Toward the institutionalization of quantum computing in pharmaceutical research.

Innovative pharmaceutical companies have started to explore quantum computing (QC). In this article, we provide a collective industry perspective from QC domain leaders at leading pharmaceutical companies. There are immediate nonfinancial benefits in engaging with QC, some likely financial returns in the short term in drug development, manufacturing, and supply chain, and potentially large scientific benefits in drug discovery long term. We discuss the required activities for institutionalizing QC: how to create an understanding of QC among researchers and management, which and how to deploy external resources, and how to identify the problems to be addressed with QC. If (and once) deployable, QC will likely have a similar trajectory to that of computer-aided drug design (CADD) and artificial intelligence (AI) during the 1990s and 2010s, respectively.

Zinner M ，Dahlhausen F ，Boehme P ，Ehlers J ，Bieske L ，Fehring L ... -  《-》

Artificial Intelligence and Quantum Computing as the Next Pharma Disruptors.

Artificial intelligence (AI) consists of a synergistic assembly of enhanced optimization strategies with wide application in drug discovery and development, providing advanced tools for promoting cost-effectiveness throughout drug life cycle. Specifically, AI brings together the potential to improve drug approval rates, reduce development costs, get medications to patients faster, and help patients complying with their treatments. Accelerated pharmaceutical development and drug product approval rates can further benefit from the quantum computing (QC) technology, which will ultimately enable larger profits from patent-protected market exclusivity.Key pharma stakeholders are endorsing cutting-edge technologies based on AI and QC , covering drug discovery, preclinical and clinical development, and postapproval activities. Indeed, AI-QC applications are expected to become standard in the pharma operating model over the next 5-10 years. Generalizing scalability to larger pharmaceutical problems instead of specialization is now the main principle for transforming pharmaceutical tasks on multiple fronts, for which systematic and cost-effective solutions have benefited in areas such as molecular screening, synthetic pathway design, and drug discovery and development.The information generated by coupling the life cycle of drugs and AI and/or QC through data-driven analysis, neural network prediction, and chemical system monitoring will enable (1) better understanding of the complexity of process data, (2) streamlining the design of experiments, (3) discovering new molecular targets and materials, and also (4) planning or rethinking upcoming pharmaceutical challenges The power of AI-QC makes accessible a range of different pharmaceutical problems and their rationalization that have not been previously addressed due to a lack of appropriate analytical tools, demonstrating the breadth of potential applications of these emerging multidimensional approaches. In this context, creating the right AI-QC strategy often involves a steep learning path, especially given the embryonic stage of the industry development and the relative lack of case studies documenting success. As such, a comprehensive knowledge of the underlying pillars is imperative to extend the landscape of applications across the drug life cycle.The topics enclosed in this chapter will focus on AI-QC methods applied to drug discovery and development, with emphasis on the most recent advances in this field.

Cova T ，Vitorino C ，Ferreira M ，Nunes S ，Rondon-Villarreal P ，Pais A ... -  《-》

Quantum computing's potential for drug discovery: Early stage industry dynamics.

Quantum computing (QC) is expected to revolutionize drug research by performing tasks classical supercomputers are not capable of. However, practically useful quantum computation is not yet a reality, and thus it is still unclear when and whether QC will be capable of solving real-world issues in drug discovery. By identifying the QC-related activities of pharmaceutical companies, startups, and academia in the field of drug discovery and development, we show that QC has gained traction across all of these stakeholder groups, that there is focus on developing utilities related to lead optimization and compound screening, and that there is a need for collaboration in the highly dynamic QC ecosystem.

Zinner M ，Dahlhausen F ，Boehme P ，Ehlers J ，Bieske L ，Fehring L ... -  《-》

The Future of Drug Development with Quantum Computing.

Novel medication development is a time-consuming and expensive multistage procedure. Recent technology developments have lowered timeframes, complexity, and cost dramatically. Current research projects are driven by AI and machine learning computational models. This chapter will introduce quantum computing (QC) to drug development issues and provide an in-depth discussion of how quantum computing may be used to solve various drug discovery problems. We will first discuss the fundamentals of QC, a review of known Hamiltonians, how to apply Hamiltonians to drug discovery challenges, and what the noisy intermediate-scale quantum (NISQ) era methods and their limitations are.We will further discuss how these NISQ era techniques can aid with specific drug discovery challenges, including protein folding, molecular docking, AI-/ML-based optimization, and novel modalities for small molecules and RNA secondary structures. Consequently, we will discuss the latest QC landscape's opportunities and challenges.

Bonde B ，Patil P ，Choubey B 《-》

Quantum computing for near-term applications in generative chemistry and drug discovery.

In recent years, drug discovery and life sciences have been revolutionized with machine learning and artificial intelligence (AI) methods. Quantum computing is touted to be the next most significant leap in technology; one of the main early practical applications for quantum computing solutions is predicted to be in quantum chemistry simulations. Here, we review the near-term applications of quantum computing and their advantages for generative chemistry and highlight the challenges that can be addressed with noisy intermediate-scale quantum (NISQ) devices. We also discuss the possible integration of generative systems running on quantum computers into established generative AI platforms.

Pyrkov A ，Aliper A ，Bezrukov D ，Lin YC ，Polykovskiy D ，Kamya P ，Ren F ，Zhavoronkov A ... -  《-》