The pharmaceutical industry has grown accustomed to investing billions of dollars to bring drugs to market, only to watch 90 percent of them fail even before clinical trials.
The problem is, and always has been, there’s simply not enough compute power in the world to accurately assess the properties of all possible molecules, nor to support the extensive experimental efforts needed in drug discovery.
“There may be more potential drug compounds than there are atoms in the universe,” said Patrick Lorton, chief technology officer at Schrödinger, the New York-based developer of a physics-based software platform designed to model and compute the properties of novel molecules for the pharma and materials industries.
“If you look at a billion molecules and you say there’s no good drug here, it’s the same as looking at a drop of water in the ocean and saying fish don’t exist,” he said.
Fresh off of a successful IPO earlier this year, Schrödinger has devoted decades to refining computational algorithms to accurately compute important properties of molecules. The company uses NVIDIA GPUs to generate and evaluate petabytes of data to accelerate drug discovery, which is a dramatic improvement over the traditional process of slow and expensive lab work.
The company works with all 20 of the biggest biopharma companies in the world, several of which have standardized on Schrödinger’s platform as a key component of preclinical research.
The COVID-19 pandemic highlights the need for a more efficient and effective drug discovery process. To that end, the company has joined the global COVID R&D alliance to offer resources and collaborate. Recently, Google Cloud has also thrown its weight behind this alliance, donating over 16 million hours of NVIDIA GPU time to hunt for a cure.
“We hope to develop an antiviral therapeutic for SARS-CoV-2, the virus that causes COVID-19, in time to have treatments available for future waves of the pandemic,” Lorton said.
Advanced Simulation Software
The pharmaceutical industry has long depended on manually intensive physical processes to find new therapeutics. This allowed it to develop many important remedies over the last 50 years, but only through a laborious trial-and-error approach, Lorton said.
He makes the comparison to airplane manufacturers, which formerly carved airplane designs out of balsa wood and tested their drag coefficient in wind tunnels. They now rely on advanced simulation software that reduces the time and resources needed to test designs.
With the pharmaceutical industry traditionally using the equivalent of balsa, Schrödinger’s drug discovery platform has become a game changer.
“We’re trying to make preclinical drug discovery more efficient,” said Lorton. “This will enable the industry to treat more diseases and help more conditions.”
Exploring New Space
For more than a decade, every major pharmaceutical company has been using Schrödinger’s software, which can perform physics simulations down to the atomic level. For each potential drug candidate, Schrödinger uses recently developed physics-based computational approaches to calculate as many as 3,000 possible compounds. This requires up to 12,000 GPU hours on high-performance computers.
Once the physics-based calculations are completed for the original set of randomly selected compounds, a layer of active learning is applied, making projections on the probable efficacy of a billion molecules.
Lorton said it currently takes four or five iterations to get a machine-learning algorithm accurate enough to be predictive, though even these projections are always double-checked with the physics-based methods before synthesizing any molecules in the lab.
This software-based approach yields much faster results, but that’s only part of the value. It also greatly expands the scope of analysis, evaluating data that human beings never would have had time to address.
“The thing that is most compelling is exploring new space,” said Lorton. “It’s not just being cheaper. It’s being cheaper and finding things you would have otherwise not explored.”
For that reason, Schrödinger’s work focuses on modeling and simulation, and using the latest high performance computing resources to expand its discovery capabilities.
Bayer Proving Platform’s Value
One customer that’s been putting Schrödinger’s technology to use is Bayer AG. Schrödinger software has been helping Bayer scientists find lead structures for several drug discovery projects, ultimately contributing to clinical development candidates.
Recently both companies agreed to co-develop a novel drug discovery platform to accelerate the process of estimating the binding affinity, as well as other properties, and synthesizability of small molecules.
Bayer can’t yet share any specific results that the platform has delivered, but Dr. Alexander Hillisch, the company’s head of computational drug design, said it’s had an impact on several active projects.
Dr. Hillisch said that the software is expected to speed up work and effectively widen Bayer’s drug-discovery capabilities. As a result, he believes it’s time for NVIDIA GPUs to get a lot more recognition within the industry.
In a typical drug discovery project, Bayer evaluates binding affinities and other properties of molecules such as absorption and metabolic stability. With Schrödinger software and NVIDIA GPUs, “we’re enumerating millions to billions of virtual compounds and are thus scanning the chemical space much more broadly than we did before, in order to identify novel lead compounds with favorable properties,” he said.
Dr. Hillisch also suggested that the impact of holistic digital drug discovery approaches can soon be judged. “We expect to know how substantial the impact of this scientific approach will be in the near future,” he said.
The drug design platform also will be part of Bayer’s work on COVID-19. The company spun off its antiviral research into a separate company in 2006, but it recently joined a European coronavirus research initiative to help identify novel compounds that could provide future treatment.
Tailor-Made Task for GPUs
Given the scope of Schrödinger’s task, Lorton made it clear that NVIDIA’s advances in developing a full-stack computing platform for HPC and AI that pushes the boundaries of performance have been as important to his company’s accomplishments as its painstaking algorithmic and scientific work.
“It could take thousands, or tens of thousands, or in some crazy case, even hundreds of thousands of dollars to synthesize and get the binding affinity of a drug molecule,” he said. “We can do it for a few dollars of compute costs on a GPU.”
Lorton said that if the company had started one of its physics calculations on a single CPU when it was founded in 1990, it would have taken until today to reach the conclusions that a single GPU can now deliver in less than an hour.
Even with the many breakthroughs in compute speed on NVIDIA GPUs, Schrödinger’s discovery projects require thousands of NVIDIA T4 and V100 Tensor Core GPUs every day, both on premises and on the Google Cloud Platform. It’s this next level of compute, combined with continued investment in the underlying science, that the company hopes will change the way all drug discovery is done.
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