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Weighted voting-based consensus clustering for chemical structure databases

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Abstract

The cluster-based compound selection is used in the lead identification process of drug discovery and design. Many clustering methods have been used for chemical databases, but there is no clustering method that can obtain the best results under all circumstances. However, little attention has been focused on the use of combination methods for chemical structure clustering, which is known as consensus clustering. Recently, consensus clustering has been used in many areas including bioinformatics, machine learning and information theory. This process can improve the robustness, stability, consistency and novelty of clustering. For chemical databases, different consensus clustering methods have been used including the co-association matrix-based, graph-based, hypergraph-based and voting-based methods. In this paper, a weighted cumulative voting-based aggregation algorithm (W-CVAA) was developed. The MDL Drug Data Report (MDDR) benchmark chemical dataset was used in the experiments and represented by the AlogP and ECPF_4 descriptors. The results from the clustering methods were evaluated by the ability of the clustering to separate biologically active molecules in each cluster from inactive ones using different criteria, and the effectiveness of the consensus clustering was compared to that of Ward’s method, which is the current standard clustering method in chemoinformatics. This study indicated that weighted voting-based consensus clustering can overcome the limitations of the existing voting-based methods and improve the effectiveness of combining multiple clusterings of chemical structures.

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Acknowledgments

Faisal Saeed is a Researcher of Universiti Teknologi Malaysia under the Post-Doctoral Fellowship Scheme for the project “Consensus Clustering Methods for Chemical Structure Databases” and this work is supported by Research Management Centre (RMC) at Universiti Teknologi Malaysia under Research University Grant Category (VOT Q.J130000.2528.07H89).

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Authors and Affiliations

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

    Faisal Saeed & Naomie Salim

  2. Information Technology Department, Sanhan Community College, Sana’a, Yemen

    Faisal Saeed

  3. Faculty of Engineering, Karary University, Khartoum, 12304, Sudan

    Ali Ahmed

  4. Faculty of Faculty of Bioscience and Bioengineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia

    Mohd Shahir Shamsir

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  1. Faisal Saeed
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  4. Naomie Salim
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Correspondence to Faisal Saeed.

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Saeed, F., Ahmed, A., Shamsir, M.S. et al. Weighted voting-based consensus clustering for chemical structure databases. J Comput Aided Mol Des 28, 675–684 (2014). https://doi.org/10.1007/s10822-014-9750-2

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  • DOI: https://doi.org/10.1007/s10822-014-9750-2

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