Py-Graph: An Easy-To-Use Interface for Building Graph-Based QSAR Models

Graph Neural Networks (GNNs) have been proved to be effective for the prediction of molecules’ properties using the molecular graph. These models not only allow to obtain high classification and regression performances, but, with the help of Explainable Artificial Intelligence, they can be useful for identifying the structural motifs that modulate the biological activity. The 3d-qsar.com portal is an online platform that allows users to build several ligand-based and structure-based models, bridging the need of computational skills. We implemented a novel application, called Py-Graph, to build and explain GNNs for classification and regression tasks. Py-Graph represents the first graphic interface that allows users to build QSAR models with GNNs and visualize the parts of the molecules that most contribute to the prediction.