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Generative model based on junction tree variational autoencoder for HOMO value prediction and molecular optimization

In this work, we provide further development of the junction tree variational autoencoder (JT VAE) architecture in terms of implementation and application of the internal feature space of the model. Pretraining of JT VAE on a large dataset and further optimization with a regression model led to a la...

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Autores principales: Kondratyev, Vladimir, Dryzhakov, Marian, Gimadiev, Timur, Slutskiy, Dmitriy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9893566/
https://www.ncbi.nlm.nih.gov/pubmed/36732800
http://dx.doi.org/10.1186/s13321-023-00681-4
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author Kondratyev, Vladimir
Dryzhakov, Marian
Gimadiev, Timur
Slutskiy, Dmitriy
author_facet Kondratyev, Vladimir
Dryzhakov, Marian
Gimadiev, Timur
Slutskiy, Dmitriy
author_sort Kondratyev, Vladimir
collection PubMed
description In this work, we provide further development of the junction tree variational autoencoder (JT VAE) architecture in terms of implementation and application of the internal feature space of the model. Pretraining of JT VAE on a large dataset and further optimization with a regression model led to a latent space that can solve several tasks simultaneously: prediction, generation, and optimization. We use the ZINC database as a source of molecules for the JT VAE pretraining and the QM9 dataset with its HOMO values to show the application case. We evaluate our model on multiple tasks such as property (value) prediction, generation of new molecules with predefined properties, and structure modification toward the property. Across these tasks, our model shows improvements in generation and optimization tasks while preserving the precision of state-of-the-art models.
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spelling pubmed-98935662023-02-03 Generative model based on junction tree variational autoencoder for HOMO value prediction and molecular optimization Kondratyev, Vladimir Dryzhakov, Marian Gimadiev, Timur Slutskiy, Dmitriy J Cheminform Research In this work, we provide further development of the junction tree variational autoencoder (JT VAE) architecture in terms of implementation and application of the internal feature space of the model. Pretraining of JT VAE on a large dataset and further optimization with a regression model led to a latent space that can solve several tasks simultaneously: prediction, generation, and optimization. We use the ZINC database as a source of molecules for the JT VAE pretraining and the QM9 dataset with its HOMO values to show the application case. We evaluate our model on multiple tasks such as property (value) prediction, generation of new molecules with predefined properties, and structure modification toward the property. Across these tasks, our model shows improvements in generation and optimization tasks while preserving the precision of state-of-the-art models. Springer International Publishing 2023-02-02 /pmc/articles/PMC9893566/ /pubmed/36732800 http://dx.doi.org/10.1186/s13321-023-00681-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Kondratyev, Vladimir
Dryzhakov, Marian
Gimadiev, Timur
Slutskiy, Dmitriy
Generative model based on junction tree variational autoencoder for HOMO value prediction and molecular optimization
title Generative model based on junction tree variational autoencoder for HOMO value prediction and molecular optimization
title_full Generative model based on junction tree variational autoencoder for HOMO value prediction and molecular optimization
title_fullStr Generative model based on junction tree variational autoencoder for HOMO value prediction and molecular optimization
title_full_unstemmed Generative model based on junction tree variational autoencoder for HOMO value prediction and molecular optimization
title_short Generative model based on junction tree variational autoencoder for HOMO value prediction and molecular optimization
title_sort generative model based on junction tree variational autoencoder for homo value prediction and molecular optimization
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9893566/
https://www.ncbi.nlm.nih.gov/pubmed/36732800
http://dx.doi.org/10.1186/s13321-023-00681-4
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