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Deep Belief Network for Molecular Feature Selection in Ligand-Based Virtual Screening

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Book cover Recent Trends in Data Science and Soft Computing (IRICT 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 843))

Abstract

It became obvious that it is required to reduce the high-dimensional of data in many data mining researches and applications. Virtual Screening (VS) is a set of computational methods that aim to score, rank and/or filter a set of chemical structures using one or more computational procedures to ensure those molecules with the largest prior probabilities of activity. 2D fingerprint descriptors are used to represent molecule features, most of these features are important and has ability to improve the molecules similarity and the others are not important and taking more computational time without any effect on the similarity score. Deep belief networks is one of the deep learning methods used to select the important features to reduce the high dimensionality by using stack of Restricted Boltzmann Machine and fine tune to enhance weights and reduce the reconstruct feature error. Thus, the features that have more reconstruct error are removed and only features with less constrict error will be used. The experimental results showed the enhancements on VS results using the proposed methods.

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Acknowledgments

This work is supported by the Ministry of Higher Education (MOHE) and the Research Management Centre (RMC) at the Universiti Teknologi Malaysia (UTM) under the Research University Grant Category (VOT Q.J130000.2528.16H74 and R.J130000.7828.4F985).

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Authors and Affiliations

  1. Faculty of Computing, Universiti Teknologi Malaysia, 81310, Johor Bahru, Johor, Malaysia

    Maged Nasser, Naomie Salim & Hentabli Hamza

  2. College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia

    Faisal Saeed

Authors
  1. Maged Nasser
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  2. Naomie Salim
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  3. Hentabli Hamza
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  4. Faisal Saeed
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Corresponding author

Correspondence to Maged Nasser .

Editor information

Editors and Affiliations

  1. College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia

    Faisal Saeed

  2. Information Systems Department, Faculty of Computing, Universiti Teknologi Malaysia, Skudai, Malaysia

    Nadhmi Gazem

  3. Information Technology Department, Faculty of Engineering and Information Technology, Taiz University, Taiz, Yemen

    Fathey Mohammed

  4. Information Systems Department, Faculty of Computing, Universiti Teknologi Malaysia, Skudai, Malaysia

    Abdelsalam Busalim

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Nasser, M., Salim, N., Hamza, H., Saeed, F. (2019). Deep Belief Network for Molecular Feature Selection in Ligand-Based Virtual Screening. In: Saeed, F., Gazem, N., Mohammed, F., Busalim, A. (eds) Recent Trends in Data Science and Soft Computing. IRICT 2018. Advances in Intelligent Systems and Computing, vol 843. Springer, Cham. https://doi.org/10.1007/978-3-319-99007-1_1

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