A Hybrid GNN-QAOA Pipeline for Constrained Drug Repurposing Portfolio Selection with Topology-Aware Compilation on IBM Heavy-Hex Quantum Hardware
Year: 2026
Type: preprint
Abstract: Constrained portfolio selection in drug repurposing (choosing subsets of approved compounds that jointly satisfy interaction avoidance, toxicity ceilings, and target-coverage requirements) remains intractable for classical integer programming beyond ∼10,000 candidates, precisely the regime where combinatorial drug repositioning operates. Existing quantum formulations address pairwise drug-combination scoring but do not account for portfolio-level constraints or the mismatch between problem graph structure and physical qubit connectivity. We introduce a three-stage pipeline: a relational graph convolutional network (RGCN) narrows the candidate pool to ∼5,000 triples, a QUBO encoding extends the Complementary-Exposure framework [6], [36] with budget, diversity, and multitarget coverage penalties, and a topology-aware QAOA compiler maps constraint blocks onto natively connected heavy-hex qubit clusters. The compiler reduces two-qubit SWAP-gate counts by 23-41% relative to Qiskit optimization-level-3 transpilation on sub-problems of 20-100 qubits (Table IV), directly lowering accumulated gate error on IBM Heron r2 hardware. Retrospective validation on ten FDA-approved repurposed drugs yields 82.5% Recall@100 on a noiseless simulator versus 79.3% for ILP/Gurobi; on real IBM Heron hardware, recall drops to 69.8% (approximation ratio 0.64), falling 9.5 percentage points behind ILP and quantifying the noise gap that future errorcorrected processors must close. The central finding is negative: at current NISQ fidelities, classical ILP produces strictly superior solutions in less wall-clock time. The contribution is therefore architectural, not algorithmic; the topology-aware compilation strategy transfers directly to improved processors without redesign, positioning the pipeline for the p ≤ 10 −4 gate-error regime projected for 2028-2030. Code and QUBO encodings will be released upon acceptance.
Source: ChemRxiv
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7140819690