Drug Developers Use Google Cloud HPC in the Fight Against ALS

By Doug Black

February 16, 2017

Within the haystack of a lethal disease such as ALS (amyotrophic lateral sclerosis / Lou Gehrig’s Disease) there exists, somewhere, the needle that will pierce this therapy-resistant affliction. Finding the needle is a trial-and-error process of monumental proportions for scientists at pharmaceutical companies, medical research centers and academic institutions. As models grow in scale so too does the need for HPC resources to run simulations iteratively, to try-and-fail fast until success is found.

That’s all well and good if there’s ready access to HPC on premises. If not, drug developers, such as ALS researcher Dr. May Khanna, Pharmacology Department assistant professor at the University of Arizona, have turned to HPC resources provided by public cloud services. But using AWS, Azure or Google introduces a host of daunting compute management problems that tax the skills and time availability of most on-site IT staffs.

These tasks include data placement, instance provisioning, job scheduling, configuring software and networks, cluster startup and tear-down, cloud provider setup, cost management and instance health checking. To handle these cloud orchestration functions tied to 5,000 cores of Google Cloud Preemptive VMs (PVMs), Dr. Khanna and her team at Arizona turned to Cycle Computing to run “molecular docking” simulations at scale by Schrödinger Glide molecular modeling drug design software.

The results: simulations that would otherwise take months have been compressed to a few hours, short enough to be run during one of Dr. Khanna’s seminars and the output shared with students.

Dr. May Khanna

Developing new drugs to target a specific disease often starts with the building blocks of the compounds that become the drugs. The process begins with finding small molecules that can target specific proteins that, when combined, can interact in a way that becomes the disease’s starting point. The goal is to find a molecule that breaks the proteins apart. This is done by simulating how the small molecules dock to the specific protein locations. These simulations are computationally intensive, and many molecules need to be simulated to find a few good candidates.

Without powerful compute resources, researchers must artificially constrain their searches, limiting the number of molecules to simulate. And they only check an area of the protein known to be biologically active. Even with these constraints, running simulations takes a long time. Done right, molecular docking is an iterative process that requires simulation, biological verification, and then further refinement. Shortening the iteration time is important to advancing the research.

The objective of Dr. Khanna’s work was to simulate the docking of 1 million compounds to one target protein. After a simulation was complete, the protein was produced in the lab, and compounds were then tested with nuclear magnetic resonance spectroscopy.

“It’s a target (protein) that’s been implicated in ALS,” the energetic Dr. Khanna told EnterpriseTech (HPCwire‘s sister pub). “The idea is that the particular protein was very interesting, people who modulated it in different ways found some significant improvement in the ALS models they have with (lab) mice. The closer we can link biology to what we’re seeing as a target, the better chance of actually getting to a real therapeutic.”

“Modulating,” Dr. Khanna explained, is disrupting two proteins interacting in a way that is associated with ALS, a disease that currently afflicts about 20,000 Americans and for which there is no cure. “We’re trying to disrupt them, to release them to do their normal jobs,” she said.

She said CycleCloud plays a central role in running Schrödinger Glide simulations. Without Google Cloud PVMs, simulations would take too long and model sizes would be too small to generate meaningful results. Without CycleCloud, the management of 5,000 PVM nodes would not be possible.

CycleCloud provides a web-based GUI, a command line interface and APIs to define cloud-based clusters. It auto-scales clusters by instance types, maximum cluster size and costing parameters, deploying systems of up to 156,000 cores while validating each piece of the infrastructure. Additionally, it syncs in-house data repositories with cloud locations in a policy / job driven fashion, to lower costs.

It should be noted that the use of Google Cloud’s PVMs, while helping to hold down the cost of running simulations to $200, contribute an additional degree of complexity to Dr. Khanna’s project work. Preemptible compute capacity offers the advantage of a consistent price not subject to dynamic demand pricing, as are other public cloud instances. PVMs are assigned to a job for a finite period of time but – here’s the rub – they can be revoked at any moment. While Dr. Khanna’s workflow was ideal for leveraging PVMs, since it consists of small, short-running jobs, PVMs can disappear at without warning.

In the case of Dr. Khanna’s ALS research work, said Jason Stowe, CEO of Cycle Computing said, “if you’re willing to getting rid of the node, but you’re able to use it during that timeframe at substantially lower cost, that allows you get a lot more computing bang for your buck. CycleCloud automates the process, taking care of nodes that go away, making sure the environment isn’t corrupted, and other technical aspects that we take care of so the user doesn’t have to.”

