Fig. 14

Overview of the MD trajectory data analysis workflow developed by Plante et al. [174] Ligands representing functionally-selective classes (e.g., full agonists, inverse agonists, partial agonists) are docked onto proteins of interest to create systems for MD simulations. Relevant frames from those simulations are selected for the development of DL training datasets. From these frames the ligand atoms are extracted, but a label is provided with each frame detailing the class of the ligand previously bound in the conformation to allow loss optimization. The protein conformations undergo a positional and orientational structure scrambling procedure to remove bias, after which they are translated into a 2D picture-like format. Each pixel in a picture represents an atom of the protein conformation, with its RGB values corresponding to the XYZ coordinates of the atom. A CNN model based on the DenseNet architecture is then developed and trained on the pictures and class labels to predict the label of the ligand that was bound in a conformation. After optimization, the network’s decisions can be analyzed using saliency mapping, as to show the protein regions/structural features relevant for the binding of different ligands