Researchers bring innovative AI and simulation tools to the COVID-19 battlefront

In its on-going run to uncover the interior workings of the Sar-CoV-2 virus, the U.S. Department of Energy's (DOE) Argonne National Laboratory is starring efforts to mates artificial quality (AI) and cutting-edge simulation workflows to amended recognize biologic observations and accelerate cause discovery.

Argonne collaborated with world and commercialized probe partners to execute adjacent real-time feedback betwixt simulation and AI approaches to recognize however 2 proteins successful the SARS-CoV-2 viral genome, nsp10 and nsp16, interact to assistance the microorganism replicate and elude the host's immune system.

The squad achieved this milestone by coupling 2 chiseled hardware platforms: Cerebras CS-1, a processor-packed silicon wafer heavy learning accelerator; and ThetaGPU, an AI- and simulation-enabled hold of the Theta supercomputer, housed astatine the Argonne Leadership Computing Facility, a DOE Office of Science User Facility.

To alteration this capability, the squad developed Stream-AI-MD, a caller exertion of the AI method called heavy learning to thrust adaptive molecular dynamics (MD) simulations successful a streaming manner. Data from simulations is streamed from ThetaGPU onto the Cerebras CS-1 level to simultaneously analyse however the 2 proteins interact.

"This needs to beryllium done astatine a standard that is unprecedented since the information procreation and AI components person to tally side-by-side," said Argonne computational biologist Arvind Ramanathan, a subordinate of the probe team. "The thought is, if 1 instrumentality is bully astatine doing MD simulations and different is precise bully astatine AI, past wherefore not mates the 2 to nutrient a overmuch larger strategy that offers much throughput with AI," explained Ramanathan.

One of the AI techniques that they're utilizing is called a variational autoencoder, which learns to seizure the astir indispensable accusation from MD simulations. The size of the simulation information sets is reduced successful a mode to marque it easier for researchers to recognize the dynamics occurring successful the simulation.

By moving their heavy learning constituent connected Cerebras CS-1, they tin place binding pockets—tiny spaces that mightiness make during the enactment of the 2 proteins—that tin beryllium targeted for small-molecule cause design.

These workflows volition yet alteration cause discoveries that dainty some the SARS-CoV-2 microorganism and different diseases, erstwhile the physical processes underlying circumstantial biologic functions are characterized, said Ramanathan. And portion the survey presently does not absorption connected vaccines, the improvement of much analyzable models could pb to vaccine design.

"This iterative workflow of supporting streaming AI and MD techniques connected emerging hardware platforms volition pave the mode for advancing our cognition of however proteins function," said Ramanathan. "In the discourse of the SARS-CoV-2 virus, a cardinal knowing of molecular processes, specified arsenic the nsp16-nsp10 interaction, is important if we privation to plan drugs that tin halt the microorganism successful its path."

The probe was published successful the proceedings from the Platform for Advanced Scientific Computing Conference (PASC '21), July 5–9, 2021, Geneva, Switzerland. ACM, New York, NY, USA.

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More information: Alexander Brace et al, Stream-AI-MD, Proceedings of the Platform for Advanced Scientific Computing Conference (2021). DOI: 10.1145/3468267.3470578