cd6093…
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