The simulation process is divided into two parts. The first step uses the Schrödinger LigPrep package, which converts 2D structures to the 3D format used in the next stage. This stage started with 4 GB of input data staged to an NFS filer. The output data was approximately 800KB and was stored on the NFS filer as well. To get the simulation done as efficiently as possible, the workload was split into 300 smaller jobs to assist in scaling the next stage of the workflow. In total, the first stage consumed 1500 core-hours of computation.

The Schrödinger Glide software package performs the second stage of the process, where the actual docking simulation is performed. Each of the 300 sub-jobs consists of four stages, each with an attendant prep stage. The total consumption was approximately 20,000 core-hours using 5,000 cores of n1-highcpu-16 instances. Each instance had 16 virtual cores with 60 gigabytes of RAM. The CycleCloud software dynamically sized the cluster based on the number of jobs in queue and replaced preempted instances.

Dr. Khanna’s research is the early stages of a process that, if successful, could take several years before reaching human clinical trials.

“The faster we can do this, the less time we have to wait for results, so we can go back and test it again and try to figure out what compounds are really binding,” she said, “the faster the process can move along.”

Dr. Khanna said plans are in place to increase the size of the pool of potential compounds, as well as include other proteins in the simulation to look for interactions that would not typically be seen until later in the process. The team will also simulate over the entire surface of the protein instead of just a known-active area unlocking “an amazing amount of power” in the search process, she said.

“That jump between docking to binding to biological testing takes a really long time, but I think we can move forward on that with this cloud computing capacity,” she said. “The mice data that we saw was really exciting…, you could see true significant changes with the mice. I can’t tell you we’ve discovered the greatest thing for ALS, but showing that if we take these small molecules and we can see improvement, even that is so significant.”

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industry updates delivered to you every week!

Nvidia’s New Blackwell GPU Can Train AI Models with Trillions of Parameters

March 18, 2024

Nvidia's latest and fastest GPU, code-named Blackwell, is here and will underpin the company's AI plans this year. The chip offers performance improvements from its predecessors, including the red-hot H100 and A100 GPUs. Read more…

Nvidia Showcases Quantum Cloud, Expanding Quantum Portfolio at GTC24

March 18, 2024

Nvidia’s barrage of quantum news at GTC24 this week includes new products, signature collaborations, and a new Nvidia Quantum Cloud for quantum developers. While Nvidia may not spring to mind when thinking of the quant Read more…

2024 Winter Classic: Meet the HPE Mentors

March 18, 2024

The latest installment of the 2024 Winter Classic Studio Update Show features our interview with the HPE mentor team who introduced our student teams to the joys (and potential sorrows) of the HPL (LINPACK) and accompany Read more…

Houston We Have a Solution: Addressing the HPC and Tech Talent Gap

March 15, 2024

Generations of Houstonian teachers, counselors, and parents have either worked in the aerospace industry or know people who do - the prospect of entering the field was normalized for boys in 1969 when the Apollo 11 missi Read more…

Apple Buys DarwinAI Deepening its AI Push According to Report

March 14, 2024

Apple has purchased Canadian AI startup DarwinAI according to a Bloomberg report today. Apparently the deal was done early this year but still hasn’t been publicly announced according to the report. Apple is preparing Read more…

Survey of Rapid Training Methods for Neural Networks

March 14, 2024

Artificial neural networks are computing systems with interconnected layers that process and learn from data. During training, neural networks utilize optimization algorithms to iteratively refine their parameters until Read more…

Nvidia’s New Blackwell GPU Can Train AI Models with Trillions of Parameters

March 18, 2024

Nvidia's latest and fastest GPU, code-named Blackwell, is here and will underpin the company's AI plans this year. The chip offers performance improvements from Read more…

Nvidia Showcases Quantum Cloud, Expanding Quantum Portfolio at GTC24

March 18, 2024

Nvidia’s barrage of quantum news at GTC24 this week includes new products, signature collaborations, and a new Nvidia Quantum Cloud for quantum developers. Wh Read more…

Houston We Have a Solution: Addressing the HPC and Tech Talent Gap

March 15, 2024

Generations of Houstonian teachers, counselors, and parents have either worked in the aerospace industry or know people who do - the prospect of entering the fi Read more…

Survey of Rapid Training Methods for Neural Networks

March 14, 2024

Artificial neural networks are computing systems with interconnected layers that process and learn from data. During training, neural networks utilize optimizat Read more…

PASQAL Issues Roadmap to 10,000 Qubits in 2026 and Fault Tolerance in 2028

March 13, 2024

Paris-based PASQAL, a developer of neutral atom-based quantum computers, yesterday issued a roadmap for delivering systems with 10,000 physical qubits in 2026 a Read more…

India Is an AI Powerhouse Waiting to Happen, but Challenges Await

March 12, 2024

The Indian government is pushing full speed ahead to make the country an attractive technology base, especially in the hot fields of AI and semiconductors, but Read more…

