Molecular property prediction

Machine learning approaches can be used to predict various molecular properties simultaneously, and used to screen millions of commercially available compounds. In the Molecular Machine Learning team, we are interested in boosting the potential of such approaches in challenging real-life scenarios, such as low-data regime training, out of distribution performance, and prediction of activity cliffs. Our ultimate goal is to augment the potential of molecular property prediction algorithms in the context of drug discovery.