Semi-Supervised Junction Tree Variational Autoencoder for Molecular
Property Prediction
- URL: http://arxiv.org/abs/2208.05119v1
- Date: Wed, 10 Aug 2022 03:06:58 GMT
- Title: Semi-Supervised Junction Tree Variational Autoencoder for Molecular
Property Prediction
- Authors: Tongzhou Shen
- Abstract summary: This research modifies state-of-the-art molecule generation method - Junction Tree Variational Autoencoder (JT-VAE) to facilitate semi-supervised learning on chemical property prediction.
We leverage JT-VAE architecture to learn an interpretable representation optimal for tasks ranging from molecule property prediction to conditional molecule generation.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent advances in machine learning have enabled accurate prediction of
chemical properties. However, supervised machine learning methods in this
domain often suffer from the label scarcity problem, due to the expensive
nature of labeling chemical property experimentally. This research modifies
state-of-the-art molecule generation method - Junction Tree Variational
Autoencoder (JT-VAE) to facilitate semi-supervised learning on chemical
property prediction. Furthermore, we force some latent variables to take on
consistent and interpretable purposes such as representing toxicity via this
partial supervision. We leverage JT-VAE architecture to learn an interpretable
representation optimal for tasks ranging from molecule property prediction to
conditional molecule generation, using a partially labelled dataset.
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