To train a surrogate model for HOMO-LUMO gap prediction, we use the multi-headed HydraGNN package developed earlier by some of our team . This surrogate, allowing multi-headed output, is ideally suited for the simultaneous prediction of multiple important molecular characteristics, such as electronic properties and synthesizability scoring. Training of HydraGNN can be performed on multiple GPU nodes. The trained surrogate model is then used to generate hydra score for subsequent molecule generation operation. Details on the HydraGNN surrogate are given below.