University of Illinois: Accelerated molecular modelling enables rapid response to H1N1


A first step in mitigating a global pandemic, like H1N1, requires quickly developing drugs to effectively treat a virus that is new and likely to evolve.


This requires a compute-intensive process to determine how, in the case of H1N1, mutations of the flu virus protein could disrupt the binding pathway of the vaccine Tamiflu, rendering it potentially ineffective.

This determination involved a daunting simulation of a 35,000- atom system, something a group of University of Illinois, Urbana- Champaign scientists, led by John Stone, decided to tackle in a new way using GPUs.

Conducting this kind of simulation on a CPU would take more than a month to calculate...and that would only amount to a single simulation, not the multiple simulations that constitute a complete study.


Stone and his team turned to the NVIDIA CUDA parallel processing architecture running on Tesla GPUs to perform their molecular modeling calculations and simulate the drug resistance of H1N1 mutations. Thanks to GPU technology, the scientists could efficiently run multiple simulations and achieve potentially life-saving results faster.


The GPU-accelerated calculation was completed in just over an hour. The almost thousandfold improvement in performance available through GPU computing and advanced algorithms empowered the scientists to perform “emergency computing” to study biological problems of extreme relevance and share their results with the medical research community.

This speed and performance increase not only enabled researchers to fulfill their original goal—testing Tamiflu’s efficacy in treating H1N1 and its mutations—but it also bought them time to make other important discoveries. Further calculations showed that genetic mutations which render the swine or avian flu resistant to Tamiflu had actually disrupted the “binding funnel,” providing new understanding about a fundamental mechanism behind drug resistance.

In the midst of the H1N1 pandemic, the use of improved algorithms based on CUDA and running on Tesla GPUs made it possible to produce actionable results about the efficacy of Tamiflu during a single afternoon. This would have taken weeks or months of computing to produce the same results using conventional approaches.

Computational scientists and researchers who are using GPUs to accelerate their applications are seeing results in days instead of months, even minutes instead of days.