Drug Discovery Looks for Its Next Fix

By Michael Feldman

July 31, 2012

Despite the highly profitable nature of the pharmaceutical business and the large amount of R&D money companies throw at creating new medicines, the pace of drug development is agonizingly slow.  Over the last few years, on average, less than two dozen new drugs have been introduced per year. One of the more promising technologies that could help speed up this process is supercomputing, which can be used not only to find better, safer drugs, but also to weed out those compounds that would eventually fail during the latter stages of drug trials.

According to a 2010 report in Nature, big pharma spends something like $50 billion per year on drug research and development. (To put that in perspective, that’s four to five times the total spend for high performance computing.) The Nature report estimates the price tag to bring a drug successfully to market is about $1.8 billion, and rising. A lot of that cost is due to the high attrition rate of drugs, which is caused by problems in absorption, distribution, metabolism, excretion and toxicity that gets uncovered during clinical trials.

Ideally, the drug makers would like know which compounds were going to succeed before they got to the expensive stages of development. That’s where high performance computing can help. The approach is to use molecular docking simulations on the computer to determine if the drug candidate can bind to the target protein associated with the disease. The general idea is to find the key (the small molecule drug) that fits in the lock (the protein).

AutoDock, probably the most common molecular modeling application for protein docking, is a one of the more popular software package used by the drug research community. It played a role in developing some of the more successful HIV drugs on the market. Fortunately, AutoDock is freely available under the GNU General Public License.

The trick is to do these docking simulations on a grand scale. Thanks to the power of modern HPC machines, millions of compounds can now be screened against a protein in a reasonable amount of time. In truth, that timeframe is dependent upon how many cores you can put to the task. For a typical medium-sized cluster that a drug company might have in-house, it would take several weeks to screen just a few thousand compounds against one target protein. To reach a more interactive workflow, you need a something approaching a petascale supercomputer.

But not necessarily an actual supercomputer. Compute clouds have turned out to be very suitable for this type of embarrassing parallel application. For example, in a recent test with 50,000 cores on Amazon’s cloud (provisioned by Cycle Computing), software was able to screen 21 million compounds against a protein target in less than three hours.

Real supercomputers work too. At Oak Ridge National Lab (ORNL), researchers there used 50,000 cores of Jaguar to screen about 10 million drug candidates in less than a day. Jeremy C. Smith, director of the Center for Molecular Biophysics at ORNL, believes his type level of virtual screening is the most cost-effective approach to turbo-charge the drug pipeline. But the real utility of the supercomputing approach, says Smith, is that it can also be used to screen out drugs with toxic side effects.

Toxicity is often hard to detect until it comes time to do clinical trials, the most expensive and time-consuming phase of drug development. Worse yet, sometimes toxicity is not discovered until after the drug has been approved and released into the wild. So identifying these compounds early has the potential to save lots of money, not to mention lives. As Smith says, “If drug candidates are going to fail, you want them to fail fast, fail cheap.”

At the molecular level, toxicity is caused by a drug binding to the wrong protein, one that is actually needed by the body, rather than just selectively binding to the protein causing the condition. The problem is humans have about a thousand proteins, so every potential compound needs to be checked against each one. When you’re working with millions of drug candidates, the job becomes overwhelming, even for the petaflop supercomputers of today. To support the toxicity problem, you’ll need an exascale machine, says Smith.

Besides screening for toxicity, the same exascale setup can be used to repurpose existing drugs for other medical conditions. That is, the drug docking software could use approved drugs as the starting point and try to match them against various target proteins know to cause disease. Right now, drug repurposing is typically discovered on a trial-and-error basis, but the increasing number of compounds that are now in this multiple-use category suggests this could be rich new area of drug discovery.

In any case, sheer compute power is not the complete answer. For starters, the software has to be scaled up to the level of the hardware, and on an exascale machine, that hardware is more than likely going to be based on heterogenous processors. But since the problem is easily parallelized (each docking operation can be performed independently of one another), at least the scaling aspect should be relatively easy to overcome.

The larger problem is that the molecular modeling software itself is imperfect. Unlike a true lock and key, proteins are dynamic structures, and the action of binding to a molecule changes their shape. Therefore, physics simulation is also required to get a more precise match.

