Abstract Computer-aided design of novel molecules and compounds is a challenging task that can be addressed with quantum computing (QC) owing to its notable advances in optimization and machine learning. Here, we use QC-assisted learning and optimization techniques implemented with near-term QC devices for molecular property prediction and generation tasks. The proposed probabilistic energy-based deep learning model trained in a generative manner facilitated by QC yields robust latent representations of molecules, while the proposed data-driven QC-based optimization framework performs guided navigation of the target chemical space by exploiting the structure–property relationships captured by the energy-based model. We demonstrate the viability of the proposed molecular design approach by generating several molecular candidates that satisfy specific property target requirements. The proposed QC-based methods exhibit an improved predictive performance while efficiently generating novel molecules that accurately fulfill target conditions and exemplify the potential of QC for automated molecular design, thus accentuating its utility.
Abstract Computer-aided design of novel molecules and compounds is a challenging task that can be addressed with quantum computing (QC) owing to its notable advances in optimization and machine learning. Here, we use QC-assisted learning and optimization techniques implemented with near-term QC devices for molecular property prediction and generation tasks. The proposed probabilistic energy-based deep learning model trained in a generative manner facilitated by QC yields robust latent representations of molecules, while the proposed data-driven QC-based optimization framework performs guided navigation of the target chemical space by exploiting the structure–property relationships captured by the energy-based model. We demonstrate the viability of the proposed molecular design approach by generating several molecular candidates that satisfy specific property target requirements. The proposed QC-based methods exhibit an improved predictive performance while efficiently generating novel molecules that accurately fulfill target conditions and exemplify the potential of QC for automated molecular design, thus accentuating its utility.
Quantum computing plays a significant role in simulating molecules and atoms and offers advantages in chemistry over classical computing. The potential of Quantum Machine Learning (QML) can be used in drug discovery, chemical reaction simulations, and Material design for pharmaceuticals. QML leverages quantum computing and advanced machine learning to accelerate the identification of drug candidates, predict molecular interactions, and optimize compounds. In this paper, we present a systematic review of the methods used for molecular property prediction and molecular generation using quantum machine learning. We have included the recent research, perspective, advantages, and challenges that must be addressed to achieve this task. The objective of this research is not only to discuss current strategies and methods used for drug discovery but also to promote interdisciplinary research in the field of quantum computing and chemistry for wellness.
Machine learning has made significant contributions to the fields of chemistry and materials science, enabling the exploration of vast chemical space through large-scale quantum chemical calculations. These models provide fast and accurate predictions of atomistic chemical properties, but they have limitations when it comes to capturing the electronic degrees of freedom of a molecule. This restricts their application in reactive chemistry and chemical analysis. To address this limitation, we introduce a deep learning framework that predicts the quantum mechanical wavefunction of a molecule in a local basis of atomic orbitals. The wavefunction serves as a foundational representation from which all other ground-state properties can be derived. Our approach maintains complete access to the electronic structure through the wavefunction, while achieving computational efficiency comparable to force-field methods. Moreover, the framework captures quantum mechanics in a form that can be analytically differentiated, allowing for efficient optimization and exploration of chemical systems. We demonstrate the potential of our approach through several examples. By leveraging the predicted wavefunction, we showcase the ability to perform inverse design of molecular structures to target specific electronic property optimizations. This opens exciting avenues for tailoring molecular structures to achieve desired electronic characteristics. Additionally, our framework paves the way for enhanced synergy between machine learning and quantum chemistry, enabling more comprehensive investigations into complex chemical systems.
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.
Quantum Machine Learning (QML) offers a transformative approach to drug design and optimization, addressing longstanding challenges in identifying novel drug candidates and refining existing ones. This paper explores the potential of QML to accelerate the drug discovery process by leveraging quantum algorithms for molecular structure exploration and optimization. We present a novel framework incorporating quantum simulation and machine learning, demonstrating its efficacy in predicting drug-target interactions and optimizing molecular properties. The core mechanism centers around accelerating the identification of promising compounds through enhanced molecular search and optimization, while quantum computational modeling provides unprecedented precision. The integration of quantum machine learning with traditional drug design methods promises to significantly reduce development time and costs, ultimately impacting the pharmaceutical industry.
The generation of chemical molecular structures is crucial for advancements in drug design, materials science, and related fields. With the rise of artificial intelligence, numerous generative models have been developed to propose promising molecular structures to specific challenges. However, exploring the vast chemical space using classical generative models demands extensive chemical structure data, considerable computational resources, and a large number of model parameters, which hinders their efficiency. Quantum computing presents a promising alternative by exploiting quantum parallelism and entanglement, potentially reducing the computational overhead required for such tasks. In this study, we designed a quantum-based molecular generator (QMG) specifically tailored to generate small molecules containing carbon, nitrogen, and oxygen atoms. This model imposes strict constraints on the quantum circuit's output quantum states, significantly eliminating mathematically invalid connection graphs and enabling more efficient sampling of valid molecular structures. Remarkably, our quantum circuit, utilizing only 134 parameters, is capable of enumerating all molecular structures comprising up to nine heavy atoms, showcasing the parameter efficiency achievable through quantum superposition and entanglement. Our experimental results show that the output generated by this circuit exhibits a high degree of validity and uniqueness after Bayesian optimization, showing comparable performance to classical deep generative models. Furthermore, by fixing specific parameters in the quantum circuits, the quantum generator can constrain the chemical space and exclusively generate chemical molecules containing specified functional groups. This feature underscores its potential value for targeted applications in specific domains, especially in drug discovery. Overall, this compact design not only reduces parameter demands but also enables efficient exploration of a nontrivial portion of chemical space, demonstrating a key advantage of quantum-based generative models over larger 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.