How to predict the side effects of millions of drug combinations

From Kurzweil AI.net (July 17):

Millions of people take up to five or more medications a day, but doctors have no idea what side effects might arise from adding another drug.*

Now, Stanford University computer scientists have developed a deep-learning system (a kind of AI modeled after the brain) called Decagon** that could help doctors make better decisions about which drugs to prescribe. It could also help researchers find better combinations of drugs to treat complex diseases.

The problem is that with so many drugs currently on the U.S. pharmaceutical market, “it’s practically impossible to test a new drug in combination with all other drugs, because just for one drug, that would be five thousand new experiments,” said Marinka Zitnik, a postdoctoral fellow in computer science and lead author of a paper presented July 10 at the 2018 meeting of the International Society for Computational Biology.

With some new drug combinations (“polypharmacy”), she said, “truly we don’t know what will happen.”

How proteins interact and how different drugs affect these proteins

So Zitnik and associates created a network describing how the more than 19,000 proteins in our bodies interact with each other and how different drugs affect these proteins. Using more than 4 million known associations between drugs and side effects, the team then designed a method to identify patterns in how side effects arise, based on how drugs target different proteins, and also to infer patterns about drug-interaction side effects.***

Based on that method, the system could predict the consequences of taking two drugs together. [read more]

Right now the computer scientists’ program only works for two drugs but they are trying to make it work for multiple drugs. A good start nonetheless.