What compound should I make next? Using Matched Molecular Series for prospective medicinal chemistry

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Specific Protein Structural Transformation Average Effect Matched Pair Pair Analysis 
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A Matched Molecular Pair (MMP) is a pair of compounds which differ only by a well-defined structural transformation [1, 2]. Together with large-scale mining of activity or physicochemical data, matched molecular pair analysis (MMPA) has the potential to aid the design of molecules with improved properties by highlighting favourable transformations.

Here we greatly enhance the performance of MMPA for activity prediction by extending to Matched Molecular Series [3, 4]. While matched pair transforms are typically equally likely to increase activity as decrease it, series of length 3 or more exhibit a much greater preference for a particular activity order. One possible reason for this is that longer series correspond to more and more specific protein environments, while matched pair analysis often suffers from being an average effect.

It will be shown that it is possible to predict, with a known degree of accuracy, what R group should increase/decrease the activity of interest, given an observed ordering of activities for a matched series (Figure 1). Predictions are wholly knowledge-based and interpretable.
Figure 1

Observed ordering of activities for a matched series.


  1. 1.
    Wassermann AM, Dimova D, Iyer P, Bajorath J: Advances in Computational Medicinal Chemistry: Matched Molecular Pair Analysis. Drug Dev Res. 2012, 73: 518-527. 10.1002/ddr.21045.CrossRefGoogle Scholar
  2. 2.
    Griffen E, Leach AG, Robb GR, Warner DJ: Matched Molecular Pairs as a Medicinal Chemistry Tool. J Med Chem. 2011, 54: 7739-7750. 10.1021/jm200452d.CrossRefGoogle Scholar
  3. 3.
    Wassermann AM, Bajorath J: A Data Mining Method To Facilitate SAR Transfer. J Chem Inf Model. 2011, 51: 1857-1866. 10.1021/ci200254k.CrossRefGoogle Scholar
  4. 4.
    Mills JEJ, Brown AD, Ryckmans T, Miller DC, Skerratt SE, Barker CM, Bunnage ME: SAR mining and its application to the design of TRPA1 antagonists. Med Chem Commun. 2012, 3: 174-178. 10.1039/c1md00213a.CrossRefGoogle Scholar

Copyright information

© O’Boyle et al; licensee Chemistry Central Ltd. 2014

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.

Authors and Affiliations

  • Noel M O’Boyle
    • 1
  • Roger Sayle
    • 1
  • Jonas Boström
    • 2
  1. 1.NextMove SoftwareCambridgeUK
  2. 2.AstraZenecaMölndalSweden

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