Autoencoder and Adversarial Autoencoder Models. RNNs that encode SMILES and decode them back to SMILES constitute the most recent generation of methods for ligand-based de novo design. In order to generate new structures, an RNN learns to predict the probability of the next character in a SMILES string, given the previous characters. An autoencoder (AE) constitutes a special architecture that generates a compressed representation of the provided input that could be used to reconstruct the chemical structures. The code layer (i.e., the compressed representation) produced by an autoencoder is a fixed-length vector of descriptors increasingly referred to as 'latent descriptors' that have been used for molecular property prediction. Variations of the original autoencoder architecture have been proposed that translate between different string representations of molecules (e.g., canonical SMILES, InChI, etc.). One such variation focused on the ability of AEs to generate new samples which resulted in variational autoencoders (VAEs). an open-source ML library was used to train and validate the models. The second pipeline consisted of two types of neural networks: feed-forward neural networks based on the four descriptors and RNNs based on SMILES.