arXiv:2206.00551 · Journal of Chemical Theory and Computation
Perspective on the Current State-of-the-Art of Quantum Computing for Drug Discovery Applications
Nick S. Blunt, Joan Camps, Ophelia Crawford, Róbert Izsák, Sebastian Leontica, Arjun Mirani, Alexandra E. Moylett, Sam A. Scivier, Christoph Sünderhauf, Patrick Schöpf, Jacob M. Taylor, Nicole Holzmann
Computational chemistry is an essential tool in the pharmaceutical industry. Quantum computing is a fast evolving technology that promises to completely shift the computational capabilities in many areas of chemical research by bringing into reach currently impossible calculations. This perspective illustrates the near-future applicability of quantum computation of molecules to pharmaceutical problems. We briefly summarize and compare the scaling properties of state-of-the-art quantum algorithms and provide novel estimates of the quantum computational cost of simulating progressively larger embedding regions of a pharmaceutically relevant covalent protein-drug complex involving the drug Ibrutinib. Carrying out these calculations requires an error-corrected quantum architecture that we describe. Our estimates showcase that recent developments on quantum phase estimation algorithms have dramatically reduced the quantum resources needed to run fully quantum calculations in active spaces of around 50 orbitals and electrons, from estimated over 1000 years using the Trotterization approach to just a few days with sparse qubitization, painting a picture of fast and exciting progress in this nascent field.
Computational chemistry is an essential tool in the pharmaceutical industry. Quantum computing is a fast evolving technology that promises to completely shift the computational capabilities in many areas of chemical research by bringing into reach currently impossible calculations. This perspective illustrates the near-future applicability of quantum computation of molecules to pharmaceutical problems. We briefly summarize and compare the scaling properties of state-of-the-art quantum algorithms and provide novel estimates of the quantum computational cost of simulating progressively larger embedding regions of a pharmaceutically relevant covalent protein-drug complex involving the drug Ibrutinib. Carrying out these calculations requires an error-corrected quantum architecture that we describe. Our estimates showcase that recent developments on quantum phase estimation algorithms have dramatically reduced the quantum resources needed to run fully quantum calculations in active spaces of around 50 orbitals and electrons, from estimated over 1000 years using the Trotterization approach to just a few days with sparse qubitization, painting a picture of fast and exciting progress in this nascent field.
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.
Computational chemistry plays a vital role in material sciences. A fast evolving technology called quantum computing has shifted computational capabilities as it promises to solve impossible calculations in some areas of material sciences and drug development. In principle, quantum computers can solve certain problems in chemistry exponentially more quickly than classical computers. As quantum computers have an error-corrected quantum system, it reduces the quantum resources to run the quantum calculations in space drastically using the Trotterization approach with small qubitization. This nascent field is showing fast progress and potential benefits as it can process large amounts of information. The algorithms used for optimization and quantum simulations help to improve the advanced computational calculations for large clusters. These algorithms help in protein structure prediction to network analysis. Yet this nascent field has its own caveats and challenges. In this chapter, we introduce the basic architecture of quantum computing and its promises and limitations in the field of computational chemistry. The quantum algorithms revolutionizing quantum computing are discussed in detail.
Quantum computers are recently being developed in wide varieties, but the computational results from quantum computing have been largely confined to constructing artificial assignments. The applications of quantum computers to real-world problems are still an active area of research. However, challenges arise when the limits of scale and complexity in biological problems are pushed, which has affected drug discovery. The fast-evolving quantum computing technology has transformed the computational capabilities in drug research by searching for solutions for complicated and tedious calculations. Quantum computing (QC) is exponentially more efficient in drug discovery, treatment, and therapeutics, generating profitable business for the pharmaceutical industry. In principle, it can be stated that quantum computing can solve complex problems exponentially faster than classical computing. Here it is needed to mention that QC will not be able to take on every task that classical computers perform—at least not now. It may be classical and quantum-coupled computational technologies combined with machine learning (ML) and artificial intelligence (AI) will solve each task in the future. This review is an overview of quantum computing, which may soon revolutionize the pharmaceutical industry in drug discovery.
