When setting up a parallel simulation, several key decisions need to be made that can impact the computational resources, wall time, data wrangling, and even the scientific results of a project. Multiple starting configurations When starting your simulations, it is important to consider your starting configurations so that you can effectively sample the relevant states of your system. This could include multiple conformations of the same protein or different modifications to that system, like different bound ligandsor mutants. Using different starting configurations allows you to explore diverse regions of conformational space in parallel, enhancing the robustness of your sampling. Having diverse "adaptive seeding" also enables one to observe conformational transitions in forward and reverse directions, vastly improving the sampling robustness. This approach is especially useful for larger systems, multidomain oligomers, or systems with large conformational flexibility due to disordered loops. However, running more starting configurations can significantly increase computational time and cost, meaning you may have to wait longer to generate a well-sampled dataset.