TeachOpenCADD is a free online platform that offers solutions to common 16 computer-aided drug design (CADD) tasks using Python programming and open-source data and 17 packages. The material is presented through interactive Jupyter notebooks, accommodating 18 users from various backgrounds and programming levels. 19 Due to the tremendous impact of deep learning (DL) methods in drug design, the 20 TeachOpenCADD platform has been expanded to include an introduction to molecular DL tasks. 21 This edition provides an overview of DL and its application in drug design, highlighting the usage 22 of diverse molecular representations in this field. The platform introduces various neural 23 network architectures, including graph neural networks (GNNs), equivariant graph neural 24 networks (EGNNs), and recurrent neural networks (RNNs). It demonstrates how to use these 25 architectures for developing predictive models for molecular property and activity prediction, 26 exemplified by the Quantum Machine 9 (QM9), ChEMBL, and Kinase Inhibitor BioActivity (KiBA) 27 data sets. The DL edition covers methods for evaluating the performance of neural networks 28 using uncertainty estimation. Furthermore, it introduces an application of GNNs for 29 protein-ligand interaction predictions, incorporating protein structure and ligand information. 30 The TeachOpenCADD platform is continuously updated with new content and is open to 31 contributions, bug reports, and questions from the community through its GitHub repository 32 (github.com/volkamerlab/teachopencadd). It can be used for self-study, classroom instruction, and 33 research applications, accommodating users from beginners to advanced levels. 34
分享
引用
