Semi-Supervised Deep Generative Model for Toxicology
Tony S. Shen · SFU Undergraduate Research Symposium Journal · 2021
Determining the properties of chemical silico (using computer) helps tasks such as drug discovery where large number of potential candidate molecules need to be screened for toxicity to human. Existing machine learning methods based on Variational Autoencoder (VAE) are able to predict properties and even generate novel molecules with certain salient properties (such as being safe to human).However, determining the properties of chemicals in wet lab is a time-consuming and expensive endeavor, resulting relatively little labeled training data required for machine learning. Semi-supervised learning is a machine learning approach that leverages the data distribution of both labeled and unlabeled data.In VAE, a hidden vector is a list of numbers that encodes all the information about a molecule; it can be used to reconstruct the molecule. Disentanglement of hidden vector ensures that the information about the of a chemical is encoded at separate compared to the structure of a chemical. With disentanglement in VAE, we can generate the chemical with a new without changing its major structure, by manipulating only the property position in hidden vector.In this project, we will modify the existing state of art approach for conditional generation of molecules (Junction Tree with Molecule Graph), to use the unlabeled portion of our data using semi supervised learning to improve prediction performance. In addition, we'll disentangle the representation of chemical in the hidden vector for conditional molecule generation task, if time permits.