Property-and structure-based descriptors are two alternative features that are used to train ML models. DFT calculations are used to determine chemical characteristics such as molecular orbitals. Whereas, the structure-based descriptor is produced directly from SMILES, which includes information about the topology, connectivity, and subfragment of the molecules. The latter enables high-throughput screening of candidates at a significantly lower computational cost, i.e. without DFT calculations.