AutoDock, for example, is only able to provide a crude match between drug and protein. To get higher fidelity docking, more compute-intensive algorithms are required. Researchers, like those at ORNL, often resort to more precise molecular dynamics codes after getting performing a crude screening run with AutoDock.

None of this is a guarantee that virtual docking on exascale machines is going to launch a golden age of drugs. It’s possible that researchers will discover that there are just a handful of small molecule compounds that actually exhibit both disease efficacy and are non-toxic. But Smith believes this approach is full of promise. “This is the way to design drugs since this mirrors the way nature works,” he says.

Subscribe to HPCwire's Weekly Update!

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

China’s Expanding Effort to Win in Microchips

July 27, 2017

The global battle for preeminence, or at least national independence, in semiconductor technology and manufacturing continues to heat up with Europe, China, Japan, and the U.S. all vying for sway. A fascinating article ( Read more…

By John Russell

Hyperion: Storage to Lead HPC Growth in 2016-2021

July 27, 2017

Global HPC external storage revenues will grow 7.8% over the 2016-2021 timeframe according to an updated forecast released by Hyperion Research this week. HPC server sales, by comparison, will grow a modest 5.8% to $14.8 Read more…

By John Russell

Exascale FY18 Budget – The Senate Provides Their Input

July 27, 2017

In the federal budgeting world, “regular order” is a meaningful term that is fondly remembered by members of both the Congress and the Executive Branch. Regular order is the established process whereby an Administrat Read more…

By Alex R. Larzelere

HPE Extreme Performance Solutions

HPE Servers Deliver High Performance Remote Visualization

Whether generating seismic simulations, locating new productive oil reservoirs, or constructing complex models of the earth’s subsurface, energy, oil, and gas (EO&G) is a highly data-driven industry. Read more…

India Plots Three-Phase Indigenous Supercomputing Strategy

July 26, 2017

Additional details on India's plans to stand up an indigenous supercomputer came to light earlier this week. As reported in the Indian press, the Rs 4,500-crore (~$675 million) supercomputing project, approved by the Ind Read more…

By Tiffany Trader

Exascale FY18 Budget – The Senate Provides Their Input

July 27, 2017

In the federal budgeting world, “regular order” is a meaningful term that is fondly remembered by members of both the Congress and the Executive Branch. Reg Read more…

By Alex R. Larzelere

India Plots Three-Phase Indigenous Supercomputing Strategy

July 26, 2017

Additional details on India's plans to stand up an indigenous supercomputer came to light earlier this week. As reported in the Indian press, the Rs 4,500-crore Read more…

By Tiffany Trader

Tuning InfiniBand Interconnects Using Congestion Control

July 26, 2017

InfiniBand is among the most common and well-known cluster interconnect technologies. However, the complexities of an InfiniBand (IB) network can frustrate the Read more…

By Adam Dorsey

NSF Project Sets Up First Machine Learning Cyberinfrastructure – CHASE-CI

July 25, 2017

Earlier this month, the National Science Foundation issued a $1 million grant to Larry Smarr, director of Calit2, and a group of his colleagues to create a comm Read more…

By John Russell

Graphcore Readies Launch of 16nm Colossus-IPU Chip

July 20, 2017

A second $30 million funding round for U.K. AI chip developer Graphcore sets up the company to go to market with its “intelligent processing unit” (IPU) in Read more…

By Tiffany Trader

Fujitsu Continues HPC, AI Push

July 19, 2017

Summer is well under way, but the so-called summertime slowdown, linked with hot temperatures and longer vacations, does not seem to have impacted Fujitsu's out Read more…

By Tiffany Trader

Researchers Use DNA to Store and Retrieve Digital Movie

July 18, 2017

From abacus to pencil and paper to semiconductor chips, the technology of computing has always been an ever-changing target. The human brain is probably the com Read more…

By John Russell

The Exascale FY18 Budget – The Next Step

July 17, 2017

On July 12, 2017, the U.S. federal budget for its Exascale Computing Initiative (ECI) took its next step forward. On that day, the full Appropriations Committee Read more…

By Alex R. Larzelere

Google Pulls Back the Covers on Its First Machine Learning Chip

April 6, 2017

This week Google released a report detailing the design and performance characteristics of the Tensor Processing Unit (TPU), its custom ASIC for the inference Read more…

