About this Research Topic
COVID-19 has widely disrupted how global research is conducted and is currently spurring a wider range of collaborative studies in the area of understanding the molecular mechanisms of how the virus, SARS-CoV-2, affects human cells. Therefore, accelerating the discovery of small molecules, and other biological therapeutics is of immediate importance. Computational techniques, especially at the interface of high-performance computing (HPC), multiscale molecular simulations, and artificial intelligence (AI) techniques are playing a critical role in designing, developing, validating novel therapeutic approaches for SARS-CoV-2. In many cases, AI-driven discovery techniques are providing novel proposals for understanding the biophysical, biochemical and biological mechanisms of SARS-CoV-2 infection.
The goal of this Research Topic is to articulate the convergence of AI, HPC, and multiscale molecular simulation techniques that are focused in providing quantitative understanding of the biophysical, biochemical and biological mechanisms of SARS-CoV-2 infection. In particular, this collection will examine how AI techniques can provide insights into testing novel hypothesis related to SARS-CoV-2 infection. Further, papers that articulate novel use of AI/machine learning to analyze simulation datasets, algorithmic development, and HPC will be particularly relevant to this topic. Papers can also summarize the state-of-the art in terms of using HPC and multiscale simulations for SARS-CoV-2 research. Finally, we also encourage papers that apply AI/HPC for cheminformatic, genomic, and pharmacogenomic approaches for finding new therapeutics against SARS-CoV-2.
Areas to cover may include:
• Multiscale molecular simulations that provide quantitative insights into the molecular mechanisms of SARS-CoV-2 infection;
• Experimental techniques combined with simulations and data analysis techniques;
• Novel computational approaches that address viral-host protein interactions and systems biology research;
• Cheminformatics, pharmacogenomics and other ‘-omic’ type research for understanding SARS-CoV-2 biology;
• Machine learning, deep learning, and artificial intelligence (AI) approaches for designing novel therapeutics for SARS-COV-2;
• Research at the intersection of AI, high performance computing (HPC) and multiscale simulations addressing fundamental questions of SARS-CoV-2 biology.
***Due to the exceptional nature of the COVID-19 situation, Frontiers is waiving article publishing charges for invited contributions until December 31st 2020.***
Dr. Chakra S. Chennubhotla holds an executive position at the University of Pittsburgh start-up SpIntellx, Inc., a computational and systems pathology company. All other Topic Editors declare no competing interests.
The goal of this Research Topic is to articulate the convergence of AI, HPC, and multiscale molecular simulation techniques that are focused in providing quantitative understanding of the biophysical, biochemical and biological mechanisms of SARS-CoV-2 infection. In particular, this collection will examine how AI techniques can provide insights into testing novel hypothesis related to SARS-CoV-2 infection. Further, papers that articulate novel use of AI/machine learning to analyze simulation datasets, algorithmic development, and HPC will be particularly relevant to this topic. Papers can also summarize the state-of-the art in terms of using HPC and multiscale simulations for SARS-CoV-2 research. Finally, we also encourage papers that apply AI/HPC for cheminformatic, genomic, and pharmacogenomic approaches for finding new therapeutics against SARS-CoV-2.
Areas to cover may include:
• Multiscale molecular simulations that provide quantitative insights into the molecular mechanisms of SARS-CoV-2 infection;
• Experimental techniques combined with simulations and data analysis techniques;
• Novel computational approaches that address viral-host protein interactions and systems biology research;
• Cheminformatics, pharmacogenomics and other ‘-omic’ type research for understanding SARS-CoV-2 biology;
• Machine learning, deep learning, and artificial intelligence (AI) approaches for designing novel therapeutics for SARS-COV-2;
• Research at the intersection of AI, high performance computing (HPC) and multiscale simulations addressing fundamental questions of SARS-CoV-2 biology.
***Due to the exceptional nature of the COVID-19 situation, Frontiers is waiving article publishing charges for invited contributions until December 31st 2020.***
Dr. Chakra S. Chennubhotla holds an executive position at the University of Pittsburgh start-up SpIntellx, Inc., a computational and systems pathology company. All other Topic Editors declare no competing interests.
Keywords: SARS-CoV-2, coronavirus, COVID-19, artificial intelligence, high-performance computing, multiscale molecular simulations
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
