Here, we present a new GNN architecture trained with an extensive DFT dataset consisting of closed-shell organic molecules (containing ≈ 2900 entries and all common functional groups) adsorbed on transition metal surfaces, able to predict the adsorption energy of closed-shell molecules with an error comparable to DFT, using a simple molecular representation. The GNN is capable of predicting the adsorption energy of larger molecules derived from biomass, polyurethanes and plastics, allowing to study chemical systems not amenable to DFT.