Application of the GNN to larger molecules related to biomass conversion, polyurethane synthesis and plastic chemical recycling shows the potential of statistical learning methods to reach areas that could not be easily addressed by standard first principles techniques. Our work paves the way for building graph-based frameworks capable to learn complex chemical patterns from high-quality datasets composed by small molecules.