It’s hard to tell much from the Nov. 5, 2014 University of Toronto news release by Michael Kennedy (also on EurekAlert but dated Nov. 10, 2014) about in silico drug testing focused on finding a treatment for ebola,
The University of Toronto, Chematria and IBM are combining forces in a quest to find new treatments for the Ebola virus.
Using a virtual research technology invented by Chematria, a startup housed at U of T’s Impact Centre, the team will use software that learns and thinks like a human chemist to search for new medicines. Running on Canada’s most powerful supercomputer, the effort will simulate and analyze the effectiveness of millions of hypothetical drugs in just a matter of weeks.
“What we are attempting would have been considered science fiction, until now,” says Abraham Heifets (PhD), a U of T graduate and the chief executive officer of Chematria. “We are going to explore the possible effectiveness of millions of drugs, something that used to take decades of physical research and tens of millions of dollars, in mere days with our technology.”
The news release makes it all sound quite exciting,
Chematria’s technology is a virtual drug discovery platform based on the science of deep learning neural networks and has previously been used for research on malaria, multiple sclerosis, C. difficile, and leukemia. [emphases mine]
Much like the software used to design airplanes and computer chips in simulation, this new system can predict the possible effectiveness of new medicines, without costly and time-consuming physical synthesis and testing. [emphasis mine] The system is driven by a virtual brain that teaches itself by “studying” millions of datapoints about how drugs have worked in the past. With this vast knowledge, the software can apply the patterns it has learned to predict the effectiveness of hypothetical drugs, and suggest surprising uses for existing drugs, transforming the way medicines are discovered.
My understanding is that Chematria’s is not the only “virtual drug discovery platform based on the science of deep learning neural networks” as is acknowledged in the next paragraph. In fact, there’s widespread interest in the medical research community as evidenced by such projects as Seurat-1’s NOTOX* and others. Regarding the research on “malaria, multiple sclerosis, C. difficile, and leukemia,” more details would be welcome, e.g., what happened?
A Nov. 4, 2014 article for Mashable by Anita Li does offer a new detail about the technology,
Now, a team of Canadian researchers are hunting for new Ebola treatments, using “groundbreaking” artificial-intelligence technology that they claim can predict the effectiveness of new medicines 150 times faster than current methods.
With the quotes around the word, groundbreaking, Li suggests a little skepticism about the claim.
Here’s more from Li where she seems to have found some company literature,
Chematria describes its technology as a virtual drug-discovery platform that helps pharmaceutical companies “determine which molecules can become medicines.” Here’s how it works, according to the company:
The system is driven by a virtual brain, modeled on the human visual cortex, that teaches itself by “studying” millions of datapoints about how drugs have worked in the past. With this vast knowledge, Chematria’s brain can apply the patterns it perceives, to predict the effectiveness of hypothetical drugs, and suggest surprising uses for existing drugs, transforming the way medicines are discovered.
I was not able to find a Chematria website or anything much more than this brief description on the University of Toronto website (from the Impact Centre’s Current Companies webpage),
Chematria makes software that helps pharmaceutical companies determine which molecules can become medicines. With Chematria’s proprietary approach to molecular docking simulations, pharmaceutical researchers can confidently predict potent molecules for novel biological targets, thereby enabling faster drug development for a fraction of the price of wet-lab experiments.
Chematria’s Ebola project is focused on drugs already available but could be put to a new use (from Li’s article),
In response to the outbreak, Chematria recently launched an Ebola project, using its algorithm to evaluate molecules that have already gone through clinical trials, and have proven to be safe. “That means we can expedite the process of getting the treatment to the people who need it,” Heifets said. “In a pandemic situation, you’re under serious time pressure.”
He cited Aspirin as an example of proven medicine that has more than one purpose: People take it for headaches, but it’s also helpful for heart disease. Similarly, a drug that’s already out there may also hold the cure for Ebola.
I recommend reading Li’s article in its entirety.
The University of Toronto news release provides more detail about the partners involved in this ebola project,
… The unprecedented speed and scale of this investigation is enabled by the unique strengths of the three partners: Chematria is offering the core artificial intelligence technology that performs the drug research, U of T is contributing biological insights about Ebola that the system will use to search for new treatments and IBM is providing access to Canada’s fastest supercomputer, Blue Gene/Q.
“Our team is focusing on the mechanism Ebola uses to latch on to the cells it infects,” said Dr. Jeffrey Lee of the University of Toronto. “If we can interrupt that process with a new drug, it could prevent the virus from replicating, and potentially work against other viruses like Marburg and HIV that use the same mechanism.”
The initiative may also demonstrate an alternative approach to high-speed medical research. While giving drugs to patients will always require thorough clinical testing, zeroing in on the best drug candidates can take years using today’s most common methods. Critics say this slow and prohibitively expensive process is one of the key reasons that finding treatments for rare and emerging diseases is difficult.
“If we can find promising drug candidates for Ebola using computers alone,” said Heifets, “it will be a milestone for how we develop cures.”
I hope this effort along with all the others being made around the world prove helpful with Ebola. it’s good to see research into drugs (chemical formulations) that are familiar to the medical community and can be used for a different purpose than originally intended. Drugs that are ‘repurposed’ should be cheaper than new ones and we already have data about side effects.
As for the “milestone for how we develop cures,” this team’s work along with all the international research on this front and on how we assess toxicity should certainly make that milestone possible.
* Full disclosure: I came across Seurat-1’s NOTOX project when I attended (at Seurat-1’s expense) the 9th World Congress on Alternatives to Animal Testing held in Aug. 2014 in Prague.