Quantum computer simulates largest molecule yet, sparking hope of future drug discoveries

Bhushan K. Bonde, Pratik Patil, Bhaskar Choubey

Yashraj Mukherjee, Dr. Praveen Pathak, Shweta Sinha

Discovery of drugs is one the time consuming and resource intensive process in the pharmaceutical industry since it takes years of research and investments. Discovery of new drugs is crucial for preserving human health and eradication of diseases. Modern drug discovery processes like CADD relies on Traditional Computing which is constrained by its raw computing power because it struggles to simulate complex interactions between biomolecules. Therefore, pharmaceutical industry can be one of the largest beneficiaries of quantum technology pushing boundaries of innovation. Quantum Computing can accelerate the drug discovery process and execute them within few minutes to hours which would require millions of years on today’s fastest supercomputer. This paper explains Quantum Computing basics, principles of quantum computing, algorithms used in quantum computing like Shor, Grover’s etc., Process of traditional drug discovery, limitations of CADD, Quantum Simulations, Virtual Screening, Molecular Docking and how Quantum Technology speeds up drug discovery process. It also addresses challenges regarding quantum technologies such as regulatory compliances, hardware limitations, cost & accessibility etc. and provides recent breakthroughs that demonstrates the application of quantum computing in drug discovery. Note that some very important terminologies related to quantum computing which are not mentioned in detail are mentioned in brief in our glossary section.

Bancha Yingngam, Alex Khang

The drug discovery process necessitates a significant investment of time and resources. Typically, over a decade and billions of dollars are required to bring a single drug to market. However, quantum computing may provide a solution to these challenges. It not only has the potential to streamline drug discovery but also to enhance the quality and accuracy of the results. This chapter explores the innovative amalgamation of quantum computing and pharmaceutical research. Traditional drug discovery methods, due to their time-consuming and costly nature, face numerous obstacles. Quantum computing, armed with its extraordinary computational power, could surmount these hurdles by providing efficient and precise simulations of drug molecules. This could, in turn, expedite the discovery of effective drug candidates. The chapter delves into the fundamentals of quantum computing, its application in drug discovery, and specific techniques relevant to this field. It also presents case studies of successful applications and discusses prospects, taking current limitations into account. A particularly exciting development in this field is quantum machine learning, a fusion of quantum mechanics and machine learning. This synergy could accelerate scientific advances by offering robust predictive modeling for drug discovery. Consequently, this chapter provides an in-depth exploration of a potential future in which quantum technology, artificial intelligence, and robotics are fully integrated. Such integration could revolutionize drug discovery, heralding a new era in health care and reshaping the boundaries of medical science and therapeutic development.

Adhira Tippur

Peihua Wang, Jen‐Hao Chen, Yu-Yuan Yang +2 more

Drug discovery and development is a time-consuming and cost-intensive process. Computer-aided drug design can speed up the timeline and reduce costs by decreasing the number of necessary biochemical experiments. The number of studies using quantum computing to solve problems in drug development has been increasing in recent years. In this review, we briefly introduce the main steps in drug discovery and development and how computers help to find potential drug candidates. Recent studies of quantum computing in drug development based on the structure of target proteins are listed chronologically. They include protein structure prediction, molecular docking, quantum simulation, and quantitative structure-activity relationship (QSAR) models. Current quantum devices are still susceptible to noise and error but are well suited for hybrid quantum-classical algorithms. The quantum advantage is demonstrated on hybrid systems and quantum-inspired devices such as quantum annealers. We hope to see more applications of quantum computing in the field of drug discovery and development.

Nilesh Ingale Nilesh Ingale