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Semi-Supervised Junction Tree Variational Autoencoder for Molecular Property Prediction. (arXiv:2208.05119v2 [cs.LG] UPDATED)
Aug. 17, 2022, 1:11 a.m. | Tongzhou Shen
cs.LG updates on arXiv.org arxiv.org
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 …
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