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De Novo Molecular Design of Caspase-6 Inhibitors by a GRU-Based Recurrent Neural Network Combined with a Transfer Learning Approach

Due to their potential in the treatment of neurodegenerative diseases, caspase-6 inhibitors have attracted widespread attention. However, the existing caspase-6 inhibitors showed more or less inevitable deficiencies that restrict their clinical development and applications. Therefore, there is an ur...

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Autores principales: Huang, Shuheng, Mei, Hu, Lu, Laichun, Qiu, Minyao, Liang, Xiaoqi, Xu, Lei, Kuang, Zuyin, Heng, Yu, Pan, Xianchao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8706867/
https://www.ncbi.nlm.nih.gov/pubmed/34959651
http://dx.doi.org/10.3390/ph14121249
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author Huang, Shuheng
Mei, Hu
Lu, Laichun
Qiu, Minyao
Liang, Xiaoqi
Xu, Lei
Kuang, Zuyin
Heng, Yu
Pan, Xianchao
author_facet Huang, Shuheng
Mei, Hu
Lu, Laichun
Qiu, Minyao
Liang, Xiaoqi
Xu, Lei
Kuang, Zuyin
Heng, Yu
Pan, Xianchao
author_sort Huang, Shuheng
collection PubMed
description Due to their potential in the treatment of neurodegenerative diseases, caspase-6 inhibitors have attracted widespread attention. However, the existing caspase-6 inhibitors showed more or less inevitable deficiencies that restrict their clinical development and applications. Therefore, there is an urgent need to develop novel caspase-6 candidate inhibitors. Herein, a gated recurrent unit (GRU)-based recurrent neural network (RNN) combined with transfer learning was used to build a molecular generative model of caspase-6 inhibitors. The results showed that the GRU-based RNN model can accurately learn the SMILES grammars of about 2.4 million chemical molecules including ionic and isomeric compounds and can generate potential caspase-6 inhibitors after transfer learning of the known 433 caspase-6 inhibitors. Based on the novel molecules derived from the molecular generative model, an optimal logistic regression model and Surflex-dock were employed for predicting and ranking the inhibitory activities. According to the prediction results, three potential caspase-6 inhibitors with different scaffolds were selected as the promising candidates for further research. In general, this paper provides an efficient combinational strategy for de novo molecular design of caspase-6 inhibitors.
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spelling pubmed-87068672021-12-25 De Novo Molecular Design of Caspase-6 Inhibitors by a GRU-Based Recurrent Neural Network Combined with a Transfer Learning Approach Huang, Shuheng Mei, Hu Lu, Laichun Qiu, Minyao Liang, Xiaoqi Xu, Lei Kuang, Zuyin Heng, Yu Pan, Xianchao Pharmaceuticals (Basel) Article Due to their potential in the treatment of neurodegenerative diseases, caspase-6 inhibitors have attracted widespread attention. However, the existing caspase-6 inhibitors showed more or less inevitable deficiencies that restrict their clinical development and applications. Therefore, there is an urgent need to develop novel caspase-6 candidate inhibitors. Herein, a gated recurrent unit (GRU)-based recurrent neural network (RNN) combined with transfer learning was used to build a molecular generative model of caspase-6 inhibitors. The results showed that the GRU-based RNN model can accurately learn the SMILES grammars of about 2.4 million chemical molecules including ionic and isomeric compounds and can generate potential caspase-6 inhibitors after transfer learning of the known 433 caspase-6 inhibitors. Based on the novel molecules derived from the molecular generative model, an optimal logistic regression model and Surflex-dock were employed for predicting and ranking the inhibitory activities. According to the prediction results, three potential caspase-6 inhibitors with different scaffolds were selected as the promising candidates for further research. In general, this paper provides an efficient combinational strategy for de novo molecular design of caspase-6 inhibitors. MDPI 2021-11-30 /pmc/articles/PMC8706867/ /pubmed/34959651 http://dx.doi.org/10.3390/ph14121249 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Huang, Shuheng
Mei, Hu
Lu, Laichun
Qiu, Minyao
Liang, Xiaoqi
Xu, Lei
Kuang, Zuyin
Heng, Yu
Pan, Xianchao
De Novo Molecular Design of Caspase-6 Inhibitors by a GRU-Based Recurrent Neural Network Combined with a Transfer Learning Approach
title De Novo Molecular Design of Caspase-6 Inhibitors by a GRU-Based Recurrent Neural Network Combined with a Transfer Learning Approach
title_full De Novo Molecular Design of Caspase-6 Inhibitors by a GRU-Based Recurrent Neural Network Combined with a Transfer Learning Approach
title_fullStr De Novo Molecular Design of Caspase-6 Inhibitors by a GRU-Based Recurrent Neural Network Combined with a Transfer Learning Approach
title_full_unstemmed De Novo Molecular Design of Caspase-6 Inhibitors by a GRU-Based Recurrent Neural Network Combined with a Transfer Learning Approach
title_short De Novo Molecular Design of Caspase-6 Inhibitors by a GRU-Based Recurrent Neural Network Combined with a Transfer Learning Approach
title_sort de novo molecular design of caspase-6 inhibitors by a gru-based recurrent neural network combined with a transfer learning approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8706867/
https://www.ncbi.nlm.nih.gov/pubmed/34959651
http://dx.doi.org/10.3390/ph14121249
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