Quantum computers are recently being developed in wide varieties, but the computational results from quantum computing have been largely confined to constructing artificial assignments. The applications of quantum computers to real-world problems are still an active area of research. However, challenges arise when the limits of scale and complexity in biological problems are pushed, which has affected drug discovery. The fast-evolving quantum computing technology has transformed the computational capabilities in drug research by searching for solutions for complicated and tedious calculations. Quantum computing (QC) is exponentially more efficient in drug discovery, treatment, and therapeutics, generating profitable business for the pharmaceutical industry. In principle, it can be stated that quantum computing can solve complex problems exponentially faster than classical computing. Here it is needed to mention that QC will not be able to take on every task that classical computers perform—at least not now. It may be classical and quantum-coupled computational technologies combined with machine learning (ML) and artificial intelligence (AI) will solve each task in the future. This review is an overview of quantum computing, which may soon revolutionize the pharmaceutical industry in drug discovery.
INTRODUCTION: Quantum computing represents a transformative advancement in computational science, with applications in drug discovery, molecular interaction simulation, drug-target binding optimization, and the analysis of complex biological data at unprecedented speeds and accuracy. Quantum computing emerges as a powerful tool to accelerate the development of new therapeutics, drug design, and the simulation of complex chemical interactions, including personalized medicine strategies. The objective of this study is to explore the potential of quantum computing in drug discovery and development, highlighting its ability to reduce time and costs while accelerating the identification of promising drug candidates. METHODS: Quantum computing algorithms, such as Grover's algorithm and the Variational Quantum Eigensolver (VQE), are utilized to simulate molecular interactions of drugs and optimize drug design. In case studies, such as IBM's use of VQE for molecular simulations, these technologies demonstrate their effectiveness. RESULTS: Quantum computing has shown promise in addressing several technological barriers, such as lengthy development timelines and high costs. Additionally, demonstrated success in molecular simulations and solving challenges during the drug development process. However, challenges related to error rates, qubit coherence, and regulatory compliance remain. DISCUSSION: This study examines the applications of quantum computing in drug discovery, highlighting key techniques such as quantum simulation, quantum machine learning, and optimization algorithms. Quantum computing is crucial for interdisciplinary collaboration among quantum physicists, computational chemists, biologists, and pharmaceutical professionals, as it is essential to overcoming these obstacles and realizing the full potential of quantum technologies in medicine. CONCLUSION: Quantum computing holds great potential in drug discovery and development, offering accelerated, more accurate, and lower-cost research avenues, particularly in complex areas such as protein folding prediction and personalized medicine. This new paradigm has tremendous potential for guiding the future of pharmaceutical development and patient-focused medicine.
The journey to develop drugs is long, time-consuming, and costly.Most drugs take years and billions of dollars before they hit the market.Traditional computations remain useful but have inadequacies to accurately reproduce molecular interactions and predict drug efficacy.Quantum computing represents a new tool to potentially revolutionize the challenges brought about by traditional computations in that it can more quickly and accurately solve several of these problems through applying the principles of quantum mechanics.This review examines the possibility of quantum computing to transform pharmaceutical research and drug development.The aim of this review is to give the reader an all-round perspective on the use of quantum computing in drug discovery, with its contributions to molecular modeling, drug-target interaction prediction, and pharmacological property optimization.It promises something new in the application of quantum algorithms to make up for the faults of classical methods used in pharmaceutical research.This review critically synthesizes a number of relevant studies that applied quantum computing capabilities to drug design.Particular attention is brought to quantum algorithms, including but not limited to VQE (Variational Quantum Eigensolver) and QAOA (Quantum Approximate Optimization Algorithm), in determining molecular structures in silico along with the capacity to predict chemical binding affinities.Secondly, the scope of the drugs-discovery application on the various platforms such as IBM Q versus Google Sycamore were compared with current classical computational approach.The quantum computer showed a lot of promise in the simulation of complicated molecular interactions faster than the classical counterparts.A lot of studies emphasize the accuracy quantum simulations have towards the prediction of drug-target binding mechanisms, and this makes the lead optimization process more efficient.However, with scalability and hardware issues, its application in the pharmaceutical research community is still yet to be wide-scale.Quantum computing promises to accelerate the drug development process by improving the speed and accuracy of molecular modeling and drug optimization.Although the field is still in its infancy, its integration into pharmaceutical research could lead to significant reductions in development time and cost.This review underlines the transformative potential of quantum computing and suggests future research directions for overcoming the current limitations and realizing its full potential in precision medicine.