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Methods to Improve Ranking Chemical Structures in Ligand-Based Virtual Screening

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Emerging Trends in Intelligent Computing and Informatics (IRICT 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1073))

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Abstract

One of the main tasks in chemoinformatics is searching for active chemical compounds in screening databases. The chemical databases can contain thousands or millions of chemical structures for screening. Therefore, there is an increasing need for computational methods that can help alleviate some challenges for saving time and cost in drug discover design. The ranking of chemical compounds can be accomplished according to their chances of clinical success by the computational tools. In this paper, the techniques that have been used to improve the ranking of chemical structures in similarity searching methods have been highlighted through two categories. Firstly, the taxonomy of using machine learning techniques in ranking chemical structures have been introduced. Secondly, we have discussed the alternative chemical ranking approaches that can be used instead of classical ranking criteria to enhance the performance of similarity searching methods.

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Author information

Authors and Affiliations

  1. Tishk International University, Erbil, Iraq

    Mohammed Mumtaz Al-Dabbagh

  2. Universiti Teknologi Malaysia, Skudia, Malaysia

    Naomie Salim

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

    Faisal Saeed

Authors
  1. Mohammed Mumtaz Al-Dabbagh
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  2. Naomie Salim
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  3. Faisal Saeed
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Corresponding author

Correspondence to Mohammed Mumtaz Al-Dabbagh .

Editor information

Editors and Affiliations

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

    Faisal Saeed

  2. School of Computing, Universiti Utara Malaysia (UUM), Sintok, Kedah Darul Aman, Malaysia

    Fathey Mohammed

  3. Management of Information Systems Department College of Business Administration, Taibah University, Yanbu, Saudi Arabia

    Nadhmi Gazem

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Al-Dabbagh, M.M., Salim, N., Saeed, F. (2020). Methods to Improve Ranking Chemical Structures in Ligand-Based Virtual Screening. In: Saeed, F., Mohammed, F., Gazem, N. (eds) Emerging Trends in Intelligent Computing and Informatics. IRICT 2019. Advances in Intelligent Systems and Computing, vol 1073. Springer, Cham. https://doi.org/10.1007/978-3-030-33582-3_25

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