Nowadays, property predictions are reconsider, and novel methodologies based on multi-parametric and multi-disciplinary data have emerged. For example, novel binding affinity predictors use a combination of ligand-and target-based approaches, like molecular similarity and molecular dynamics techniques, fused with NMR spectral data to predict the putative activity of compounds . Another key example is the use of chemical, in vitro, and in vivo data to anticipate future side effects of lead compounds . And, recent advances, like network pharmacology, have opened the possibility of decoding complex natural product mixtures to identify the compounds associated with their reported bioactivity . Namely, the paradigm to predict properties based on unique kinds of data (i.e. chemical, biological, or clinical) has been broken now.