Quantum Computing Applied to COVID-19 Drug Exploration

Researchers at the Oak Ridge National Laboratory (ORNL) are utilising the power of supercomputers to refine the screening of potential drug molecules that can disrupt the coronavirus's spike protein from binding to human cells. This project, led by Jeremy Smith, is a significant step forward in the fight against COVID-19.

The researchers are focusing on molecular dynamics simulations on the ORNL Summit IBM GPU-based system, the second fastest supercomputer in the world. In addition, they have been using the Stampede2 supercomputer, ranking #35 globally and #3 in U.S. academia, which was provided by the Texas Advanced Computing Center (TACC). Computer time on both Summit and Stampede2 was granted by the HPC Covid-19 Consortium.

The computational prescreening process is based on a structural model of the target protein's atoms. The ORNL research project has performed approximately 2.4 billion docking calculations with the Enamine REAL database of compounds, narrowing down the top 3000 potential candidates based on binding affinity to the active sites of the coronavirus spike protein.

The DOE Office of Science's National Virtual Biotechnology Laboratory is supporting this project, along with other COVID-19 studies, with funding provided by the Coronavirus CARES Act. Funding and support for the drug discovery pipeline work were also provided by the Alabama Supercomputer Authority, the National Institutes of Health, a National Science Foundation Graduate Research Fellowship, the Cancer Research Informatics Shared Resource Facility of the University of Kentucky Markey Cancer Center, and the University of Kentucky's Center for Computational Sciences (CCS) high-performance computing resources.

The quantum mechanical refinement protocol developed by Irle and Vuong using Stampede2 is still in its preliminary stages. However, reducing the number of drug candidates and reliably narrowing down the most active species could result in a faster response to suddenly emergent pandemic situations like COVID-19.

The research findings were published in the Journal of Chemical Information and Modeling with the DOI 10.1021/acs.jcim.0c01010. The group of researchers at ORNL involved in developing a supercomputer-driven process for in-silico drug discovery is not explicitly named in the provided search results. Nevertheless, their work is a testament to the power of supercomputing in the fight against global health crises.

The researchers had a positive experience with the Stampede2 supercomputer, citing high performance and active support. They plan to investigate 15 spike protein clusters and refine the binding energies of 150 protein ligand complexes in the pipeline study. Successful vaccines targeting the coronavirus's spike protein have started to drive down global infection rates, and this ORNL research project could potentially lead to the discovery of new drugs to combat the virus.

As of now, the COVID-19 disease has infected over 180 million people and killed nearly four million people worldwide. The supercomputer-driven pipeline for in-silico drug discovery could revolutionise the way we approach drug discovery and response to pandemics in the future.