Penn State Researchers Using Quantum Machine Learning to Condense Drug Development Process for COVID-19
(HPC.Wire) Researchers from Penn State University are using quantum machine learning, a burgeoning crossover field that combines machine learning with quantum information processing to condense the arduous process of drug development for COVID-19.
“Discovering any new drug that can cure a disease is like finding a needle in a haystack,” said Swaroop Ghosh, professor of electrical engineering and computer science and engineering at Penn State, in an interview with the university. Ghosh and his doctoral students (Mahabubul Alam, Abdullah Ash Saki, Junde Li and Ling Qiu) were tackling a challenging element of the COVID-19 drug discovery formula: identifying the billions upon billions of molecules for the supercomputers to screen.
“High-performance computing such as supercomputers and artificial intelligence can help accelerate this process by screening billions of chemical compounds quickly to find relevant drug candidates,” Ghosh said. “This approach works when enough chemical compounds are available in the pipeline, but unfortunately this is not true for COVID-19. This project will explore quantum machine learning to unlock new capabilities in drug discovery by generating complex compounds quickly.”
The researchers, who previously worked to develop a toolkit for using quantum computing to solve combinatorial optimization problems, which aim to find the ideal item from a large – and even unsearchable – set of items. Drug discovery falls under the combinatorial optimization umbrella, which made the transition to COVID-19 drug discovery relatively painless for the team.