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This study introduces PhysDual-GCN, a physics-informed graph neural network designed to approximate\n                    <jats:italic>docking-derived<\/jats:italic>\n                    binding affinity scores for DYRK2, an understudied yet biologically relevant target in Alzheimer\u2019s disease (AD). The model jointly processes ligand molecular graphs and a sequence-based graph representation of DYRK2, while explicitly incorporating Coulomb and Lennard\u2013Jones interaction terms as analytical physical energy components. Because no experimentally measured binding affinities are available for DYRK2-drug pairs, all reference labels used for evaluation were obtained exclusively from widely used classical docking tools (AutoDock Vina, Smina, QVina, CB-DOCK). These tools exhibit an inherent uncertainty of approximately\u2009\u00b1\u20090.5\u20131.5\u00a0kcal\/mol, which constrains the interpretability of absolute deviations. PhysDual-GCN was trained solely on docking-derived scores and evaluated using a strict ligand-level separation to avoid circularity during model development. Due to the limited number of ligands (\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u20094 FDA-approved AD drugs: brexpiprazole, donepezil, galantamine, rivastigmine), the results should be viewed as\n                    <jats:italic>agreement with computational references<\/jats:italic>\n                    rather than generalizable predictive performance. The model achieved low absolute errors (MAE\u2009=\u20090.31\u00a0kcal\/mol; RMSE\u2009=\u20090.44\u00a0kcal\/mol) relative to the reference docking scores and correctly identified stronger binders such as donepezil (\u2212 10.8\u00a0kcal\/mol) and brexpiprazole (\u2212 10.0\u00a0kcal\/mol). These findings demonstrate that integrating physical interaction terms into a GNN framework can enhance interpretability while providing a computationally efficient surrogate for classical docking workflows. Overall, PhysDual-GCN offers a biologically meaningful and explainable approximation tool for DYRK2 interaction scoring. While the present results are constrained by the small number of compounds and the absence of 3D protein features, the approach establishes a foundation for future large-scale, experimentally validated studies in AD drug repurposing.\n                  <\/jats:p>","DOI":"10.1038\/s41598-026-35102-7","type":"journal-article","created":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T16:14:59Z","timestamp":1770826499000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A physics-informed graph neural network to approximate docking-based binding affinity for DYRK2 in Alzheimer\u2019s drug repurposing"],"prefix":"10.1038","volume":"16","author":[{"given":"Veysel","family":"Gider","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cafer","family":"Budak","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,2,11]]},"reference":[{"key":"35102_CR1","doi-asserted-by":"publisher","first-page":"371","DOI":"10.14283\/jpad.2021.23","volume":"8","author":"AP Porsteinsson","year":"2021","unstructured":"Porsteinsson, A. 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