  Since the introduction of artificial intelligence in medicinal chemistry, the
necessity has emerged to analyse how molecular property variation is modulated
by either single atoms or chemical groups. In this paper, we propose to train
graph-to-graph neural network using semi-supervised learning for attributing
structure-property relationships. As initial case studies we apply the method
to solubility and molecular acidity while checking its consistency in
comparison with known experimental chemical data. As final goal, our approach
could represent a valuable tool to deal with problems such as activity cliffs,
lead optimization and de-novo drug design.
