Graphormers have emerged as a promising approach in the field of graph machine learning, demonstrating superior performance on various molecular datasets. In this study, we investigate the effectiveness of Graphormers on two benchmark datasets from MoleculeNet. By leveraging the transformer architecture and self-attention mechanisms, Graphormers excel in incorporating the structural inductive biases of graph representations into transformer learning. On comparing various graph machine learning models such as Graph Convolutional Network (GCN) and Graph Attention Network (GAT) and observed that our solution outperformed these established methods on both datasets as evidenced by high ROC-AUC scores and RMSE values with its advanced encoding mechanisms overcoming the limitations in capturing long-range dependencies, susceptibility to overfitting, or computational inefficiencies. The results of our study emphasize the effectiveness of Graphormers in predicting molecular properties, indicating their potential in driving advancements in drug discovery and computational chemistry research.