Fig. 1
From: Designing optimized drug candidates with Generative Adversarial Network
The general workflow. This model is composed of an Encoder–Decoder (A and B) that converts SMILES into latent space vectors that are then used as real data in the training of a WGAN-GP network that comprises a Generator (D) and Critic (E). The feedback-loop, Predictor (F), and selecting Pareto optimal molecules by NSGA-II algorithm (G) are only active during the optimization step