Graph-Aware Transformer: Is Attention All Graphs Need?
Table 3: Comparison of Top-1 Accuracy Obtained by the Different Single-Model Methods on the Current Benchmark
['[BOLD] Models', 'S2S Schwaller et al. ( 2018 )', 'WLDN Jin et al. ( 2017 )', 'GTPN Do et al. ( 2019 )', 'WLDN5 Coley et al. ( 2019 )', 'MT Schwaller et al. ( 2019 )', 'GRAT']
MT Schwaller et al. showing the best performance represents the input and output chemical compounds in SMILES and takes the sequence-to-sequence approach with Transformer. Note that we only take atom labels for nodes and bond types and distances for edges from the graph structure, hence there is loss of information during the transformation of SMILES into the graph structure. We expect that the performance could be improved if GRAT considers the other chemical information carried in SMILES such as formal charges and valences. Adding more complete molecule information to GRAT is remained as future work.