Deep learning-based drug discovery and molecule generation
The project aims to facilitate the research and development of new drugs by exploring deep learning methods to process molecules and to generate new molecules. The deep learning models that will be experimented include few shot learning, generative adversarial network, and variational autoencoder. We would like to improve these methods specifically for pharmacological datasets, which are vastly different from many common, public dataset used in academic research on the aforementioned models. We would like to leverage heterogenous datasets, and learn molecule semantics representation with GAN, VAE, or other representational learning neural networks to facilitate efficient molecule generation.