QBMG: quasi-biogenic molecule generator with deep recurrent neural network

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Author(s)
Zheng, Shuangjia
Yan, Xin
Gu, Qiong
Yang, Yuedong
Du, Yunfei
Lu, Yutong
Xu, Jun
Griffith University Author(s)
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2019
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Abstract

Biogenic compounds are important materials for drug discovery and chemical biology. In this work, we report a quasi-biogenic molecule generator (QBMG) to compose virtual quasi-biogenic compound libraries by means of gated recurrent unit recurrent neural networks. The library includes stereo-chemical properties, which are crucial features of natural products. QMBG can reproduce the property distribution of the underlying training set, while being able to generate realistic, novel molecules outside of the training set. Furthermore, these compounds are associated with known bioactivities. A focused compound library based on a given chemotype/scaffold can also be generated by this approach combining transfer learning technology. This approach can be used to generate virtual compound libraries for pharmaceutical lead identification and optimization.

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Journal of Cheminformatics

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11

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1

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© The Author(s) 2019. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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Macromolecular and materials chemistry

Science & Technology

Physical Sciences

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Chemistry, Multidisciplinary

Computer Science, Information Systems

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Zheng, S; Yan, X; Gu, Q; Yang, Y; Du, Y; Lu, Y; Xu, J, QBMG: quasi-biogenic molecule generator with deep recurrent neural network, Journal of Cheminformatics, 2019, 11 (1)

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