An aging population. Antibiotic-resistant infections. Afflictions by the hundreds that still lack a cure.
The need for new medications is higher than ever, but so is the cost and time to bring them to market. Developing a new drug can cost billions and take as long as 14 years, according to the U.S. Food and Drug Administration. Yet with all that effort, only 8 percent of drugs make it to market, the FDA said.
“We need to make smarter decisions about which potential medicines we develop and test,” said Abraham Heifets, co-founder of San Francisco-based startup Atomwise.
The six-year-old company, a member of our Inception startup incubator program, is working to make that happen by using GPU-accelerated deep learning to predict which molecules are most likely to lead to treatments. It’s already had some success, identifying possible medicines for multiple sclerosis and the deadly Ebola virus.
How Atomwise Finds Drug Candidates
To understand Atomwise, it helps to know a little about how drug discovery works.
Researchers first identify the biological cause of a disease — usually a protein — to target with a treatment. A protein may help a tumor grow or cause inflammation, for example. Next they search for a medicine that will hit that target, inhibiting or boosting its function.
The company’s AtomNet deep learning software sifts through millions of possible molecules for effective treatments. It then analyzes stimulations that show how the potential medicine will behave in the human body.
The software predicts whether the treatment works against the target, how it affects other parts of the body, its toxicity and possible side effects. Atomwise uses our Tesla V100 and other NVIDIA GPUs for both training and inference on AtomNet.
After completing its evaluation, Atomwise delivers the drug candidates to customers, such as the pharmaceutical giant Merck and top research institutions like the Dana Farber Cancer Institute at Harvard, Stanford University and the Baylor School of Medicine. These organizations conduct further studies to determine if a compound can be used as an approved treatment.
For every molecule that becomes a drug, millions might be physically tested and determined to be unsuitable, Heifets said. By using AI to analyze simulations, Atomwise reduces the time researchers spend building and testing new medications that ultimately won’t work out.
“Every other form of manufacturing simulates its prototypes before it builds them,” he added. “Our goal is to give the pharmaceutical industry the same advantage that other industries have.”
According to Heifets, the company’s method is about 100x faster than high-throughput screening, a commonly used technique to automate drug compound evaluation. It’s a million times faster than a medicinal chemist doing custom synthesis. And its hit rate is 10,000x better than wet lab experiments, he said.
Targeting Ebola, Multiple Sclerosis
Atomwise already has made progress on the Ebola virus and multiple sclerosis, which currently lack sufficient treatments. Ebola, with a death rate as high as 90 percent, has killed thousands of people since it appeared in 1976. Atomwise found a drug candidate that may block Ebola’s entry into healthy cells.
Multiple sclerosis, a potentially disabling disease of the brain and spinal cord, affects about 2.3 million people worldwide, according to the National Multiple Sclerosis Society. Devising a cure that can reach the brain is extremely difficult because of what’s known as the blood-brain barrier, which prevents most molecules from entering the brain. A potential cure would have to pass through this barrier.
Atomwise explored 8.2 million molecules to discover several candidates that could prove to be cures. These were effective in animal trials, and have been licensed to a pharmaceutical company in the U.K. for further exploration.
“I want to see that we can solve hard problems and find molecules that become treatments for disease,” Heifets said.
Learn more about NVIDIA technology to advance deep learning in healthcare.
* Main image for this story shows Atomwise’s simulated drug research in which a neural network learns to recognize chemical functional groups. Image courtesy of Atomwise.