In 2007, Luke Arrigoni, an AI entrepreneur, earned 63,000 at his first job as a junior software developer. Today, he says AI tools that write better code than he did back then cost just 120 annually. The numbers don't sit right with him. Arrigoni, who runs Loti AI, a company that helps Hollywood stars find unauthorized deepfakes, worries that underpriced AI tools encourage companies to eliminate entry-level roles. He wants to flip the incentive structure so people's careers don't end before they begin.

Adverse reactions caused by drug combinations are an increasingly common phenomenon, making their accurate prediction an important challenge in modern medicine. However, the polynomial nature of this problem renders lab-based identification of adverse reactions insufficient. Dozens of computational approaches have therefore been proposed for the task in recent years, with varying degrees of success. One group of methods that has seemingly been under-utilised in this area is tensor factorisation, despite their clear applicability to this type of data. In this work, we apply three such models to a benchmark dataset in order to compare them against established techniques. We find, in contrast to previous reports, that for this task tensor factorisation models are competitive with state-of-the-art graph neural network models and we recommend that future work in this field considers cheaper methods with linear complexity before running costly deep learning processes.

People who need medication rarely take just one pill. Many of those who have been prescribed medications for health reasons take upwards of five medications per day. According to the U.S. Centers for Disease Control and Prevention, the percentage of persons using at least one prescription drug in the past 30 days, in the U.S., is 48.9 percent; and the percentage of people using three or more prescription drugs ('polypharmacy') in the past 30 days stands at 23.1 percent; whereas those using five or more prescription drugs in the past 30 days is 11.9 percent. The figures for the U.S. will mirror those of many other high-income countries. From the ubiquitous aspirin to the most sophisticated prescription medicine on the market, all medications come with side effects.

You are free to share this article under the Attribution 4.0 International license. Researchers have created an artificial intelligence system for predicting, not simply tracking, potential side effects from drug combinations. Last month alone, 23 percent of Americans took two or more prescription drugs, according to one CDC estimate. Furthermore, 39 percent over age 65 take five or more, a number that's increased three-fold in the last several decades. And if that isn't surprising enough, try this one: in many cases, doctors have no idea what side effects might arise from adding another drug to a patient's personal pharmacy.

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. With some new drug combinations, she said, "truly we don't know what will happen." But computer science may be able to help. In a paper presented July 10th at the 2018 meeting of the International Society for Computational Biology in Chicago. 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.

IMAGE: Marinka Zitnik and colleagues designed a system to predict billions of potential drug combination side effects. Last month alone, 23 percent of Americans took two or more prescription drugs, according to one CDC estimate, and 39 percent over age 65 take five or more, a number that's increased three-fold in the last several decades. And if that isn't surprising enough, try this one: in many cases, doctors have no idea what side effects might arise from adding another drug to a patient's personal pharmacy. 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. With some new drug combinations, she said, "truly we don't know what will happen."

The use of multiple drugs, termed polypharmacy, is common to treat patients with complex diseases or co-existing medical conditions. However, a major consequence of polypharmacy is a much higher risk of side effects for the patient. Polypharmacy side effects emerge because of drug interactions, in which activity of one drug may change, favorably or unfavorably, if taken with another drug. The knowledge of drug interactions is limited because these complex relationships are usually not observed in small clinical testing. Discovering polypharmacy side effects thus remains a challenge with significant implications for patient mortality and morbidity. Here we introduce Decagon, an approach for modeling polypharmacy side effects. The approach constructs a multimodal graph of protein-protein interactions, drug-protein interactions, and the polypharmacy side effects, which are represented as drug-drug interactions, where each side effect is an edge of a different type. Decagon is developed specifically to handle such multimodal graphs with a large number of edge types. Our approach develops a new graph convolutional neural network for multirelational link prediction in multimodal networks. Unlike approaches limited to predicting simple drug-drug interaction values, Decagon can predict the exact side effect, if any, through which a given drug combination manifests clinically. Decagon accurately predicts polypharmacy side effects, outperforming baselines by up to 69%. Furthermore, Decagon models particularly well side effects that have a strong molecular basis, while on predominantly non-molecular side effects, it achieves good performance because of effective sharing of model parameters across edge types. Decagon creates an opportunity to use large molecular and patient population data to flag and prioritize polypharmacy side effects for follow-up analysis via formal pharmacological studies.