Machine Learning Gets Smarter To Speed Up Drug Discovery

Researchers at Carnegie Mellon University developed a self-supervised learning framework that leverages the large amounts of unlabeled data that other models can't. Predicting molecular properties quickly and accurately is important to advancing scientific discovery and application in areas ranging from materials science to pharmaceuticals. Because experiments and simulations to explore potential options are time-consuming and costly, scientists have investigated using machine learning (ML) methods to aid in computational chemistry research. But most ML models can only make use of known, or labeled, data. This makes it nearly impossible to predict with accuracy the properties of novel compounds. In an industry like drug discovery, there are millions of molecules from which to select for use in a potential drug candidate. A prediction error as small as 1% can lead to the misidentification of more than 10,000 molecules.