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.

翻译：分子性质预测是基于人工智能的药物设计领域中最为重要且最具挑战性的任务之一。在当前主流方法中，最常使用的用于训练深度神经网络模型的特征表示是基于SMILES和分子图的，尽管这些方法简洁有效，但也限制了捕捉空间信息的能力。在本工作中，我们提出基于曲率的Transformer，通过引入黎奇曲率的离散化来提升图Transformer神经网络模型在分子图数据上提取结构信息的能力。为了将曲率嵌入模型中，我们在注意力分数计算过程中，将图的曲率信息作为位置编码添加到节点特征中。该方法无需改变原始网络架构即可引入图数据中的曲率信息，并且具有扩展到其他模型的潜力。我们在包括PCQM4M-LST、MoleculeNet在内的化学分子数据集上进行了实验，并与Uni-Mol、Graphormer等模型进行了比较，结果表明该方法能够达到最先进的结果。这证明了离散化的黎奇曲率在描述图分子数据的局部几何结构的同时，也反映了结构与功能之间的关系。