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arXiv:2602.15451

Molecular Design beyond Training Data with Novel Extended Objective Functionals of Generative AI Models Driven by Quantum Annealing Computer

Hayato Kunugi, Mohsen Rahmani, Yosuke Iyama, Yutaro Hirono, Akira Suma, Matthew Woolway, Vladimir Vargas-Calderón, William Kim, Kevin Chern, Mohammad Amin, Masaru Tateno

q-bio.QMcs.AIcs.LGquant-phSubmitted Feb 17, 2026• Announced Feb 18, 2026• 0 citations
v1 · Feb 17, 2026v2 · May 7, 2026v3 · May 12, 2026
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Abstract

Deep generative modeling to stochastically design small molecules is an emerging technology for accelerating drug discovery and development. However, one major issue in molecular generative models is their lower frequency of drug-like compounds. To resolve this problem, we developed a novel framework for optimization of deep generative models integrated with a D-Wave quantum annealing computer, where our Neural Hash Function (NHF) presented herein is used both as the regularization and binarization schemes simultaneously, of which the latter is for transformation between continuous and discrete signals of the classical and quantum neural networks, respectively, in the error evaluation (i.e., objective) function. The compounds generated via the quantum-annealing generative models exhibited higher quality in both validity and drug-likeness than those generated via the fully-classical models, and was further indicated to exceed even the training data in terms of drug-likeness features, without any restraints and conditions to deliberately induce such an optimization. These results indicated an advantage of quantum annealing to aim at a stochastic generator integrated with our novel neural network architectures, for the extended performance of feature space sampling and extraction of characteristic features in drug design.

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42 pages, 7 figures

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Deep generative modeling to stochastically design small molecules is an emerging technology for accelerating drug discovery and development. However, one major issue in molecular generative models is their lower frequency of drug-like compounds. To resolve this problem, we developed a novel framework for optimization of deep generative models integrated with a D-Wave quantum annealing computer, where our Neural Hash Function (NHF) presented herein is used both as the regularization and binarization schemes simultaneously, of which the latter is for transformation between continuous and discrete signals of the classical and quantum neural networks, respectively, in the error evaluation (i.e., objective) function. The compounds generated via the quantum-annealing generative models exhibited higher quality in both validity and drug-likeness than those generated via the fully-classical models, and was further indicated to exceed even the training data in terms of drug-likeness features, without any restraints and conditions to deliberately induce such an optimization. These results indicated an advantage of quantum annealing to aim at a stochastic generator integrated with our novel neural network architectures, for the extended performance of feature space sampling and extraction of characteristic features in drug design.
Deep generative modeling to stochastically design small molecules is an emerging technology for accelerating drug discovery and development. However, one major issue in molecular generative models is their lower frequency of drug-like compounds. To solve this problem, we develop a novel, quantum annealing, generative-model approach for optimizing deep generative models, an approach that includes integrating with a D-Wave annealing quantum computer. Of particular note, our neural hash function (NHF) is used simultaneously as a regularization scheme and binarization scheme, of which the latter is for transformation between continuous and discrete signals of the classical and quantum neural networks, respectively, in the error evaluation (i.e., objective) function. The compounds generated via the quantum-annealing generative models exhibit higher quality in both validity and drug-likeness than those generated via the fully-classical models, and even exceed the training data in terms of drug-likeness features, without any restraints and conditions to deliberately induce such an optimization. These results suggest that quantum annealing could be used as a stochastic generator integrated with our novel neural network architectures for extending the performance of feature space sampling and the extraction of characteristic features in drug design.
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The convergence of Quantum Computing and Generative Artificial Intelligence (AI) marks a transformative leap in the field of drug discovery. Traditional drug development pipelines are often slow, costly, and limited by classical computational constraints. This paper presents a novel hybrid approach—QuantumGeneraDrug—that integrates quantum generative models with deep learning architectures to accelerate molecule generation, optimize binding affinities, and reduce computation time. A comprehensive literature survey highlights the evolution of both quantum algorithms and generative AI models applied to pharmaceutical research. The proposed system demonstrates improved performance over existing frameworks in key metrics such as valid molecule rate, druglikeness (QED) score, binding affinity accuracy, and processing time per compound. Comparative results are illustrated using numerical tables and visualized via bar and pie charts. The study concludes that Quantum Generative AI holds immense promise for next-generation drug discovery, especially in tackling complex diseases and enabling personalized medicine.
Existing drug discovery pipelines take 5–10 years and cost billions of dollars. Computational approaches aim to sample from regions of the whole molecular and solid-state compounds called chemical space, which could be on the order of106010^{60}1060. Deep generative models can model the underlying probability distribution of both the physical structures and property of drugs and relate them nonlinearly. By exploiting patterns in massive datasets, these models can distill salient features that characterize the molecules. Generative adversarial networks (GANs) discover drug candidates by generating molecular structures that obey chemical and physical properties and show affinity toward binding with the receptor for a target disease. However, classical GANs cannot explore certain regions of the chemical space and suffer from training instabilities. The practical utility of such models is limited due to the vast size of the search space, characterized by millions of parameters. A full quantum GAN may require more than 90 qubits even to generate small molecules with up to nine heavy atoms. The proposed quantum GAN with a hybrid generator (QGAN-HG) model is composed of a hybrid quantum generator that supports various number of qubits and quantum circuit layers, and a classical discriminator. The QGAN-HG with less than 20% of the original parameters can learn molecular distributions as efficiently as its classical counterpart. Another extended version of the proposed QGAN-HG, which utilizes multiple quantum subcircuits, considerably accelerates our standard QGAN-HG training process and avoids the potential gradient vanishing issue of deep neural networks.
Abstract : We propose a hybrid quantum–classical framework that integrates variational quantum machine learning (QML) for molecular binding affinity prediction with quantum annealing for Pareto-optimal drug formulation design. Leveraging a four-qubit variational circuit, our QML model achieves superior predictive performance compared to state-of-the-art classical baselines, effectively capturing complex molecular interactions. In parallel, we formulate drug formulation optimization as a Quadratic Unconstrained Binary Optimization (QUBO) problem and solve it using D-Wave’s quantum annealer, enabling efficient identification of Pareto-optimal solutions that balance bioavailability, toxicity, and stability. By unifying predictive modeling and combinatorial optimization within a single pipeline, our framework demonstrates the potential of near-term quantum devices to accelerate and enhance critical stages of drug discovery. These findings underscore the transformative role of NISQ-era algorithms in bridging molecular informatics and formulation design, paving the way for scalable, data-driven pharmaceutical innovation.
Drug discovery for a given protein target is a complex, resource-intensive process, often requiring years of research and billions of dollars in investment. Computational techniques, particularly deep learning models, have accelerated molecular generation, but they remain constrained by classical computational limitations and reliance on known chemical patterns. To address these challenges, we propose a hybrid quantum conditional GAN with gradient penalty for drug design, integrating quantum computing to enhance exploration of the vast chemical space. The proposed approach leverages a hybrid classical and quantum generative model to create novel molecular structures while ensuring they adhere to chemical and pharmacological properties. By conditioning the generative model on protein target sequences, we improve molecular specificity, increasing the likelihood of discovering viable drug candidates. Our framework generates chemically valid, diverse, and target-specific molecules, offering a promising avenue for more efficient and precise data-driven drug discovery.