By Mike May, PhD<\/p>\n
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.<\/p>\n
\u201cAI is an umbrella for different types of machine learning models that can be trained toward different goals,\u201d says Noor Shaker, PhD, senior vice president and general manager at X-Chem. For example, AI encompasses models for supervised and unsupervised learning.<\/p>\n
Supervised learning algorithms are trained on examples. \u201cThis requires an existing body of large, labeled\u2014annotated\u2014datasets that can be used to train a machine learning model that can be later used to assign labels for new data points,\u201d Shaker notes. \u201cSuch systems are used in prediction tasks, such as the prediction of drug-target interactions or drug profiles.\u201d<\/p>\n
Unsupervised learning algorithms, in contrast, explore data for new patterns with no expectations. These algorithms, Shaker points out, \u201care used to learn from what we know about a specific domain, with the aim of creating and expanding the domain space.\u201d<\/p>\n