Quantum drug discovery: a hybrid quantum graph neural network–variational quantum eigensolver framework for serine neutralization
Abstract
Abstract Quantum computing has emerged as a powerful tool for modeling molecular systems with high accuracy, accelerating the search for novel therapeutic targets. In this study, we present a two-stage hybrid workflow that combines a Quantum Graph Neural Network with a Variational Quantum Eigensolver to identify potent serine neutralizers in the QM9 dataset. In the first stage, an advanced quantum graph neural network architecture incorporating attention layers, self-distillation, and an adaptive learning-rate schedule is trained to predict ionization potentials and binding free energies. Across five independent random-seed trials, our adaptive-thresholded QGNN–VQE pipeline achieves an average $$R^2$$ R 2 of $$0.990 \pm 0.008$$ 0.990 ± 0.008 and a mean absolute error of $$0.034 \pm 0.001$$ 0.034 ± 0.001 eV ( $$\approx 0.79 \pm 0.03$$ ≈ 0.79 ± 0.03 kcal/mol) on the QM9 validation set, demonstrating robust, chemical-accuracy-level predictions. Building on these high-precision predictions, the second stage employs a QAOA-inspired hybrid ranking scheme that merges quantum graph neural network outputs, feature-space similarity (via and cosine similarity), and variational quantum eigensolver-derived energy stability. This $$\alpha $$ α -weighted ( $$\alpha = 0.95$$ α = 0.95 ) scoring framework is applied to over 133,000 molecules, efficiently parsed in parallel. The final ranked list is validated through an improved bijection test, featuring an adaptive similarity threshold to…
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