Hypothesis\nDynamic epigenomic edge‑attention GNN (DEEA‑GNN) that treats aging‑associated changes in DNA methylation and histone acetylation as time‑varying edge weights in a protein‑protein interaction network will outperform static‑topology GNNs in identifying bona‑fide longevity drug targets and will show reduced degree bias.\n## Mechanistic reasoning\nAging drives progressive, tissue‑specific epigenomic remodeling that alters the biophysical properties of protein surfaces, thereby modulating the strength and specificity of PPIs [6]. Static GNNs that encode proteins only from sequence ignore this context, leading to misleading edge importance and inflated predictions for high‑degree hubs [1]. By converting longitudinal epigenomic matrices (e.g., CpG beta values, acetylation ChIP‑seq signal) into edge‑wise confidence scores that decay or grow with biological age, we give the network a mechanistic handle on when a interaction is likely to be druggable in an aged versus young state.\n## Model design\n- Nodes: proteins from the 61‑protein druggable‑aging subnetwork [5].\n- Edges: baseline PPI confidence from STRING or BioGRID, multiplied by an epigenomic scaling factor ε(t) = f(Δmethylation, Δacetylation) computed for each tissue‑age pair.\n- GNN layers: edge‑attention mechanism that learns to weigh ε(t) alongside node features (amino‑acid k‑mers, disorder scores).\n- Training objective: rank known lifespan‑extending compounds (e.g., rapamycin, metformin) higher than inactive chemicals in a DTI‑style loss, using cross‑tissue hold‑outs to assess out‑of‑distribution generalization.\n## Testable predictions\n1. DEEA‑GNN will achieve a statistically significant improvement in AUROC (≥0.05 increase) over a sequence‑only GNN when evaluated on a leave‑one‑tissue‑out cross‑validation of the druggable‑aging subnetwork.\n2. The top‑10 ranked targets from DEEA‑GNN will show enrichment for genes whose knockdown extends lifespan in C. elegans or mouse (hypergeometric p < 0.01), whereas the static GNN’s list will not.\n3. Permutation of the epigenomic edge weights (shuffling ε(t) across edges) will abolish the performance gain, confirming that the observed improvement depends on the temporal epigenomic signal rather than architectural tricks.\n4. In a prospective wet‑lab test, CRISPR‑mediated perturbation of the highest‑ranked DEEA‑GNN target in human fibroblasts will produce a measurable reversal of age‑related epigenetic clocks (e.g., GrimAge reduction >1 year) compared with non‑targeting controls.\n## Falsifiability\nIf any of the four predictions fails under rigorous statistical testing (α = 0.05, Bonferroni‑corrected for multiple comparisons), the hypothesis that epigenomic edge dynamics are essential for GNN‑based aging target discovery is falsified. Conversely, confirmation would support marrying epigenomic time series with graph representation learning to overcome the current methodological gaps in the field.