A de novo molecular generation method using latent vector based generative adversarial network

TLDR

Deep learning has been used to generate novel drug‑like structures. The study proposes LatentGAN, an autoencoder–GAN architecture for de novo molecular design. LatentGAN was trained to generate random drug‑like and target‑biased molecules. LatentGAN successfully produced compounds occupying the same chemical space as the training set, yielded novel structures with comparable drug‑likeness scores, and differed from RNN‑based models, demonstrating complementary performance.

Abstract

Deep learning methods applied to drug discovery have been used to generate novel structures. In this study, we propose a new deep learning architecture, LatentGAN, which combines an autoencoder and a generative adversarial neural network for de novo molecular design. We applied the method in two scenarios: one to generate random drug-like compounds and another to generate target-biased compounds. Our results show that the method works well in both cases. Sampled compounds from the trained model can largely occupy the same chemical space as the training set and also generate a substantial fraction of novel compounds. Moreover, the drug-likeness score of compounds sampled from LatentGAN is also similar to that of the training set. Lastly, generated compounds differ from those obtained with a Recurrent Neural Network-based generative model approach, indicating that both methods can be used complementarily.