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AI Takes On Drug Discovery

Published in Artificial Intelligence, Drugs development.

By Mike May, PhD

Artificial intelligence appears poised to transform many aspects of our lives, and drug discovery is no exception. By removing much of the trial and error from drug discovery, AI reveals new targets and tactics for treating diseases.

“AI is an umbrella for different types of machine learning models that can be trained toward different goals,” says Noor Shaker, PhD, senior vice president and general manager at X-Chem. For example, AI encompasses models for supervised and unsupervised learning.

Supervised learning algorithms are trained on examples. “This requires an existing body of large, labeled—annotated—datasets that can be used to train a machine learning model that can be later used to assign labels for new data points,” Shaker notes. “Such systems are used in prediction tasks, such as the prediction of drug-target interactions or drug profiles.”

Unsupervised learning algorithms, in contrast, explore data for new patterns with no expectations. These algorithms, Shaker points out, “are used to learn from what we know about a specific domain, with the aim of creating and expanding the domain space.”