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The Maximum Common Substructure (MCS) Search as a New Tool for SAR and QSAR

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Advances in QSAR Modeling

Abstract

The Maximum Common Substructure (MCS) between two molecules induces a similarity that makes it possible to group compounds sharing the same pattern. In our study the relevance of a similarity measure exclusively based on MCS has been implemented in new software based on the fmcs_R package. The newly developed program searches for the largest substructures between a target molecule, with unknown property value, and a set of similar molecules with experimental value to assess the toxicity of the target chemical. In QSAR and read-across , while reasoning on the similarity of the evaluated molecules, another important aspect to consider is the difference of two molecules that share a large common part. Thus, the present study examines the issue of the MCS itself, and the differences between a reference and a similar molecule by the aid of an ad hoc developed software. The most important features of this software are: (I) the process of the MCSs between two molecules represented as graphs and (II) the detection and the graphical representation of the dissimilar substructures that are identified in the target and the source molecules. The user may consequently quantify the properties and weights of these substructures to improve the assessment of new substances. This new software is integrated into ToxRead, a system to visualize structures and substructures for expert reasoning. Moreover, an automatic search in a database containing the role of small substructures in amplifying or reducing the property can help in improving the final assessment.

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Notes

  1. 1.

    http://www.oecd.org/chemicalsafety/risk-assessment/groupingofchemicalschemicalcategoriesandread-across.htm.

  2. 2.

    http://www.toxtree.sourceforge.net/.

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Acknowledgements

This research was supported by the PROSIL project (LIFE12 ENV/IT/000154). We thank Serena Manganelli and Giuseppa Raitano from the IRCCS—Istituto di Ricerca Farmacologiche Mario Negri, who provided insight and expertise that greatly assisted the research.

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Correspondence to Azadi Golbamaki .

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Golbamaki, A., Franchi, A.M., Gini, G. (2017). The Maximum Common Substructure (MCS) Search as a New Tool for SAR and QSAR. In: Roy, K. (eds) Advances in QSAR Modeling. Challenges and Advances in Computational Chemistry and Physics, vol 24. Springer, Cham. https://doi.org/10.1007/978-3-319-56850-8_5

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