The simplified molecular-input line-entry system (SMILES) and the molecular graph are commonly used in chem-informatics to represent a molecule. Transformers are widely used for encoding SMILES to learn the relationship between elements that are far away from each other, while Graph Convolutional Networks (GCNs) are popular in graph representation learning and mostly focus on local structures. Since different information can be extracted from the SMILES string and the molecular graph, their integration might benefit the molecular property prediction task. In this work, we propose a bimodal supervised contrastive learning (BSCL) framework to integrate the SMILES string and the molecular graph in a unified network. Furthermore, the vanilla supervised contrastive loss (SCL) is not suitable for regression tasks, hence we design a weighted SCL to solve the problem. Six publicly available molecular property datasets are used to evaluate the proposed BSCL method, and our results show that the proposed bimodal method is superior to using the SMILES string or the molecular graph alone. Our code is released at https://github.com syanl992/BSCL.