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Prediction of New Bioactive Molecules of Chemical Compound Using Boosting Ensemble Methods

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Soft Computing in Data Science (SCDS 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 788))

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Abstract

Virtual screening (VS) methods can be categorized into structure-based virtual screening (SBVS) that involves knowledge about the target’s 3D structure and ligand-based virtual screening (LBVS) approaches that utilize information from at least one identified ligand. However, the activity prediction of new bioactive molecules in highly diverse data set is still less accurate and the result is not comprehensive enough since only one approach is applied at one time. This paper aims to recommend the boosting ensemble method, MultiBoost, into LBVS using the well-known chemoinformatics database, the MDL Drug Data Report (MDDR). The experimental results were compared with Support Vector Machines (SVM). The final outcomes showed that MultiBoost ensemble classifiers had improved the effectiveness of the prediction of new bioactive molecules in high diverse data.

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Acknowledgments

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

Author information

Authors and Affiliations

  1. Kolej Yayasan Pelajaran Johor, KM16, Jalan Kulai-Kota Tinggi, 81900, Kota Tinggi, Johor, Malaysia

    Haslinda Hashim

  2. Information Systems Department, Faculty of Computing, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia

    Haslinda Hashim & Faisal Saeed

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

    Faisal Saeed

Authors
  1. Haslinda Hashim
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  2. Faisal Saeed
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Corresponding author

Correspondence to Faisal Saeed .

Editor information

Editors and Affiliations

  1. Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia

    Azlinah Mohamed

  2. University of Tennessee at Knoxville, Knoxville, Tennessee, USA

    Michael W. Berry

  3. Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia

    Bee Wah Yap

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Hashim, H., Saeed, F. (2017). Prediction of New Bioactive Molecules of Chemical Compound Using Boosting Ensemble Methods. In: Mohamed, A., Berry, M., Yap, B. (eds) Soft Computing in Data Science. SCDS 2017. Communications in Computer and Information Science, vol 788. Springer, Singapore. https://doi.org/10.1007/978-981-10-7242-0_22

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  • DOI: https://doi.org/10.1007/978-981-10-7242-0_22

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-7241-3

  • Online ISBN: 978-981-10-7242-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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