From Quantum Computing Algorithms to Human Health Applications: Tunnelling through the Disciplinary Barriers
Ross Amory, J. David Brook, Shayantan Chaudhuri, Oliver Dowinton, Gian Gentinetta, Christopher J. Hayes, Jonathan Hirst, Bang C. Huynh, Katherine Inzani, G. Jones, Abhishek Khedkar, Tommaso Macrì, Luis Á. Martínez-Martínez, Lana Mineh, Ashley Montanaro, Stefan Ostermann, David M. Rogers, Alexander Schukert, Evan Sheridan, Rebecca C. Trueman
We describe a collaborative research project spanning the disciplines of quantum hardware, quantum algorithms, conventional computational chemistry, synthetic medicinal chemistry and life sciences. Our project seeks to demonstrate an impact of quantum computing on human health. It is one of several funded by Wellcome Leap as part of their Quantum for Bio Program. We aim to use quantum computing to guide the discovery of covalent inhibitors for the treatment of the rare disease myotonic dystrophy type I. Modelling the formation of a chemical bond in the context of the rest of the inhibitor and the protein is a challenge, because of the size of the systems of interest, the intricacies of the electronic structure where the importance of electron correlation may vary between reactant and transition states, and the extensive sampling of configurational space that is often required. We are developing an efficient hybrid quantum-classical framework to address the accuracy and scalability limitations of conventional computational drug design approaches. A quantum-enhanced density functional approximation is developed based on data from computation on neutral-atom quantum hardware, which can then be deployed within conventional quantum mechanics/molecular mechanics calculations. Our quantum-enhanced framework can be systematically updated as more capable quantum processors become available, heralding the prospect of using quantum-derived data to train improved density functional approximations and, ultimately, to support a wide range of drug design efforts.
We describe a collaborative research project spanning the disciplines of quantum hardware, quantum algorithms, conventional computational chemistry, synthetic medicinal chemistry and life sciences. Our project seeks to demonstrate an impact of quantum computing on human health. It is one of several funded by Wellcome Leap as part of their Quantum for Bio Program. We aim to use quantum computing to guide the discovery of covalent inhibitors for the treatment of the rare disease myotonic dystrophy type I. Modelling the formation of a chemical bond in the context of the rest of the inhibitor and the protein is a challenge, because of the size of the systems of interest, the intricacies of the electronic structure where the importance of electron correlation may vary between reactant and transition states, and the extensive sampling of configurational space that is often required. We are developing an efficient hybrid quantum-classical framework to address the accuracy and scalability limitations of conventional computational drug design approaches. A quantum-enhanced density functional approximation is developed based on data from computation on neutral-atom quantum hardware, which can then be deployed within conventional quantum mechanics/molecular mechanics calculations. Our quantum-enhanced framework can be systematically updated as more capable quantum processors become available, heralding the prospect of using quantum-derived data to train improved density functional approximations and, ultimately, to support a wide range of drug design efforts.
High Resolution Image Download MS PowerPoint Slide Targeted covalent inhibitors represent a promising class of drugs that form specific chemical bonds with their biological targets. There are a multitude of molecular systems where covalent inhibitors could address human health challenges. A high-level quantum chemical description of the formation of the critical covalent bond could provide new mechanistic detail and insights into how the mechanism is influenced by the surrounding biomolecular environment. However, accurately simulating the reactivity and binding specificity of such inhibitors remains a significant challenge. By leveraging advances in quantum computing hardware and algorithms, we discuss how quantum computing could benefit the design of targeted covalent inhibitors and enable more accurate simulations of protein–ligand interactions and accelerate de novo drug discovery.
Quantum computing, with its superior computational capabilities compared to classical approaches, holds the potential to revolutionize numerous scientific domains, including pharmaceuticals. However, the application of quantum computing for drug discovery has primarily been limited to proof-of-concept studies, which often fail to capture the intricacies of real-world drug development challenges. In this study, we diverge from conventional investigations by developing a hybrid quantum computing pipeline tailored to address genuine drug design problems. Our approach underscores the application of quantum computation in drug discovery and propels it towards more scalable system. We specifically construct our versatile quantum computing pipeline to address two critical tasks in drug discovery: the precise determination of Gibbs free energy profiles for prodrug activation involving covalent bond cleavage, and the accurate simulation of covalent bond interactions. This work serves as a pioneering effort in benchmarking quantum computing against veritable scenarios encountered in drug design, especially the covalent bonding issue present in both of the case studies, thereby transitioning from theoretical models to tangible applications. Our results demonstrate the potential of a quantum computing pipeline for integration into real world drug design workflows.
