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TRUVACE RECORD VERSION
record: TRV-2026-1990
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-20T06:55:23.777736Z
status: published
lens: g_space
sector: health
headline: Integrating graph attention networks and deep learning with multi-stage computational screening: Bisdemethoxycurcumin as a potent A₂A receptor inhibitor for Parkinson's disease
dek: 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…
gain_title: 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.
problem_title: (none)
trace_subject: (none)
gain_reading: 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.
gain_evidence: BDMC offers superior predicted binding affinity to the A₂A receptor and a more favourable predicted toxicity profile compared to L-DOPA
problem_reading: (none)
problem_evidence: (none)
quick_read: Researchers built a multi-stage computational pipeline for Parkinson's disease drug discovery. Graph attention networks were used in QSAR modelling on 1393 compounds, followed by docking, density functional theory, molecular dynamics and toxicity profiling of six phytochemicals from turmeric and sage.

Among the compounds tested, bisdemethoxycurcumin stood out as the best predicted A2A receptor binder and appeared safer than L-DOPA in silico. All of this remains computational, so the findings only justify further laboratory and animal testing and do not show a working therapy.
limitation: The evidence is entirely computational and predicted, and the authors state that bisdemethoxycurcumin still requires in-vitro and in-vivo experimental validation.
tag: Evidence-backed gain
key_points: The graph attention network model outperformed Extra Trees, Random Forest and SVR-RBF in predictive accuracy, with R2 of 0.991 against 0.742, 0.761 and 0.65. | Bisdemethoxycurcumin docked to the A2A receptor at -9.1 kcal/mol, compared with -7.2 kcal/mol for L-DOPA. | A 200 ns molecular dynamics run showed stable binding with no dissociation events. | ADMET tools predicted blood brain barrier permeability and toxicity Class 5 (LD50 2560 mg/kg), against Class 4 for L-DOPA.
rundown: The study targeted four Parkinson's-related proteins: A2A receptor, GABA-A receptor, Nrf2 and alpha-synuclein. The training set was derived from 6-hydroxybenzothiazole 2-carboxamide and related scaffolds, with 312 molecular descriptors in the feature pool.

Y-randomization with 100 permutations was used to check that the models were not capturing chance correlations. Docking covered ar-turmerone, alpha-turmerone, bisdemethoxycurcumin, camphor, 1,8-cineole and thujone isomers.

Quantum calculations showed bisdemethoxycurcumin had the lowest HOMO-LUMO gap (0.1428 eV) and the highest electrophilicity index (0.1477). The molecular dynamics run showed 3-8 persistent hydrogen bonds, averaging about 5.
sources:
- peer_reviewed | Bioorganic Chemistry | https://doi.org/10.1016/j.bioorg.2026.110653 | 2026-10-14
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