Background

Recent work shows that GNNs applied to drug‑target affinity (DTA) and aging interactome models suffer from batch effects, data leakage via shared scaffolds, and unmodeled node/edge confounders[https://academic.oup.com/bib/article/26/5/bbaf554/8303310]. Methods such as RelFCI and GMAC demonstrate that explicitly adjusting for latent confounders improves causal graph recovery[https://arxiv.org/html/2507.01700v2][https://pmc.ncbi.nlm.nih.gov/articles/PMC6536645/]. In static biological graphs, high co‑expression is often mistaken for causality when confounders remain unaddressed[https://bioscipublisher.com/index.php/cmb/article/html/3925/]. Over‑smoothing—a known limitation of deep GNNs—further obscures true signal by making node representations indistinguishable after many message‑passing layers.

Hypothesis

Latent confounders drive artificial homogenization of node features, causing GNN over‑smoothing that inflates predictions for highly connected, confounder‑rich hub proteins (e.g., HSP90, TP53) as putative aging drug targets, while suppressing true peripheral targets.

Mechanistic Reasoning

1. Confounder‑induced feature convergence – Hidden batch or physiological variables (e.g., inflammation, circadian state) correlate with both gene expression and network topology. When these variables are not regressed out, connected nodes acquire similar feature vectors, reducing the variance that GNNs rely on to distinguish targets.
2. Over‑smoothing amplification – Each GNN layer aggregates neighbor features; with converged node features, additional layers rapidly push all nodes toward a common embedding. Hub nodes, possessing the highest degree, reach this steady state fastest, yielding uniformly high scores regardless of true biological relevance.
3. Spurious hub prioritization – Because aging interactome analyses often rank targets by GNN output score, hubs confound the ranking, producing false‑positive hits that correlate with confounder load rather than causal aging mechanisms.
4. Corrective edge dropout – Randomly dropping edges proportionally to estimated confounder strength (derived from residuals of a GMAC‑style confounder model) injects noise that prevents premature convergence, preserving discriminative power for peripheral, low‑degree true targets.

Testable Predictions

• Prediction 1: In a benchmark aging‑target dataset (e.g., curated geroprotector interactions from DrugAge[https://genomics.senescence.info/drug/]), a standard GNN will assign top‑5 % scores to hub proteins with high confounder burden (top quartile of GMAC‑estimated latent variables). After applying confounder‑aware edge dropout, the same hubs’ scores will drop significantly (paired t‑test, p < 0.01) while scores for known peripheral aging targets (e.g., SIRT6, KLOTHO) will rise.
• Prediction 2: Ablation studies varying the number of GNN layers will show that performance (AUROC on a held‑out aging‑target set) peaks at 2–3 layers for the vanilla model but shifts to 4–5 layers for the confounder‑adjusted model, indicating reduced over‑smoothing.
• Prediction 3: Permutation of confounder labels (shuffling residuals across nodes) will abolish the improvement from edge dropout, returning performance to baseline levels.

Experimental Design

1. Data preparation – Collect a multi‑omics aging network (protein‑protein interactions, transcriptome, epigenome) from human tissues. Estimate latent confounders using GMAC (PCA on residuals after regressing out known covariates).
2. Model variants – Train three GNN architectures (GCN, GraphSAGE, GAT) on the same drug‑target affinity task: (a) vanilla, (b) with standard dropout, (c) with confounder‑weighted edge dropout (probability ∝ confounder score).
3. Evaluation – Use a temporal split that ensures no aging‑trajectory leakage[https://pmc.ncbi.nlm.nih.gov/articles/PMC12995911/]. Assess ranking of known aging DrugAge targets versus random proteins, focusing on hub vs. non‑hub strata.
4. Statistical testing – Compare AUROC, enrichment of top‑10 % predictions, and calibration curves across variants using DeLong’s test and bootstrap confidence intervals.

Falsification Criteria

If confounder‑aware edge dropout fails to (a) reduce hub‑centric bias, (b) improve peripheral target recovery, or (c) show a layer‑depth shift as predicted, the hypothesis is falsified. Likewise, if permuting confounder labels does not diminish the edge‑dropout benefit, the claimed mechanistic link is invalid.

Conclusion: By explicitly modeling how latent confounders induce feature convergence that exacerbates GNN over‑smoothing, we propose a concrete, falsifiable adjustment that should rescue true aging‑signal from hub‑driven noise, addressing a critical gap identified in current GNN‑based drug‑target prediction pipelines.