Deep Molecular Dreaming: Inverse machine learning for de novo molecular design with surjective representations
The de novo design of new functional chemical compounds can bring enormous scientific and technological advances. For this reason, researchers in cheminformatics have developed a plethora of A.I. methodologies for the challenging inverse molecular design task [1] They include deep learning techniques such as variational autoencoders (VAE) [2, 3, 4] generative adversarial networks (GAN) [5, 6] reinforcement learning (RL) [7, 8] and evolutionary techniques such as genetic algorithms (GA) [9, 10, 11]