Authors: Javier Villalba-Díez
Source: The European Physical Journal D (article, 2025-07-01)
Field: Materials Science — Materials Chemistry

Quantum drug discovery: a hybrid quantum graph neural network–variational quantum eigensolver framework for serine neutralization 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 $$R^2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>R</mml:mi> <mml:mn>2</mml:mn> </mml:msup> </mml:math> of $$0.990 \pm 0.008$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>0.990</mml:mn> <mml:mo>±</mml:mo> <mml:mn>0.008</mml:mn> </mml:mrow> </mml:math> and a mean absolute error of $$0.034 \pm 0.001$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>0.034</mml:mn> <mml:mo>±</mml:mo> <mml:mn>0.001</mml:mn> </mml:mrow> </mml:math> eV ( $$\approx 0.79 \pm 0.03$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>≈</mml:mo> <mml:mn>0.79</mml:mn> <mml:mo>±</mml:mo> <mml:mn>0.03</mml:mn> </mml:mrow> </mml:math> 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 $$\alpha $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>α</mml:mi> </mml:math> -weighted ( $$\alpha = 0.95$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>α</mml:mi> <mml:mo>=</mml:mo> <mml:mn>0.95</mml:mn> </mml:mrow> </mml:math> ) 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 ensure both structural and functional robustness. The top-scoring candidate identified by our pipeline is 5,6,7-tetrahydro-4 H-pyrazolo[4,3-c]pyridin-4-one , highlighting the efficacy of quantum-enhanced modeling in pinpointing complex pharmacological targets. These findings underscore the transformative potential of hybrid quantum-classical workflows in drug discovery, offering a flexible blueprint for advanced screening of other critical biomolecular interactions in the rapidly expanding field of quantum biology. GraphicAbstract VQE for Serine Neutralization Quantum ; Serine ; Computer science ; Chemistry ; Physics ; Quantum mechanics ; Organic chemistry Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Computational Drug Discovery Methods