Fig. 7
From: A beginner’s approach to deep learning applied to VS and MD techniques
Overview of the generative AE architecture developed by Degiacomi [137]. Of a protein of interest, the flattened Cartesian coordinates of a dataset of conformations are fed to the AE model as input. After learning, the latent space is a low-dimensional representation of the conformational space of the protein. Through interpolation, it now becomes possible to generate new protein conformations, which can be used as starting conformations for other in silico techniques, such as molecular docking or MD simulations