Research across global academic and commercial labs to create a more efficient drug discovery process won recognition today with a special Gordon Bell Prize for work fighting COVID-19.
A team of 27 researchers led by Rommie Amaro at the University of California at San Diego (UCSD) combined high performance computing (HPC) and AI to provide the clearest view to date of the coronavirus, winning the award.
Their work began in late March when Amaro lit up Twitter with a picture of part of a simulated SARS-CoV-2 virus that looked like an upside-down Christmas tree.
Seeing it, one remote researcher noticed how a protein seemed to reach like a crooked finger from behind a protective shield to touch a healthy human cell.
I said, holy crap, that's crazy' only through sharing a simulation like this with the community could you see for the first time how the virus can only strike when it's in an open position, said Amaro, who leads a team of biochemists and computer experts at UCSD.
Amaro shared her early results on Twitter. The image in the tweet was taken by Amaro's lab using what some call a computational microscope, a digital tool that links the power of HPC simulations with AI to see details beyond the capabilities of conventional instruments.
It's one example of work around the world using AI and data analytics, accelerated by NVIDIA Clara Discovery, to slash the $2 billion in costs and ten-year time span it typically takes to bring a new drug to market.
A Virtual Microscope Enhanced with AI In early October, Amaro's team completed a series of more ambitious HPC+AI simulations. They showed for the first time fine details of how the spike protein moved, opened and contacted a healthy cell.
One simulation (below) packed a whopping 305 million atoms, more than twice the size of any prior simulation in molecular dynamics. It required AI and all 27,648 NVIDIA GPUs on the Summit supercomputer at Oak Ridge National Laboratory.
More than 4,000 researchers worldwide have downloaded the results that one called critical for vaccine design for COVID and future pathogens.
Today, it won a special Gordon Bell Prize for COVID-19, the equivalent of a Nobel Prize in the supercomputing community.
Two other teams also used NVIDIA technologies in work selected as finalists in the COVID-19 competition created by the ACM, a professional group representing more than 100,000 computing experts worldwide.
And the traditional Gordon Bell Prize went to a team from Beijing, Berkeley and Princeton that set a new milestone in molecular dynamics, also using a combination of HPC+AI on Summit.
An AI Funnel Catches Promising Drugs Seeing how the infection process works is one of a string of pearls that scientists around the world are gathering into a new AI-assisted drug discovery process.
Another is screening from a vast field of 1068 candidates the right compounds to arrest a virus. In a paper from part of the team behind Amaro's work, researchers described a new AI workflow that in less than five months filtered 4.2 billion compounds down to the 40 most promising ones that are now in advanced testing.
We were so happy to get these results because people are dying and we need to address that with a new baseline that shows what you can get with AI, said Arvind Ramanathan, a computational biologist at Argonne National Laboratory.
Ramanathan's team was part of an international collaboration among eight universities and supercomputer centers, each contributing unique tools to process nearly 60 terabytes of data from 21 open datasets. It fueled a set of interlocking simulations and AI predictions that ran across 160 NVIDIA A100 Tensor Core GPUs on Argonne's Theta system with massive AI inference runs using NVIDIA TensorRT on the many more GPUs on Summit.
Docking Compounds, Proteins on a Supercomputer Earlier this year, Ada Sedova put a pearl on the string for protein docking (described in the video below) when she described plans to test a billion drug compounds against two coronavirus spike proteins in less than 24 hours using the GPUs on Summit. Her team's work cut to just 21 hours the work that used to take 51 days, a 58x speedup.
In a related effort, colleagues at Oak Ridge used NVIDIA RAPIDS and BlazingSQL to accelerate by an order of magnitude data analytics on results like Sedova produced.
Among the other Gordon Bell finalists, Lawrence Livermore researchers used GPUs on the Sierra supercomputer to slash the training time for an AI model used to speed drug discovery from a day to just 23 minutes.
From the Lab to the Clinic The Gordon Bell finalists are among more than 90 research efforts in a supercomputing collaboration using 50,000 GPU cores to fight the coronavirus.
They make up one front in a global war on COVID that also includes companies such as Oxford Nanopore Technologies, a genomics specialist using NVIDIA's CUDA software to accelerate its work.
Oxford Nanopore won approval from European regulators last month for a novel system the size of a desktop printer that can be used with minimal training to perform thousands of COVID tests in a single day. Scientists worldwide have used its handheld sequencing devices to understand the transmission of the virus.
Relay Therapeutics uses NVIDIA GPUs and software to simulate with machine learning how proteins move, opening up new directions in the drug discovery process. In September, it started its first human trial of a molecule inhibitor to treat cancer.
Startup Structura uses CUDA on NVIDIA GPUs to analyze initial images of pathogens to quickly determine their 3D atomic structure, another key step in drug discovery. It's a member of the NVIDIA Inception program, which gives startups in AI access to the latest GPU-accelerated technologies and market partners.
From Clara Discovery to Cambridge-1
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Research across global academic and commercial labs to create a more efficient drug discovery process won recognition today with a special Gordon Bell Prize for work fighting COVID-19.
