Hypothesis

Integrating latent confounder variables directly into equivariant graph neural network (eqGNN) training via adversarial confounder encoding will enable the model to learn representations that isolate nonconfoundable network substructures—topological motifs that persist across batches, tissues, and species—and thereby prioritize aging‑specific drug targets with higher causal validity than current pretrained eqGNNs.

Mechanistic Rationale

Recent eqGNNs achieve AUROC 0.9665 by incorporating 3D protein structure and self‑supervised pre‑training on unlabeled molecules【https://academic.oup.com/bib/article/26/5/bbaf554/8303310】【https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2024.1452339/full】. However, these models still assume that the observed graph is free of hidden confounders【https://pmc.ncbi.nlm.nih.gov/articles/PMC11494969/】. When hidden variables such as batch effects, cell‑type composition, or genetic ancestry correlate with both topology and labels, the network learns spurious edges that masquerade as true interactions【https://academic.oup.com/gigascience/article/doi/10.1093/gigascience/giac014/6547681】. Causal work shows that standard confounder removal via GLM before training can degrade performance to below‑chance levels【https://academic.oup.com/gigascience/article/doi/10.1093/gigascience/giac014/6547681】. An adversarial confounder encoder, trained to predict known confounders from the node embeddings while the main eqGNN is optimized to minimize this prediction, forces the embeddings to be invariant to those factors【https://bioscipublisher.com/index.php/cmb/article/html/3925/】. Invariance has been shown to uncover subgraph patterns that are statistically indistinguishable under different confounder realizations—these are the nonconfoundable motifs described in theoretical work on network reconstruction with missing data【https://pmc.ncbi.nlm.nih.gov/articles/PMC3822397/】. By constraining the eqGNN to retain only information that cannot be linearly predicted from confounder proxies, the model is expected to highlight topological motifs (e.g., feed‑forward loops, cliques, or bridge nodes) that are mechanistically stable across contexts and thus more likely to represent bona fide aging‑related drug targets.

Testable Predictions

1. eqGNNs equipped with an adversarial confounder module will achieve higher out‑of‑sample AUROC on held‑out aging‑target datasets (e.g., DrugAge, Geroprotectors) when evaluated across multiple batches or tissue‑specific protein‑protein interaction (PPI) networks, compared with baseline eqGNNs that rely only on pre‑training【https://academic.oup.com/bib/article/26/5/bbaf554/8303310】.
2. The top‑ranked nodes identified by the confounder‑invariant eqGNN will overlap significantly with experimentally validated longevity genes (e.g., from the GenAge database) and will be enriched for known nonconfoundable motifs such as tri‑adic cliques and bow‑tie structures, whereas baseline eqGNN rankings will show no such enrichment.
3. Perturbation (CRISPR knock‑down or small‑molecule inhibition) of the top‑ranked invariant nodes in human fibroblasts will produce a consistent shift in senescence‑associated secretory phenotype (SASP) markers across at least three different donor lines and two culture conditions, while perturbations of baseline‑ranked nodes will yield heterogeneous or null effects.

Experimental Design

• Data: Collect aging‑relevant PPI networks from GTEx (multiple tissues), BioGRID, and STRING; label edges with known aging‑target interactions from DrugAge and Geroprotectors.
• Models: (a) Baseline eqGNN with 3D structure (e.g., SE(3)-Transformer) and self‑supervised pre‑training; (b) Same architecture augmented with an adversarial confounder head predicting batch, tissue, and donor ancestry via gradient reversal.
• Training: Optimize primary link‑prediction loss while maximizing confounder prediction loss (gradient reversal λ tuned via validation).
• Evaluation: Perform five‑fold cross‑validation where each fold holds out an entire tissue or batch; compute AUROC, AUPRC, and calibration. Additionally, conduct an external test on a completely independent aging‑interaction set (e.g., from the Human Ageing Genomic Resources).
• Validation: Use network motif detection (Mfinder) to extract frequency of tri‑adic cliques, feed‑forward loops, and bridge nodes among top‑5 % ranked proteins; compare enrichment via hypergeometric test against degree‑matched random sets.
• Perturbation: Select top‑10 invariant and baseline nodes; employ siRNA knockdown in primary human fibroblasts from three donors; measure SA‑β‑gal, p16^INK4a, and IL‑6 secretion after 72 h under normoxic and low‑serum conditions.

Potential Pitfalls and Alternatives

If adversarial training fails to remove confounder information (e.g., confounder head still predicts >80 % accuracy), we will increase the capacity of the confounder encoder or switch to a mutual‑information‑based invariance loss (e.g., DOMINO)【https://pmc.ncbi.nlm.nih.gov/articles/PMC11494969/】. Should the invariant eqGNN not outperform the baseline, we will examine whether hidden confounders are non‑linear or higher‑order, prompting the use of causal graph discovery algorithms (e.g., LiNGAM) to generate explicit confounder graphs for integration as auxiliary node features.

By directly modeling and removing confounder influence during representation learning, this approach aims to extract the nonconfoundable core of the aging interactome—a set of topologically robust, mechanistically interpretable nodes that can be prioritized for preclinical validation with greater confidence than current correlation‑driven GNN predictions.