Biomedical engineers at Duke University have developed a new method to improve the effectiveness of machine learning models when searching for new molecular therapeutics. This approach uses an algorithm to identify gaps in datasets, allowing researchers to more than double their accuracy in some cases. This could make it easier for scientists to identify and classify molecules with characteristics that could be useful for the development of new drug candidates and other materials.
