AI News, Artificial intelligence helps researchers predict drug combinations' side effects

Artificial intelligence helps researchers predict drug combinations' side effects

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.

Zitnik and colleagues Monica Agrawal, a master's student, and Jure Leskovec, an associate professor of computer science, lay out an artificial intelligence system for predicting, not simply tracking, potential side effects from drug combinations.

Too many combinations Once available to doctors in a more user-friendly form, Decagon's predictions would be an improvement over what's available now, which essentially comes down to chance -- a patient takes one drug, starts taking another and then develops a headache or worse.

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.

When they searched the medical literature for evidence of ten side effects predicted by Decagon but not in their original data, the team found that five out of the ten have recently been confirmed, lending further credence to Decagon's predictions.

They also hope to create a more user-friendly tool to give doctors guidance on whether it's a good idea to prescribe a particular drug to a particular patient and to help researchers developing drug regimens for complex diseases with fewer side effects.

Artificial intelligence helps Stanford computer scientists predict the side effects of millions of drug combinations

Last month alone, 23 percent of Americans took two or more prescription drugs, according to one CDC estimate.

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.

Once it’s available to doctors in a more user-friendly form, Decagon’s predictions would be an improvement over what’s available now, which essentially comes down to chance – a patient takes one drug, starts taking another and then develops a headache or worse.

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.

When they searched the medical literature for evidence of 10 side effects predicted by Decagon but not in their original data, the team members found that five out of the ten have recently been confirmed, lending further credence to Decagon’s predictions.

They also hope to create a more user-friendly tool to give doctors guidance on whether it’s a good idea to prescribe a particular drug to a particular patient and to help researchers developing drug regimens for complex diseases with fewer side effects.

Graph Neural Network for Multirelational Link Prediction

Decagon's graph convolutional neural network (GCN) model is a general approach for multirelational link prediction in any multimodal network.

The approach constructs a multimodal graph of protein-protein interactions, drug-protein target interactions, and the polypharmacy side effects, which are represented as drug-drug interactions, where each side effect is an edge of a different type.

In computational pharmacology, Decagon creates an opportunity to use large molecular, pharmacological, and patient population data to flag and prioritize polypharmacy side effects for follow-up analysis via formal pharmacological studies.

A multimodal graph consists of protein-protein interactions, drug-protein targets, and drug-drug interactions encoded by 964 different polypharmacy side effects (i.e., edge types ri, i = 1, ..., 964).

The graph encodes information that Ciprofloxacin (node C) taken together with Doxycycline (node D) or with Simvastatin (node S) increases the risk of bradycardia side effect (side effect type r2), and its combination with Mupirocin (M) increases the risk of gastrointestinal bleed side effect r1.

Decagon predicts associations between pairs of drugs and side effects (shown in red) with the goal of identifying polypharmacy side effects, i.e., side effects which cannot be attributed to either individual drug in the pair.

Scientist Use AI to Predict Side Effects From Drug Combinations

With 125 billion possible side effects between all possible pairs of drugs, accurately predicting how a patient may react to a new drug can be a dangerous guessing game.

The new system, dubbed Decagon, could aid doctors when prescribing drugs to patients already on a laundry list of medications, while also assisting researchers in finding better combinations of drugs to treat complex diseases.

“It's practically impossible to test a new drug in combination with all other drugs, because just for one drug that would be 5,000 new experiments,” Marinka Zitnik, a postdoctoral fellow in computer science, said in a statement.

They used more than four million of the 125 billion known associations between drugs and side effects to design a deep learning system that identifies patterns in how side effects arise based on how drugs target different proteins.

Decagon AI system predicts side effects of drug combinations

Stanford researchers have developed an AI tool called Decagon that can predict the potential side effects of drug combinations.

It uses more than four million known associations between drugs and side effects, then applies machine learning to identify patterns and predict how two drugs will interact.

For example, there was no indication in the team’s original data that the combination of atorvastatin, a cholesterol drug, and amlodipine, a blood pressure medication, would lead to muscle inflammation.

When the researchers explored the medical literature for evidence of 10 side effects predicted by Decagon, they found that five out of the 10 have recently been confirmed, lending further credence to the system’s predictions.

AI predicts Drug pair Side Effects | Decagon

Millions of people take upwards of five medications a day, but testing the side effects of such combinations is impractical. Now, Stanford computer scientists have ...