By Tiffany Trader

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Nvidia highlights strengths of its newest GPU silicon in response to Google's report on the performance and energy advantages of its custom tensor processor. Read more…

By Tiffany Trader

Quantum Bits: D-Wave and VW; Google Quantum Lab; IBM Expands Access

March 21, 2017

For a technology that’s usually characterized as far off and in a distant galaxy, quantum computing has been steadily picking up steam. Just how close real-wo Read more…

By John Russell

HPC Compiler Company PathScale Seeks Life Raft

March 23, 2017

HPCwire has learned that HPC compiler company PathScale has fallen on difficult times and is asking the community for help or actively seeking a buyer for its a Read more…

By Tiffany Trader

Trump Budget Targets NIH, DOE, and EPA; No Mention of NSF

March 16, 2017

President Trump’s proposed U.S. fiscal 2018 budget issued today sharply cuts science spending while bolstering military spending as he promised during the cam Read more…

By John Russell

CPU-based Visualization Positions for Exascale Supercomputing

March 16, 2017

In this contributed perspective piece, Intel’s Jim Jeffers makes the case that CPU-based visualization is now widely adopted and as such is no longer a contrarian view, but is rather an exascale requirement. Read more…

By Jim Jeffers, Principal Engineer and Engineering Leader, Intel

Nvidia’s Mammoth Volta GPU Aims High for AI, HPC

May 10, 2017

At Nvidia's GPU Technology Conference (GTC17) in San Jose, Calif., this morning, CEO Jensen Huang announced the company's much-anticipated Volta architecture a Read more…

By Tiffany Trader

How ‘Knights Mill’ Gets Its Deep Learning Flops

June 22, 2017

Intel, the subject of much speculation regarding the delayed, rewritten or potentially canceled “Aurora” contract (the Argonne Lab part of the CORAL “ Read more…

By Tiffany Trader

Leading Solution Providers

Facebook Open Sources Caffe2; Nvidia, Intel Rush to Optimize

April 18, 2017

From its F8 developer conference in San Jose, Calif., today, Facebook announced Caffe2, a new open-source, cross-platform framework for deep learning. Caffe2 is the successor to Caffe, the deep learning framework developed by Berkeley AI Research and community contributors. Read more…

By Tiffany Trader

Reinders: “AVX-512 May Be a Hidden Gem” in Intel Xeon Scalable Processors

June 29, 2017

Imagine if we could use vector processing on something other than just floating point problems.  Today, GPUs and CPUs work tirelessly to accelerate algorithms Read more…

By James Reinders

Russian Researchers Claim First Quantum-Safe Blockchain

May 25, 2017

The Russian Quantum Center today announced it has overcome the threat of quantum cryptography by creating the first quantum-safe blockchain, securing cryptocurrencies like Bitcoin, along with classified government communications and other sensitive digital transfers. Read more…

By Doug Black

MIT Mathematician Spins Up 220,000-Core Google Compute Cluster

April 21, 2017

On Thursday, Google announced that MIT math professor and computational number theorist Andrew V. Sutherland had set a record for the largest Google Compute Engine (GCE) job. Sutherland ran the massive mathematics workload on 220,000 GCE cores using preemptible virtual machine instances. Read more…

By Tiffany Trader

Google Debuts TPU v2 and will Add to Google Cloud

May 25, 2017

Not long after stirring attention in the deep learning/AI community by revealing the details of its Tensor Processing Unit (TPU), Google last week announced the Read more…

By John Russell

Groq This: New AI Chips to Give GPUs a Run for Deep Learning Money

April 24, 2017

CPUs and GPUs, move over. Thanks to recent revelations surrounding Google’s new Tensor Processing Unit (TPU), the computing world appears to be on the cusp of Read more…

By Alex Woodie

Six Exascale PathForward Vendors Selected; DoE Providing $258M

June 15, 2017

The much-anticipated PathForward awards for hardware R&D in support of the Exascale Computing Project were announced today with six vendors selected – AMD Read more…

By John Russell

Top500 Results: Latest List Trends and What’s in Store

June 19, 2017

Greetings from Frankfurt and the 2017 International Supercomputing Conference where the latest Top500 list has just been revealed. Although there were no major Read more…

By Tiffany Trader

  • arrow
  • Click Here for More Headlines
  • arrow
Share This