T019: MD simulations. In this talktorial, we explain the key concepts behind MD simulations and provide the code to run a short MD simulation of EGFR in complex with a ligand on a local machine or on Google Colab , which allows for GPU-accelerated simulations. The protein and ligand are thereby separately prepared with pdbfixer [60] and RDKit , and subsequently combined using MDTraj and openff-toolkit . The simulation is run with OpenMM , a high-performance toolkit for molecular simulation including language bindings for Python. The talktorial produces a 100 ps trajectory if run on Google Colab. On a local machine, only 20 fs are generated by default to keep computational efforts reasonable. However, the simulation time can be increased by the user in case a local GPU is available. We refer to the work by Arantes et al. for further reading on different MD protocols run with OpenMM using Jupyter notebooks on Google Colab. T020: Analyzing MD simulations. We analyze and visualize the results of the trajectory generated with the previous talktorial using the Python packages MDAnalysis and NGLView. First, the protein is structurally aligned across all trajectory frames, followed by calculating the root-mean-square deviation (RMSD) for different system components; i.e. protein, backbone, and ligand. Then, we take a closer look at a selected interaction between ligand and protein atoms, showcasing the contribution of distance and angle to the hydrogen bond strengths. New talktorials on common tasks in structural bioinformatics to assess interaction patterns. T014 detects the binding site in an EGFR kinase structure and compares the prediction to the binding site defined by KLIFS . T015 performs a re-docking for an EGFR-ligand complex with Smina . T016 detects protein-ligand interactions in an EGFRligand complex structure with PLIP . T017 introduces basic and advanced usages of the molecular visualization tool NGLView , which is used throughout most of TeachOpenCADD's talktorials. Finally, T018 outlines a fully automated lead optimization pipeline: Based on an input structure, the pocket is detected and a set of compounds similar to a selected ligand are fetched from PubChem . These compounds are docked into the selected binding site. The most promising compounds w.r.t. docking scores and interaction profiles are proposed as optimized compounds. with OpenMM . T020 analyzes the resulting MD trajectory with a focus on the root-mean-square deviation (RMSD) between trajectory frames and the dynamics of protein-ligand interactions using MDAnalysis .