TeachOpenCADD goes Deep Learning: Open-source Teaching Platform Exploring Molecular DL Applications

Abstract

TeachOpenCADD is a free online platform that offers solutions to common computer-aided drug design (CADD) tasks using Python programming and open-source data and packages. The material is presented through interactive Jupyter notebooks, accommodating users from various backgrounds and programming levels. Due to the tremendous impact of deep learning (DL) methods in drug design, the TeachOpenCADD platform has been expanded to include an introduction to molecular DL tasks. This edition provides an overview of DL and its application in drug design, highlighting the usage of diverse molecular representations in this field. The platform introduces various neural network architectures, including graph neural networks (GNNs), equivariant graph neural networks (EGNNs), and recurrent neural networks (RNNs). It demonstrates how to use these architectures for developing predictive models for molecular property and activity prediction, exemplified by the Quantum Machine 9 (QM9), ChEMBL, and Kinase Inhibitor BioActivity (KiBA) data sets. The DL edition covers methods for evaluating the performance of neural networks using uncertainty estimation. Furthermore, it introduces an application of GNNs for protein-ligand interaction predictions, incorporating protein structure and ligand information. The TeachOpenCADD platform is continuously updated with new content and is open to contributions, bug reports, and questions from the community through its GitHub repository (https://github.com/volkamerlab/teachopencadd). It can be used for self-study, classroom instruction, and research applications, accommodating users from beginners to advanced levels.