In order to build an accurate RT prediction model and compare it with the most popular existing approaches, we selected XGBoost 31 , Chemprop , and two Graph Convolutional Neural Networks (GCNNs) provided by PyTorch Geometric 32 : Graph Attention Convolutional Networks (GATv2Conv) and Topology Adaptive Graph Convolutional Networks (TAGConv) . It should be noted that most of the recent studies on QSRR tend to choose a GNN-based model as the optimal approach.