Charles Tahan Exits National Quantum Coordination Office

March 12, 2024

(March 1, 2024) My first official day at the White House Office of Science and Technology Policy (OSTP) was June 15, 2020, during the depths of the COVID-19 loc Read more…

AI Bias In the Spotlight On International Women’s Day

March 11, 2024

What impact does AI bias have on women and girls? What can people do to increase female participation in the AI field? These are some of the questions the tech Read more…

Alibaba Shuts Down its Quantum Computing Effort

November 30, 2023

In case you missed it, China’s e-commerce giant Alibaba has shut down its quantum computing research effort. It’s not entirely clear what drove the change. Read more…

Nvidia H100: Are 550,000 GPUs Enough for This Year?

August 17, 2023

The GPU Squeeze continues to place a premium on Nvidia H100 GPUs. In a recent Financial Times article, Nvidia reports that it expects to ship 550,000 of its lat Read more…

Analyst Panel Says Take the Quantum Computing Plunge Now…

November 27, 2023

Should you start exploring quantum computing? Yes, said a panel of analysts convened at Tabor Communications HPC and AI on Wall Street conference earlier this y Read more…

Shutterstock 1285747942

AMD’s Horsepower-packed MI300X GPU Beats Nvidia’s Upcoming H200

December 7, 2023

AMD and Nvidia are locked in an AI performance battle – much like the gaming GPU performance clash the companies have waged for decades. AMD has claimed it Read more…

DoD Takes a Long View of Quantum Computing

December 19, 2023

Given the large sums tied to expensive weapon systems – think $100-million-plus per F-35 fighter – it’s easy to forget the U.S. Department of Defense is a Read more…

Synopsys Eats Ansys: Does HPC Get Indigestion?

February 8, 2024

Recently, it was announced that Synopsys is buying HPC tool developer Ansys. Started in Pittsburgh, Pa., in 1970 as Swanson Analysis Systems, Inc. (SASI) by John Swanson (and eventually renamed), Ansys serves the CAE (Computer Aided Engineering)/multiphysics engineering simulation market. Read more…

Intel’s Server and PC Chip Development Will Blur After 2025

January 15, 2024

Intel's dealing with much more than chip rivals breathing down its neck; it is simultaneously integrating a bevy of new technologies such as chiplets, artificia Read more…

Baidu Exits Quantum, Closely Following Alibaba’s Earlier Move

January 5, 2024

Reuters reported this week that Baidu, China’s giant e-commerce and services provider, is exiting the quantum computing development arena. Reuters reported � Read more…

Leading Solution Providers

Contributors

Choosing the Right GPU for LLM Inference and Training

December 11, 2023

Accelerating the training and inference processes of deep learning models is crucial for unleashing their true potential and NVIDIA GPUs have emerged as a game- Read more…

Training of 1-Trillion Parameter Scientific AI Begins

November 13, 2023

A US national lab has started training a massive AI brain that could ultimately become the must-have computing resource for scientific researchers. Argonne N Read more…

Shutterstock 1179408610

Google Addresses the Mysteries of Its Hypercomputer 

December 28, 2023

When Google launched its Hypercomputer earlier this month (December 2023), the first reaction was, "Say what?" It turns out that the Hypercomputer is Google's t Read more…

Comparing NVIDIA A100 and NVIDIA L40S: Which GPU is Ideal for AI and Graphics-Intensive Workloads?

October 30, 2023

With long lead times for the NVIDIA H100 and A100 GPUs, many organizations are looking at the new NVIDIA L40S GPU, which it’s a new GPU optimized for AI and g Read more…

AMD MI3000A

How AMD May Get Across the CUDA Moat

October 5, 2023

When discussing GenAI, the term "GPU" almost always enters the conversation and the topic often moves toward performance and access. Interestingly, the word "GPU" is assumed to mean "Nvidia" products. (As an aside, the popular Nvidia hardware used in GenAI are not technically... Read more…

Shutterstock 1606064203

Meta’s Zuckerberg Puts Its AI Future in the Hands of 600,000 GPUs

January 25, 2024

In under two minutes, Meta's CEO, Mark Zuckerberg, laid out the company's AI plans, which included a plan to build an artificial intelligence system with the eq Read more…

Google Introduces ‘Hypercomputer’ to Its AI Infrastructure

December 11, 2023

Google ran out of monikers to describe its new AI system released on December 7. Supercomputer perhaps wasn't an apt description, so it settled on Hypercomputer Read more…

China Is All In on a RISC-V Future

January 8, 2024

The state of RISC-V in China was discussed in a recent report released by the Jamestown Foundation, a Washington, D.C.-based think tank. The report, entitled "E Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire