A hands-on tutorial on quantitative structure-activity relationships using fully expressive graph neural networks

This hands-on tutorial offers a practical guide to implementing Graph Neural Networks (GNNs) within the scope of Quantitative Structure-Activity Relationship (QSAR) modeling. Readers will be guided through a detailed, step-by-step process of applying a state-of-the-art GNN algorithm for activity predictions. It is anticipated that this practical tutorial will provide readers with sufficient knowledge to apply GNNs to their problems as well as to gain a good intuition of the underlying concepts. Specifically, the first part of this tutorial introduces the basic theory of GNNs for QSAR, including molecular graphs and their transformation in the GNN. The second part translates this theory into practical code, culminating in a working implementation of a QSAR model using a GNN. Basic knowledge of QSAR modeling and basic proficiency in the Python programming language and Keras deep learning framework is required. The code for the implementation detailed in this tutorial is available at https://github.com/akensert/molgraph/.