Quantum computing, with its superior computational capabilities compared to classical approaches, holds the potential to revolutionize numerous scientific domains, including pharmaceuticals. However, the application of quantum computing for drug discovery has primarily been limited to proof-of-concept studies, which often fail to capture the intricacies of real-world drug development challenges. In this study, we diverge from conventional investigations by developing an advanced quantum computing pipeline tailored to address genuine drug design problems. Our approach underscores the pragmatic application of quantum computation and propels it towards practical industrial adoption. We specifically construct our versatile quantum computing pipeline to address two critical tasks in drug discovery: the precise determination of Gibbs free energy profiles for prodrug activation involving covalent bond cleavage, and the accurate simulation of covalent bond interactions. This work serves as a pioneering effort in benchmarking quantum computing against veritable scenarios encountered in drug design, especially the covalent bonding issue present in both of the case studies, thereby transitioning from theoretical models to tangible applications. Our results demonstrate the potential of a quantum computing pipeline for integration into real world drug design workflows.
Quantum AI, the integration of quantum computing and artificial intelligence, is revolutionizing drug design by addressing the limitations of traditional pharmaceutical development. This review explores its transformative potential in enhancing molecular simulations, structure-based, target-based, and ligand-based drug discovery, and personalized medicine. Quantum computing leverages principles like superposition, entanglement, and interference to perform complex calculations exponentially faster than classical systems, enabling precise modeling of molecular interactions. Algorithms such as the Variational Quantum Eigensolver (VQE) and Quantum Phase Estimation (QPE) outperform classical methods like Density Functional Theory (DFT) in accuracy and efficiency, facilitating the identification of promising drug candidates and optimizing their pharmacokinetic properties. Quantum AI also enhances personalized medicine by analyzing vast genomic datasets to tailor treatments, improving efficacy and minimizing adverse effects. Despite challenges, including noisy intermediate-scale quantum (NISQ) device limitations and the need for advanced error correction, ongoing research and interdisciplinary collaboration are driving progress. Quantum AI promises to reduce development costs, accelerate drug discovery, and democratize access to life-saving medications. By enabling simulation-based discovery and expanding drug-candidate libraries to include peptides and antibodies, it paves the way for breakthroughs in treating complex diseases. This review underscores the need for continued investment in quantum technology to fully harness its potential, positioning the pharmaceutical industry for a future where drug development is faster, more precise, and patient-centric, ultimately improving global healthcare outcomes.
Scientific and technological advances in medicine and systems biology have unequivocally shown that health and disease must be viewed in the context of the interplay among multiple molecular and environmental factors. Understanding the effects of cellular interconnection on disease progression may lead to the identification of novel disease genes and pathways, and hence influence precision diagnostics and therapeutics. To accomplish this goal, the emerging field of network medicine applies network science approaches to investigate disease pathogenesis, integrating information from relevant Omics databases, including protein-protein interaction, correlation-based, gene regulatory, and Bayesian networks. However, this requires analysing and computing large amounts of data. Moreover, if we are to efficiently search for new drugs and new drug combinations, there is a pressing need for computational methods that could allow us to access the immense chemical compound space until now largely unexplored. Finally, at the microscopic level, drug-target chemistry simulation is ultimately a quantum problem, and hence it requires a quantum solution. As we will discuss, quantum computing may be a key ingredient in enabling the full potential of network medicine. We propose to combine network medicine and quantum algorithms in a novel research field, quantum network medicine, to lay the foundations of a new era of disease prevention and drug design.
Quantum chemistry plays a crucial role in advancing fundamental understanding of chemical processes and driving innovation in energy, medicine, and materials science. Recent progress in quantum computing has opened new possibilities for molecular simulations that are beyond the practical limits of classical approaches. Academic studies have demonstrated hybrid Density Functional Theory (DFT) and Variational Quantum Eigensolver (VQE) benchmarks on small transition-metal systems, such as iron porphyrin and heme analogues, using current noisy intermediate-scale quantum (NISQ) hardware. Although full-scale simulations of complex biological systems such as the complete Cytochrome P450 (CYP450) active site remain a long-term goal due to limitations in qubit numbers, coherence times, and error correction, the underlying methodologies are now well established. At present, quantum chemistry applications in drug discovery remain largely experimental, and widespread practical medical impact is expected to require further technological advances over the next 5–10 years. Nevertheless, quantum software and algorithm development for chemical applications is progressing rapidly. This perspective summarizes recent advances in quantum computing algorithms, hardware, and software relevant to chemistry, and critically discusses the remaining challenges and opportunities for applying quantum computing to chemical problems, with particular emphasis on drug discovery and development.