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An Adaptive Reference Point Approach to Efficiently Search Large Chemical Databases

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Recent Advances of Neural Network Models and Applications

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 26))

Abstract

The ability to rapidly search large repositories of molecules is a crucial task in chemoinformatics. In this work we propose AOR, an approach based on adaptive reference points to improve state of the art performances in querying large repositories of binary fingerprints basing on the Tanimoto distance. We propose a unifying view between the context of reference points and the previously proposed hashing techniques. We also provide a mathematical model to forecast and generalize the results, that is validated by simulating queries over an excerpt of the ChemDB. Clustering techniques are finally introduced to improve the performances. For typical situations the proposed algorithm is shown to resolve queries up to 4 times faster than compared methods.

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Correspondence to Francesco Napolitano .

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Napolitano, F., Tagliaferri, R., Baldi, P. (2014). An Adaptive Reference Point Approach to Efficiently Search Large Chemical Databases. In: Bassis, S., Esposito, A., Morabito, F. (eds) Recent Advances of Neural Network Models and Applications. Smart Innovation, Systems and Technologies, vol 26. Springer, Cham. https://doi.org/10.1007/978-3-319-04129-2_7

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  • DOI: https://doi.org/10.1007/978-3-319-04129-2_7

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-04128-5

  • Online ISBN: 978-3-319-04129-2

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