Regarding the ESR approach, we began by training Skalic's Variational Autoencoder (VAE) to generate SMILES strings resembling 3D shape and pharmacophoric features of a given seed molecule (Figure ). In this method, a Convolutional Neural Network (CNN) takes as input a three dimensional structure of a molecule (optimized by one of the MM force-fields, in Skalic, MMFF) and encodes 3D distributions of selected properties (e.g., aromatic rings, H-bond acceptors and donors, spatial distribution of heavy atoms) into a 512-dimensional vector (so-called hidden representation). This vector is then perturbed with Gaussian noise to produce a set of similar vectors. Next, the Long-Short Term Memory (LSTM) module generates SMILES matching each of these perturbed representations, in effect generating additional yet similar molecules (in Figure , examples shown on the right) having some shape and pharmacophoric similarity with the seed molecule. Importantly, Skalic showed that the similarity between these LSTM-output molecules and the input seed molecule is not as strongly dependent on seed's conformation as for the 512-dimensional hidden representation.