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Journal of Cheminformatics

MolGAN-QRL: a hybrid framework for molecule generation using quantum-enhanced reinforcement learning

Mohamed Iheb Hergli, Emna Harigua‐Souiai

Published Feb 2, 2026• 1 citation
Publisher

Abstract

Discovering novel drug candidates remains a considerable challenge in pharmaceutical research. Generative AI models such as Generative Adversarial Networks (GANs) have shown considerable promise in de novo molecular generation. They demonstrated high potential in drug discovery applications, yet they often face challenges such as limited chemical coverage and mode collapse. In the present study, we developed MolGAN-QRL, a hybrid quantum-classical framework that introduced quantum-enhanced reinforcement learning within the MolGAN architecture to address these limitations. The proposed framework leveraged a hybrid reward mechanism to further optimize chemical validity, uniqueness, and drug-likeliness of the generated molecules. Experimental results demonstrated that MolGAN-QRL consistently achieved enhanced generative performances compared to classical MolGAN, with up to a 16-fold increase in the count of unique and valid generated compounds under certain conditions. These gains reflected the effectiveness of quantum-guided exploration and highlighted the known trade-off between uniqueness and validity in generative chemistry. Overall, our findings underlined the value of quantum-enhanced reward modeling in mitigating mode collapse and advancing molecular generation, and support the potential of hybrid quantum-classical methods to advance generative chemistry for drug discovery applications. SCIENTIFIC CONTRIBUTION: MolGAN-QRL introduces the first variant of the MolGAN framework, that is augmented with a variational quantum circuit (VQC) within the reinforcement-learning reward module, rather than the generator, the discriminator or the noise function. It leverages a hybrid reward mechanism that trains a quantum-classical function that lead to better mitigation of mode-collapse, through higher uniqueness scores and 16-fold more novel, valid and unique molecules generated.

DOI
10.1186/s13321-025-01148-4

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Discovering novel drug candidates remains a considerable challenge in pharmaceutical research. Generative AI models such as Generative Adversarial Networks (GANs) have shown considerable promise in de novo molecular generation. They demonstrated high potential in drug discovery applications, yet they often face challenges such as limited chemical coverage and mode collapse. In the present study, we developed MolGAN-QRL, a hybrid quantum-classical framework that introduced quantum-enhanced reinforcement learning within the MolGAN architecture to address these limitations. The proposed framework leveraged a hybrid reward mechanism to further optimize chemical validity, uniqueness, and drug-likeliness of the generated molecules. Experimental results demonstrated that MolGAN-QRL consistently achieved enhanced generative performances compared to classical MolGAN, with up to a 16-fold increase in the count of unique and valid generated compounds under certain conditions. These gains reflected the effectiveness of quantum-guided exploration and highlighted the known trade-off between uniqueness and validity in generative chemistry. Overall, our findings underlined the value of quantum-enhanced reward modeling in mitigating mode collapse and advancing molecular generation, and support the potential of hybrid quantum-classical methods to advance generative chemistry for drug discovery applications. SCIENTIFIC CONTRIBUTION: MolGAN-QRL introduces the first variant of the MolGAN framework, that is augmented with a variational quantum circuit (VQC) within the reinforcement-learning reward module, rather than the generator, the discriminator or the noise function. It leverages a hybrid reward mechanism that trains a quantum-classical function that lead to better mitigation of mode-collapse, through higher uniqueness scores and 16-fold more novel, valid and unique molecules generated.
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.
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Drug discovery is a notoriously lengthy, costly, and often unsuccessful process. Traditional methods rely heavily on high-throughput screening and iterative optimization, which are computationally intensive and time-consuming. This research investigates the potential of Quantum Generative Adversarial Networks (QGANs) to significantly accelerate this process by learning efficient molecular representations and generating novel drug candidates. QGANs utilize quantum superposition to explore a vast chemical space, offering a fundamentally different approach compared to classical generative models. We propose a framework where a quantum generator network learns to create molecular structures, while a classical discriminator network evaluates their properties against desired criteria. The core innovation lies in the application of quantum computing principles to molecular representation learning, aiming to drastically reduce the computational burden associated with generating and evaluating potential drug candidates. The resulting molecular representations are then used to guide the generation of new compounds with optimized characteristics, addressing a critical bottleneck in the drug discovery pipeline. The framework presented herein provides a theoretical foundation and outlines a potential approach for leveraging quantum computing to revolutionize drug discovery.
The pharmaceutical industry faces mounting challenges in drug discovery, where exploring a chemical space exceeding 10 60 possible compounds remains computationally prohibitive. Traditional in-silico screening approaches struggle to capture the quantum mechanical interactions that determine molecular behavior. To overcome these limitations, this study introduces a hybrid quantum–classical machine learning framework that uniquely integrates molecular graph theory with quantum-enhanced learning for accelerated drug discovery. Using the QM9 benchmark dataset containing 133,885 molecules of low-to-medium structural complexity, we extract Laplacian eigenvalue spectra as topological descriptors that preserve both connectivity and electronic information. These features are jointly processed by Random Forest (RF), Support Vector Machine (SVM), and a Variational Quantum Classifier (VQC) in a weighted ensemble architecture designed for balanced generalization. The Quantum Graph Neural Network (QGNN) component achieves 97.5% accuracy, while the hybrid ensemble attains 100.0% accuracy, confirming strong predictive reliability. Although quantum training requires roughly 3.2× longer computation, it captures high-dimensional correlations that classical methods overlook. Overall, the proposed framework bridges the gap between theoretical quantum computing and practical pharmaceutical modeling, providing a scalable, physics-inspired pathway toward intelligent molecular screening and next-generation drug discovery systems.
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.
The drug design process currently requires considerable time and resources to develop each new compound that enters the market. This work develops an application of hybrid quantum generative models based on the integration of parameterized quantum circuits into known molecular generative adversarial networks and proposes quantum cycle architectures that improve model performance and stability during training. Through extensive experimentation on benchmark drug design datasets, quantum machine 9 (QM9) and PubChemQC 9 (PC9), the introduced models are shown to outperform the previously achieved scores. Most prominently, the new scores indicate an increase of up to 30% in the quantitative estimation of druglikeness. The new hybrid quantum machine learning algorithms, as well as the achieved scores of pharmacokinetic properties, contribute to the development of fast and accurate drug discovery processes.