Toward big data in QSAR/QSPR

We investigate a prospective path to processing “big data” in the field of computer-aided drug design, motivated by the expected increase of the size of available databases. We argue that graph machines, which exempt the designer of a predictive model from handcrafting, selecting and computing ad hoc molecular descriptors, may open a way toward efficient model design procedures. We recall the principle of graph machines, which perform predictions directly from the molecular structure described as a graph, without resorting to descriptors. We discuss scalability issues in the present implementation of graph machines, and we describe an application to the prediction of an important thermodynamic property of contrast agents for MRI imaging.