Perspective on the Current State-of-the-Art of Quantum Computing for Drug Discovery Applications

Blunt, Nick S.; Camps, Joan; Crawford, Ophelia; Leontica, Sebastian; Mirani, Arjun; Moylett, Alexandra E.; Scivier, Sam A.; Sünderhauf, Christoph; Schöpf, Patrick; Holzmann, Nicole

doi:10.1021/acs.jctc.2c00574

Cited by 76 publications

(33 citation statements)

References 162 publications

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“…Overview of quantum computing Quantum computing is a relatively new eld in computer science that aims to develop computer systems based on quantum mechanics principles. It operates differently from classical computers by using quantum bits (qubits) instead of binary digits (bits) [31] . Unlike classical computers, qubits can be in multiple states at once, which allows quantum computers to solve certain problems much faster than classical computers.…”

Section: Methodsmentioning

confidence: 99%

Leveraging Quantum Annealing for Ligand Modelling in Drug Discovery

Shetty

Joshi

Mehta

et al. 2023

Preprint

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Drug discovery is an intricate and multifaceted process that necessitates the identification and development of novel medications to combat various illnesses. This convoluted procedure typically encompasses various stages, including fundamental research, preclinical research, clinical research, and FDA approval. Notwithstanding the indispensability of the drug discovery process, it is time-consuming and exorbitant, with low success rates often being the norm. In this paper, we endeavour to provide a comprehensive appraisal of the divergent methodologies employed in drug discovery, inclusive of the wet lab approach and the classical computer-based approach. The wet lab approach requires extensive experimentation within laboratory settings to pinpoint potential drug candidates, whereas the classical computer-based approach employs computational techniques to simulate and prognosticate the properties of potential drug compounds. Despite the merits of both approaches, they are not without limitations, which we shall delve into in the course of this discourse. The multifariousness of the drug discovery process and the sheer volume of data generated during the course of experimentation necessitate the use of advanced technologies and algorithms in enhancing the process's performance. This paper aims to furnish an overview of the present state of drug discovery, while also underscoring the need for relentless research and innovation in this domain.

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Section: Methodsmentioning

confidence: 99%

Leveraging Quantum Annealing for Ligand Modelling in Drug Discovery

Shetty

Joshi

Mehta

et al. 2023

Preprint

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“…Government investment in quantum computing is all but assured across the next decade due to the potential for quantum computers to break existing encryptions standards (National Academies of Sciences, Engineering, and Medicine, 2019). Although it may seem like there is no room for yeast research to align with quantum computing, it should be emphasised that one of the dominant areas of early application for quantum computing has indeed been in the life sciences, with key applications in drug discovery and design (Blunt et al, 2022; Cao et al, 2018). As quantum computers increase in complexity and scale their ability to model larger systems will rapidly improve.…”

Section: The Bigger the Challeneges The Bigger The Opportunitiesmentioning

confidence: 99%

Visioning synthetic futures for yeast research within the context of current global techno‐political trends

Dixon,

Walker,

Pretorius

2023

Yeast

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Yeast research is entering into a new period of scholarship, with new scientific tools, new questions to ask and new issues to consider. The politics of emerging and critical technology can no longer be separated from the pursuit of basic science in fields, such as synthetic biology and engineering biology. Given the intensifying race for technological leadership, yeast research is likely to attract significant investment from government, and that it offers huge opportunities to the curious minded from a basic research standpoint. This article provides an overview of new directions in yeast research with a focus on Saccharomyces cerevisiae, and places these trends in their geopolitical context. At the highest level, yeast research is situated within the ongoing convergence of the life sciences with the information sciences. This convergent effect is most strongly pronounced in areas of AI‐enabled tools for the life sciences, and the creation of synthetic genomes, minimal genomes, pan‐genomes, neochromosomes and metagenomes using computer‐assisted design tools and methodologies. Synthetic yeast futures encompass basic and applied science questions that will be of intense interest to government and nongovernment funding sources. It is essential for the yeast research community to map and understand the context of their research to ensure their collaborations turn global challenges into research opportunities.

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“…Therefore, to understand the potential utility of quantum computers in tackling truly relevant chemical problems, it is crucial to investigate the ability of VQE to accurately describe more complex systems and to predict relative chemical properties, particularly those related to reaction mechanisms. [68][69][70][71] In this work, we introduce a new benchmark data set derived from quantum chemical modeling. This set consists of medium-sized molecules and chemical properties obtained from energy differences, thereby providing sufficient complexity that is of true interest to chemists.…”

Section: Introductionmentioning

confidence: 99%