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Evaluation of machine-learning methods for ligand-based virtual screening

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Abstract

Machine-learning methods can be used for virtual screening by analysing the structural characteristics of molecules of known (in)activity, and we here discuss the use of kernel discrimination and naive Bayesian classifier (NBC) methods for this purpose. We report a kernel method that allows the processing of molecules represented by binary, integer and real-valued descriptors, and show that it is little different in screening performance from a previously described kernel that had been developed specifically for the analysis of binary fingerprint representations of molecular structure. We then evaluate the performance of an NBC when the training-set contains only a very few active molecules. In such cases, a simpler approach based on group fusion would appear to provide superior screening performance, especially when structurally heterogeneous datasets are to be processed.

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Acknowledgements

We thank the following: the Alexander S. Onassis Public Benefit Foundation, the Engineering and Physical Sciences Research Council and the Novartis Institutes for Biomedical Research for funding George Papadatos; the Biotechnology and Biological Sciences Research Council and GlaxoSmithKline for funding David Wood; MDL Information Systems Inc. for provision of the MDL Drug Data Report database; and the Royal Society, SciTegic Inc., Tripos Inc. and the Wolfson Foundation for hardware, laboratory and software support.

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Authors and Affiliations

  1. Department of Chemistry, University of Sheffield, Western Bank, Sheffield, S3 7HF, UK

    Beining Chen

  2. Department of Automatic Control and Systems Engineering, University of Sheffield, Mappin Street, Sheffield, S1 3JD, UK

    Robert F. Harrison

  3. Krebs Institute for Biomolecular Research and Department of Information Studies, University of Sheffield, 211 Portobello Street, Sheffield, S10 2TN, UK

    George Papadatos & David J. Wood

  4. Krebs Institute for Biomolecular Research and Department of Information Studies, University of Sheffield, 211 Portobello Street, Sheffield, S1 4DP, UK

    Peter Willett

  5. GlaxoSmithKline Research and Development, Stevenage, SG1 2NY, UK

    Xiao Qing Lewell

  6. Novartis Pharma AG, CH-4056, Basel, Switzerland

    Paulette Greenidge & Nikolaus Stiefl

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  1. Beining Chen
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  2. Robert F. Harrison
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  3. George Papadatos
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  4. Peter Willett
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  5. David J. Wood
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  6. Xiao Qing Lewell
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  7. Paulette Greenidge
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  8. Nikolaus Stiefl
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Correspondence to Peter Willett.

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Chen, B., Harrison, R.F., Papadatos, G. et al. Evaluation of machine-learning methods for ligand-based virtual screening. J Comput Aided Mol Des 21, 53–62 (2007). https://doi.org/10.1007/s10822-006-9096-5

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  • DOI: https://doi.org/10.1007/s10822-006-9096-5

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