A De Novo Molecular Generation Method Using Latent Vector Based Generative Adversarial Network

20 June 2019, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


Recently deep learning method has been used for generating novel structures. In the current study, we proposed a new deep learning method, LatentGAN, which combine an autoencoder and a generative adversarial neural network for doing de novo molecule design. We applied the method for structure generation in two scenarios, one is to generate random drug-like compounds and the other is to generate target biased compounds. Our results show that the method works well in both cases, in which sampled compounds from the trained model can largely occupy the same chemical space of the training set and still a substantial fraction of the generated compound are novel. The distribution of drug-likeness score for compounds sampled from LatentGAN is also similar to that of the training set.


Deep Learning
Deep Generative Models
Molecular generation
Wasserstein-GAN variant
Autoencoder Neural Network

Supplementary weblinks


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