Machine learning gets smarter to speed up drug discovery

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.