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Semi-supervised Junction Tree Variational Autoencoder for molecular graphs

Resource type
Thesis type
(Thesis) M.Sc.
Date created
2023-01-20
Authors/Contributors
Abstract
Molecular Representation Learning is essential to solving many drug discovery and computational chemistry problems. It is a challenging problem due to the complex structure of molecules and the vast chemical space. Graph representations of molecules are more expressive than traditional representations, such as molecular fingerprints. Therefore, they can improve the performance of machine learning models. We propose SeMole, a method that augments the Junction Tree Variational Autoencoders, a state-of-the-art generative model for molecular graphs, with semi-supervised learning. SeMole aims to improve the accuracy of molecular property prediction when having limited labeled data by exploiting unlabeled data. We enforce that the model generates molecular graphs conditioned on target properties by incorporating the property into the latent representation. We propose an additional pre-training phase to improve the training process for our semi-supervised generative model. We perform an experimental evaluation on the ZINC dataset using three different molecule properties and demonstrate the benefits of semi-supervision. We perform further experiments on the QM9 dataset including twelve molecule properties.
Document
Extent
35 pages.
Identifier
etd22356
Copyright statement
Copyright is held by the author(s).
Permissions
This thesis may be printed or downloaded for non-commercial research and scholarly purposes.
Supervisor or Senior Supervisor
Thesis advisor: Ester, Martin
Language
English
Member of collection
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