A Hybrid Quantum-Classical Workflow for Early-Stage Molecular Discovery: A Workflow-Level Proof-of-Concept

Charnelle Razo (Department of Informatics and Analytics, National University of Science and Technology, Zimbabwe)
Belinda Ndlovu (Department of Informatics and Analytics, National University of Science and Technology, Zimbabwe)

Scientific Journal of Informatics
Vol. 13 No. 3: August 2026

Purpose: Classical computational approaches struggle to model molecular quantum interactions, making drug discovery costly and time-consuming. Quantum computing shows promise in molecular modelling, but much of the existing work focuses on isolated proof-of-concept tasks. These studies usually do not examine the full molecular discovery process. This study demonstrates the feasibility of a unified hybrid quantum–classical workflow for early-stage molecular discovery and examines how quantum-based molecular representations influence later optimisation and prediction. Methods/Study design/approach: The study developed and evaluated a five-stage hybrid quantum-classical workflow integrating query interpretation, molecular screening with quantum re-ranking, VQE-based energy estimation,  property prediction and report generation. The system was implemented using Qiskit, Qiskit Nature, RDKit, and spaCy on the QM9 dataset using noiseless state-vector simulation. Result/Findings: The NLP component was evaluated across four tiers: 180 template-based queries, 160 independently written queries, 1,000 independently written queries, and 1,000 externally sourced BioASQ Task B questions. F1-scores were 89.9%, 82.1%, 73.0%, and 71.3%. Screening achieved constraint satisfaction rates of 90%-100%. Quantum-informed ranking differed substantially from cosine similarity and evaluated the utility of this through top-k enrichment and multi-property hit rates. MIFM achieved a mean VQE absolute error of 0.0156 Ha compared with 0.0218 Ha for ZZFeatureMap and 0.0248 Ha for cosine similarity. VQE and property-prediction evaluations, scalability experiments, and simulated NISQ tests provided broader evidence of workflow behaviour. Novelty/Originality/Value: This study presents a reproducible hybrid quantum–classical workflow for early-stage molecular discovery, extended with a chemistry-aware MolecularInteractionFeatureMap (MIFM). The study does not claim universal quantum advantage or physical quantum-hardware validation.