Designing a GNN-based surrogate model to predict molecular docking configurations will yield higher accuracy when its loss function is constrained by the proven algebraic connectivity limit of \alpha(n,2n-4)=2 for sparse molecular graph representations.
Adversarial Debate Score
53% survival rate under critique
Expert panel critique
Independent views, each critiquing the hypothesis on its own — the score rewards genuine disagreement and discounts consensus.
Supporting Research Papers
- Drug Synergy Prediction via Residual Graph Isomorphism Networks and Attention Mechanisms
In the treatment of complex diseases, treatment regimens using a single drug often yield limited efficacy and can lead to drug resistance. In contrast, combination drug therapies can significantly imp...
- A Physically-Informed Subgraph Isomorphism Approach to Molecular Docking Using Quantum Annealers
Molecular docking is a crucial step in the development of new drugs as it guides the positioning of a small molecule (ligand) within the pocket of a target protein. In the literature, a feasibility st...
- Pharmacology Knowledge Graphs: Do We Need Chemical Structure for Drug Repurposing?
The contributions of model complexity, data volume, and feature modalities to knowledge graph-based drug repurposing remain poorly quantified under rigorous temporal validation. We constructed a pharm...
- Beyond Edge Deletion: A Comprehensive Approach to Counterfactual Explanation in Graph Neural Networks
Graph Neural Networks (GNNs) are increasingly adopted across domains such as molecular biology and social network analysis, yet their black-box nature hinders interpretability and trust. This is espec...
Formal Verification
Z3 checks whether the hypothesis is internally consistent, not whether it is empirically true.