Still, there are further cases to investigate. Experiments should be expanded to bigger data sets with higher number of descriptors/fingerprints/learned representations, which will also require more refined feature grouping for grouped penalties. Furthermore, can multiple interrelated properties be predicted with multi-task penalties? In an attempt to select a small number of joint features for each task, one can try combining the Frobenius norm of residuals as an empirical risk L in [� � �] with the sum of Elastic Net or Group Lasso penalties of each task as a grouped penalty C.