The prediction of molecular properties is one of the most important and
challenging tasks in the field of artificial intelligence-based drug design.
Among the current mainstream methods, the most commonly used feature
representation for training DNN models is based on SMILES and molecular graphs,
although these methods are concise and effective, they also limit the ability
to capture spatial information. In this work, we propose Curvature-based
Transformer to improve the ability of Graph Transformer neural network models
to extract structural information on molecular graph data by introducing
Discretization of Ricci Curvature. To embed the curvature in the model, we add
the curvature information of the graph as positional Encoding to the node
features during the attention-score calculation. This method can introduce
curvature information from graph data without changing the original network
architecture, and it has the potential to be extended to other models. We
performed experiments on chemical molecular datasets including PCQM4M-LST,
MoleculeNet and compared with models such as Uni-Mol, Graphormer, and the
results show that this method can achieve the state-of-the-art results. It is
proved that the discretized Ricci curvature also reflects the structural and
functional relationship while describing the local geometry of the graph
molecular data