In its ongoing campaign to reveal the inner workings of the Sar-CoV-2 virus, the U.S. Department of Energy’s (DOE) Argonne National Laboratory is leading efforts to couple Artificial Intelligence (AI) and cutting-edge simulation workflows to better understand biological observations and accelerate drug discovery.
Argonne collaborated with academic and commercial research partners to achieve near real-time feedback between simulation and AI approaches to understand how two proteins in the SARS-CoV-2 viral genome interact to help the virus replicate and elude the host’s immune system.
The team achieved this milestone by coupling two distinct hardware platforms: a processor-packed silicon wafer deep learning accelerator; and ThetaGPU, an AI- and simulation-enabled extension of the Theta supercomputer, housed at the Argonne Leadership Computing Facility, a DOE Office of Science User Facility.
To enable this capability, the team developed Stream-AI-MD, a novel application of the AI method called deep learning to drive adaptive molecular dynamics (MD) simulations in a streaming manner. Data from simulations is streamed from ThetaGPU onto the platform to simultaneously analyse how the two proteins interact.
This needs to be done at a scale that is unprecedented since the data generation and AI components have to run side-by-side. The idea is, if one machine is good at doing MD simulations and another is very good at AI, then why not couple the two to produce a much larger system that offers more throughput with AI.
– Argonne computational biologist
One of the AI techniques that they’re using is called a variational autoencoder, which learns to capture the most essential information from MD simulations. The size of the simulation data sets is reduced in a way to make it easier for researchers to understand the dynamics occurring in the simulation. By running their deep learning component on the platform, they can identify binding pockets — tiny spaces that might develop during the formation of the two proteins — that can be targeted for small-molecule drug design.
These workflows will ultimately enable drug discoveries that treat both the SARS-CoV-2 virus and other diseases when the physical processes underlying specific biological functions are characterised. While the study currently does not focus on vaccines, the development of more complex models could lead to vaccine design.
This iterative workflow of supporting streaming AI and MD techniques on emerging hardware platforms will pave the way for advancing our knowledge of how proteins function. In the context of the SARS-CoV-2 virus, a fundamental understanding of molecular processes is important for designing drugs that can stop the virus in its path.
As reported by OpenGov Asia, AI is an integral part of modern life in numerous ways, from facial recognition to AI assistance in making more accurate medical diagnoses. In addition, researchers at the U.S. Department of Energy’s (DOE) Argonne National Laboratory are leading efforts to leverage AI to accelerate new and potentially transformative discoveries in science.
The researchers are looking at ways to apply the same advances to scientific problems, such as to discover things faster, to discover unknown things. AI methods can provide humans with very powerful, knowledgeable and imaginative assistance that can accelerate the discovery process. They focus on long-term AI that will change many aspects of society in a way that is currently unimaginable.
Argonne researchers across the laboratory complex are using AI to design better materials and processes, safeguard the nation’s power grid, accelerate medical treatments, automate traditional research, and drive discovery. Argonne is at the forefront of AI research, playing an integral role in applying innovative AI methods to solve problems and change lives. The AI research conducted by Argonne scientists has practical implications that contribute to society.