OREANDA-NEWS. Breakfast cereal. Baby shampoo. Cough syrup. Dishwasher detergent.

This grocery list is also a list of products that can make you sick. Very sick, in fact, if they aren’t thoroughly tested before hitting supermarket shelves.

Some contain a variety of chemical compounds people are exposed to every day, which could be toxic. Toxins come from just about anywhere—environmental pollution, ingredients in cosmetics or cleaning products, medicines, food additives and pesticides, to name just a few sources.

Before a box is picked off a shelf and dropped into a shopping cart, months of testing went into the contents listed on the label. With new products reaching the market every day, drug makers, food manufacturers and retailers no longer rely on laborious tests to assess data on thousands of chemicals. They rely on researchers using GPUs to help determine friend from foe.

Big Data, With Computational Power to Match

The Institute of Bioinformatics at the Johannes Kepler University in Linz, Austria, is the first organization to successfully apply a deep network approach for toxicity prediction.

Map Panel is a mock-up showing how deep learning could be used to group compounds together.
This Map Panel mock-up shows how deep networks could be used to group compounds together.
This placed it among five finalists for NVIDIA’s 2015 Global Impact Award. The annual grant of \$150,000 is given to researchers using NVIDIA technology for groundbreaking work that addresses social, humanitarian and environmental problems.

Researchers found the high computational costs of this approach can only be tackled with GPUs.

“High-throughput biotechnology gave us the data—big data—but how do you crack that data? Now, we have the hardware to process the data,” said Dr. Sepp Hochreiter, who heads the institute. “We can use compute GPUs and standard graphics cards to open up the neutral networks and we have access to vast computational power.”

“It’s infeasible for government agencies or big pharmaceutical companies to test for chemical toxicity and undesired side effects using biological methods,” he said. This would mean long test periods and live test subjects. “Using computational models makes it much easier,” Hochreiter said.

Tesla GPUs Crunch Deep Networks Numbers

Deep networks are a type of artificial neural network characterized by a large number of layers and hidden coding units. To tackle the deep networks on toxicity prediction data sets, the institute uses four NVIDIA Tesla K40 GPUs—part of the Tesla accelerated computing platform of GPU accelerators and enabling software.

The GPUs are mounted into a Dell R920 server with four octacore CPUs and 512GB main memory. The programs for training the deep networks on the Tesla K40 GPUs were written in CUDA.

Pharmacophore
Scientists examine pieces of chemical structure to find patterns that indicate reactive centers. Molecules are then classified based on the results.
“At first we started out with small-scale computer graphics cards, the kind that you play games on. But then we got the Tesla K40 cards with huge memory so we could store the data on the card itself,” said Thomas Unterthiner, who is working on the research at the institute, while studying for his Ph.D. “The neural network runs on the GPU—the CPU hardly does anything. It’s the GPU that does all the number crunching.”

Now, using powerful computational models, it’s becoming faster and easier to predict whether a substance may be toxic by interrupting certain biological pathways based on the chemical structure. This helps scientists better determine which chemicals need further testing.

Among the institute’s achievements is a high accuracy rate in toxicity prediction, which this January helped it win the Tox21 Data Challenge organized by a group that included the U.S. National Institute of Environmental Health Sciences, U.S. Environmental Protection Agency and U.S. Food and Drug Administration.