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Deep Molecular Representation in Cheminformatics

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Handbook of Deep Learning Applications

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 136))

Abstract

Quantum-chemical descriptors are powerful predictors of discovering and designing new materials of desired properties. Wave-function-based methods are often employed to calculate quantum-chemical descriptors, which are time consuming. Recently, machine learning models have been used for predicting quantum-chemical descriptors because of their computational advantages. However, it is difficult to generate a proper molecular representation for training. This work reviews recent molecular representation techniques and then employs variational autoencoders to encode Bag-of-Bond molecular representation. The encoded representation reduce the dimensionality of features and extract the essential information through a deep neural network structure. Results on a benchmark dataset show that the deep encoded molecular representation outperforms Bag-of-Bond representations in predicting electronic quantum-chemical descriptors.

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Author information

Authors and Affiliations

  1. School of Materials Science and Engineering, UNSW Australia, Sydney, NSW, Australia

    Peng Jiang & Alan Crosky

  2. School of Mining Engineering, UNSW Australia, Sydney, NSW, Australia

    Serkan Saydam & Hamed Lamei Ramandi

  3. School of Civil and Environmental Engineering, UNSW Australia, Sydney, NSW, Australia

    Mojtaba Maghrebi

  4. Department of Civil Engineering, Ferdowsi University of Mashhad, Mashhad, Razavi Khorasan, Iran

    Mojtaba Maghrebi

Authors
  1. Peng Jiang
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  2. Serkan Saydam
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  3. Hamed Lamei Ramandi
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  4. Alan Crosky
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  5. Mojtaba Maghrebi
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Corresponding author

Correspondence to Mojtaba Maghrebi .

Editor information

Editors and Affiliations

  1. Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  2. School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

    Sanjiban Sekhar Roy

  3. University of Canberra, Bruce, ACT, Australia

    Dharmendra Sharma

  4. Department of Civil Engineering, National Institute of Technology Patna, Patna, Bihar, India

    Pijush Samui

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Jiang, P., Saydam, S., Ramandi, H.L., Crosky, A., Maghrebi, M. (2019). Deep Molecular Representation in Cheminformatics. In: Balas, V., Roy, S., Sharma, D., Samui, P. (eds) Handbook of Deep Learning Applications. Smart Innovation, Systems and Technologies, vol 136. Springer, Cham. https://doi.org/10.1007/978-3-030-11479-4_8

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