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arXiv:2212.07826 · Quantum Machine Intelligence

Quantum-classical generative models for drug design

Prateek Jain, Param Pathak, Krishna Bhatia, Shalini Devendrababu, Srinjoy Ganguly

Published Mar 16, 2026• 7 citations
arXivHTMLPDF

Abstract

Abstract In molecular research, the modelling and analysis of molecules through simulation is an important part that has a direct influence on medical development, material science and drug discovery. The processing power required to design protein chains with hundreds of peptides is huge. Classical computing techniques, including state-of-the-art machine learning models being deployed on classical computing machines, have proven to be inefficient in this task, though they have been successful in a limited way. Moreover, current practical implementations, as opposed to purely theoretical modelling, are often infeasible in terms of both time and cost. One of the major areas where quantum machine learning is expected to have a profound advantage over classical algorithms is drug discovery. Quantum generative models have given some promising benefits in recent studies. This paper introduces three novel quantum generative adversarial network (QGAN) architecture variants resulting from different configurations, various quantum circuit layers and patched ansatz. A quantum simulator from Xanadu’s PennyLane was utilized for executing the QGAN models trained on the QM9 dataset. Upon evaluation, one of the models, namely the QWGAN-HG-GP (Wasserstein distance with gradient penalty) model, outperformed the other QGAN models in different drug molecule property metrics.

DOI
10.1007/s42484-026-00356-x

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Abstract In molecular research, the modelling and analysis of molecules through simulation is an important part that has a direct influence on medical development, material science and drug discovery. The processing power required to design protein chains with hundreds of peptides is huge. Classical computing techniques, including state-of-the-art machine learning models being deployed on classical computing machines, have proven to be inefficient in this task, though they have been successful in a limited way. Moreover, current practical implementations, as opposed to purely theoretical modelling, are often infeasible in terms of both time and cost. One of the major areas where quantum machine learning is expected to have a profound advantage over classical algorithms is drug discovery. Quantum generative models have given some promising benefits in recent studies. This paper introduces three novel quantum generative adversarial network (QGAN) architecture variants resulting from different configurations, various quantum circuit layers and patched ansatz. A quantum simulator from Xanadu’s PennyLane was utilized for executing the QGAN models trained on the QM9 dataset. Upon evaluation, one of the models, namely the QWGAN-HG-GP (Wasserstein distance with gradient penalty) model, outperformed the other QGAN models in different drug molecule property metrics.
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.
Current drug discovery processes can cost billions of dollars and usually take five to ten years. People have been researching and implementing various computational approaches to search for molecules and compounds from the chemical space, which can be on the order of 1060 molecules. One solution involves deep generative models, which are artificial intelligence models that learn from nonlinear data by modeling the probability distribution of chemical structures and creating similar data points from the trends it identifies. These generative models can extract salient features that characterize the molecules. However, they often suffer from increased time complexity. Aiming for faster runtime and greater robustness when analyzing high-dimensional data, we designed and implemented a Hybrid Quantum-Classical Generative Adversarial Network (QGAN) to synthesize molecules. There are two parts in the QGAN: a quantum generator that creates molecules based on the probability distributions of likely combinations and a classical discriminator that differentiates real molecules from the generated ones. We hypothesized that a quantum generator would be more impactful, offering a runtime speedup and increasing generated molecule possibilities, because we could use quantum mechanical phenomena superposition, entanglement, and interference to analyze more atom-bond combinations than a classical generator. The PyTorch-PennyLane implementation of the QGAN generated seven chemically stable molecules out of 300, a 2.3% success rate. Although the QGAN is a work in progress, it has demonstrated a path towards more efficient drug development, which would accelerate the development of medicines and reduce costs for the whole R&D process.
De novo drug design with desired biological activities is crucial for developing novel therapeutics for patients. The drug development process is time- and resource-consuming, and it has a low probability of success. Recent advances in machine learning and deep learning technology have reduced the time and cost of the discovery process and therefore, improved pharmaceutical research and development. In this paper, we explore the combination of two rapidly developing fields with lead candidate discovery in the drug development process. First, artificial intelligence has already been demonstrated to successfully accelerate conventional drug design approaches. Second, quantum computing has demonstrated promising potential in different applications, such as quantum chemistry, combinatorial optimizations, and machine learning. This article explores hybrid quantum-classical generative adversarial networks (GAN) for small molecule discovery. We substituted each element of GAN with a variational quantum circuit (VQC) and demonstrated the quantum advantages in the small drug discovery. Utilizing a VQC in the noise generator of a GAN to generate small molecules achieves better physicochemical properties and performance in the goal-directed benchmark than the classical counterpart. Moreover, we demonstrate the potential of a VQC with only tens of learnable parameters in the generator of GAN to generate small molecules. We also demonstrate the quantum advantage of a VQC in the discriminator of GAN. In this hybrid model, the number of learnable parameters is significantly less than the classical ones, and it can still generate valid molecules. The hybrid model with only tens of training parameters in the quantum discriminator outperforms the MLP-based one in terms of both generated molecule properties and the achieved KL divergence. However, the hybrid quantum-classical GANs still face challenges in generating unique and valid molecules compared to their classical counterparts.
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.
Contemporary drug discovery and development processes require billions of dollars and lengthy amounts of time, which is why researchers are utilizing computational chemistry methods like machine learning to speed up molecular synthesis pathways. Generative Adversarial Networks, in particular, have shown immense potential for accelerating the drug discovery pipeline. However, these models consume a significant amount of computational resources, as they must perform adversarial training on expansive datasets to accurately sample the vast chemical space. Aiming for more efficient runtimes and rigorous analyses of high-dimensional molecular data with quantum computing, we previously developed a hybrid quantum-classical generative adversarial network (QGAN) to generate potential small organic drug candidates. Later, inspired by the classical NetGAN, we upgraded QGAN to a hybrid quantum-classical graph generative adversarial network (QNetGAN) which generated graph structures via random walks. This model was more efficient and had a higher accuracy rate (47.0%) than QGAN (2.3%). Most recently, we developed QNetGAN v2, an upgraded version of QNetGAN that features graph convolution. This model was able to generate 273 out of 300 structurally valid molecules that satisfy the octet rule and Lipinski's Rule of Five, yielding an 91.0% success rate and a training time of 35 minutes. This was made possible by integrating several post-processing algorithms into the original QNetGAN model, including the octet rule satisfaction validator, hydrogen addition algorithm, formal charge calculation, and molecular geometry (.XYZ file coordinate) calculation. In the future, we plan to use the QNetGAN v2 pathway to generate molecules with a wider range of elements and add functionality to calculate important molecular properties, ideally developing a third and more capable version of the QNetGAN molecular generation pathway that is able to outperform the accuracy of classical NetGAN.