Skip to Content
Artificial intelligence

AI could help design better drugs that don’t clash with other medication

February 6, 2020
A pharmacist selects a bottle of medication from shelves and shelves of options.
A pharmacist selects a bottle of medication from shelves and shelves of options.
A pharmacist selects a bottle of medication from shelves and shelves of options.Joe Raedle/Getty

A new system that can predict a proposed drug’s chemical structure could help prevent adverse drug interactions, one of the leading causes of patient death.

Why it matters: According to the FDA, serious adverse drug interactions could kill more than 100,000 hospitalized people in the US every year. But traditional ways of avoiding such interactions during drug development require expensive and laborious physical testing and clinical trials to catalogue all the proposed drug’s possible chemical interactions with existing ones.

How it works: The system takes in two different drugs and generates a prediction for how or whether they will interact. To get there, the researchers first translated the 3D chemical structures of drugs into a character format known as SMILES that could be read by a neural network. The drug melatonin, for example, is represented by “CC(=O)NCCC1=CNc2c1cc(OC)cc2,” while morphine is represented by “CN1CCC23C4OC5=C(O)C=CC(CC1C2C=CC4O)=C35.”

They then trained a neural network on a database of known drug interactions. The resulting system predicts the probability that two drugs will have an adverse interaction and shows the particular parts of the molecule that contributed to that prediction.

The results: When the researchers tested their system on two common drug interaction data sets, it performed better than state-of-the-art results from existing AI systems. The paper, which was led by researchers at health information technology company IQVIA, is being presented at the proceedings of the Association for the Advancement of Artificial Intelligence later this week.

Co-pilot: The new techniques for analyzing chemical data could have many other applications, including drug and material design. “There's just an awful lot of the modern world that depends on chemistry,” says David Cox, the IBM director of the MIT-IBM Watson AI Lab, a member of which coauthored the paper. “There’s tremendous potential for AI to be a copilot for us, augmenting our ability to reason about chemical interactions, properties, and qualities.”

Correction: An earlier version of this article misstated David Cox‘s title as director and SMILES as a new convention. The article has since been updated.

To have more stories like this delivered directly to your inbox, sign up for our Webby-nominated AI newsletter The Algorithm. It's free.

Deep Dive

Artificial intelligence

chasm concept
chasm concept

Artificial intelligence is creating a new colonial world order

An MIT Technology Review series investigates how AI is enriching a powerful few by dispossessing communities that have been dispossessed before.

open sourcing language models concept
open sourcing language models concept

Meta has built a massive new language AI—and it’s giving it away for free

Facebook’s parent company is inviting researchers to pore over and pick apart the flaws in its version of GPT-3

spaceman on a horse generated by DALL-E
spaceman on a horse generated by DALL-E

This horse-riding astronaut is a milestone in AI’s journey to make sense of the world

OpenAI’s latest picture-making AI is amazing—but raises questions about what we mean by intelligence.

labor exploitation concept
labor exploitation concept

How the AI industry profits from catastrophe

As the demand for data labeling exploded, an economic catastrophe turned Venezuela into ground zero for a new model of labor exploitation.

Stay connected

Illustration by Rose WongIllustration by Rose Wong

Get the latest updates from
MIT Technology Review

Discover special offers, top stories, upcoming events, and more.

Thank you for submitting your email!

Explore more newsletters

It looks like something went wrong.

We’re having trouble saving your preferences. Try refreshing this page and updating them one more time. If you continue to get this message, reach out to us at with a list of newsletters you’d like to receive.