Understanding disease is one of the great human endeavors and remains an incredible challenge.
Drug discovery is hard, on average taking over a decade to develop, $2.5 billion dollars in R&D.
And yet 90% of the efforts fail, the cost has doubled every 9 years. This is called Eroom's law.
For the $1.5 trillion dollar pharma industry there is a great deal at stake to accelerate the successful discovery of new drugs.
COVID-19 hits home this urgency.
Using breakthroughs in computer science we can begin to use simulation, in-silico methods, to understand the biological machinery of the
proteins that affect disease and search for lead candidate drugs before the slow process of in-vivo testing.
Drug discovery is hard in every respect.
First, it is hard to find the protein that is implicated in a disease.
Second, it is hard to find a small molecule, chemical compound that can bind with, and to activate or de-active the protein.
Third, getting the small molecule inside the cell is hard as mother nature has created defense systems to protect us from foreign substance.
Finally, what does your body do with the chemical compound? How does it safely absorb, distribute, metabolize, and excrete the compound?
This is called pharmaco-kinetics.
A spectacular arsenal of technologies is deployed in drug discovery.
Genomics is used to choose protein targets responsible for a disease and determine which patient will respond to a drug.
Cryo-Electron-microscope-imaging and 3D reconstruction let us see a protein's structure which determines its function.
Data analytics is used to screen 10^60 potential chemical compounds for fingerprints that might lead to a drug candidate.
Deep learning AI is used to generate and design new leads learning from known chemicals.
Docking predicts affinity, strength of the bond between a chemical and target protein.
Molecular dynamics analyzes the physical motion of a protein and its interactions with a chemical.
Pathology and radiology imaging are used to study the biomarkers, the condition of a disease and effectiveness of a drug.
Natural language processing study publications and health records to find chemicals that might be re-purposed for other disease targets.
Today, I'm pleased to announce the NVIDIA Clara Discovery, a state-of-the-art suite of tools for scientists to discover life-saving drugs.
Where there are popular industry tools, our computer scientists accelerate them.
Where no tools exist, we develop them, like the NVIDIA Parabricks, Clara Imaging, BioMegatron, BioBERT and NVIDIA RAPIDS.
We hope this will bend the trajectory of Eroom's law and lead to faster and more effective discovery of life saving drugs.
The UK is an epicenter of healthcare research.
In fact, Cambridge is the home where Francis Crick and James Watson discovered DNA.
But scientists in the UK need a state-of-the-art computing infrastructure.
So, we're going to build one. It's called Cambridge-1.
400 petaFLOPS of AI performance, it will be the fastest in the UK and top 30 in the world.
Cambridge-1 will host our UK AI and healthcare research collaborations with academia, industry, and startups.
All partners already use NVIDIA computing.
Cambridge-1 lets them do experiments too large for their infrastructure or while theirs are being built.
Our first partners are AstraZeneca, GSK, King's College, NHS, and Oxford Nanopore.
Today we are also announcing a partnership of GSK and NVIDIA to build the world's first AI Drug Discovery Lab.
Let's hear from Dr. Kim Branson, GSK's head of AI.
Understanding human disease has never been more urgent or important than it is today.
This undertaking is bringing together the best minds and the latest breakthroughs in biology.
The complexity of human biology is striking. There are 45,000 genes that make up 500 million protein interactions in a single cell
alone, and the human body has trillions.
With new tools, including genomic sequencers, microscopy, and medical imaging, we can measure this complexity for the first time:
from a genome to a single cell to a protein.
And new perspectives, looking at population-scale data, now capture the vast diversity of human health
while helping us zoom in on an individual's unique genetic makeup and personal history.
GSK has accumulated more data in its first quarter this year than in its entire 300-year history combined.
It's an amount of data no human could understand. And no equation can fully capture.
So, GSK believes that AI and computational biology, especially when combined with our
vast data sources, can break new ground in drug discovery.
But we can't do it alone. That's why GSK is partnering with NVIDIA to build out the world's first dedicated in-house drug discovery AI lab.
If AI is software written by a computer, then companies will need powerful AI computers to write great software.
This insight has transformed NVIDIA and other AI leaders.
I expect every major industry - healthcare, transportation, manufacturing, logistics, retail - will all eventually have
large AI compute infrastructures that collaborate with their software developers and researchers.
But it is challenging to build these systems, to develop the software and the tools to run on these large supercomputers.
So, we built a ready-made AI supercomputer called the NVIDIA DGX SuperPOD.
The DGX SuperPOD is available now, scalable from 20 to 140 DGX systems.
A 140-DGX-configuration is a Top 20 supercomputer in the world
We've also published a SuperPOD blueprint for our large network of partners to replicate using NVIDIA technologies.
Enterprises can go from zero to their valuable AI researchers being productive in just a few weeks, not many months.
The savings in productivity alone will pay for the system.
You've heard me say it. The more you buy, the more you save.
There are many installations going around the world already
CDAC is going to be the most powerful AI supercomputer in India.
Argonne National Lab is using DGX SuperPOD for computational drug screening.
The University of Florida has a system built to train next generation AI researchers and students.
The Wallenberg AI and Software Research is building the largest AI infrastructure in Sweden.
Facebook AI Research Lab has Super DGX for their researchers.
And Naver, Korea's #1 search engine and world's fastest growing messenger platform, is installing SuperPOD to advance natural language understanding.