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How Quantum Computers Could Change Drug Development in 2023?

Here are some details about how quantum computers could affect drug development in 2023

One day, quantum computers might be able to create brand-new medicines and solve intricate issues with the healthcare supply chain. Experts anticipate a steady trickle of new developments in the developing field in the interim.

The theories of quantum mechanics are used by quantum computing to solve certain kinds of problems that are too complex for traditional computing. Maximillian Zinner, a quantum computing researcher at Witten/Herdecke University in Germany, explains that individual optimization problems will likely be the first useful applications in drug development. This could include enhancing drug pricing models or optimizing supply chains for large clinical trials, but these applications are probably still years away.

However, the field of quantum computing will pursue more ambitious goals in the future. Quantum computers may alter drug development in 2023. According to Zinner, the “holy grail” of drug development—testing and developing new medications in silico, or through computer modeling—could be achieved by quantum computers in 10 to 15 years.

Nevertheless, there are numerous development obstacles before the field’s real-world applications catch up to the growing hype. According to Dr. Leonard Fehring, a colleague of Zinner at Witten/Herdecke University, “Do not expect a big bang to happen that suddenly causes everything in healthcare to be powered by quantum computing.” These developments require time.

There are three main areas in drug development where quantum computing is likely to be most useful. First, according to Zinner, quantum computing may perform better than classical computing at solving difficult optimization problems. Utilizing qubits, quantum computers can simultaneously measure all of a complex function’s possible values and determine the maxima and minima associated with the lowest costs and highest efficiency. According to Fehring, quantum computing could solve small optimization issues in areas like clinical staffing models and pharmaceutical supply chains by the middle of the 2020s.

Second, quantum processing could recreate electrons inside a particle, really displaying protein collapsing and prompting the improvement of new medications, Fehring adds. Chemicals operate by the laws of quantum physics at the molecular level, and their interactions typically involve complicated probabilities that today’s supercomputers are unable to process. According to Zinner, it will probably take between 10 and 15 years for quantum computers to successfully design and test new therapeutic molecules in silico. In the end, “it’s just a very complicated optimization problem to make new molecules from nothing,” he says.

Thirdly, according to Fehring, an application known as quantum computing machine learning may enhance the accuracy of existing AI strategies. This includes improving the accuracy of natural language processing models like BioGPT and locating patterns in data from medical imaging and electronic health records.

Still, machine learning can be processed with conventional computers, so quantum computing isn’t necessary for its advancement. Instead, according to Fehring, quantum computing could help AI programs by assisting in specific statistical calculations and increasing overall processing power.

Most importantly, quantum computers are best suited to enhance rather than detract from the work of advanced classical computers. According to Penman, “quantum computers are not general-purpose machines.” They have the skills necessary to solve very specific problems at a very high level.