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Recent Advances of Neural Network Models and Applications

Volume 26 of the series Smart Innovation, Systems and Technologies pp 63-74

An Adaptive Reference Point Approach to Efficiently Search Large Chemical Databases

  • Francesco NapolitanoAffiliated withDepartment of Informatics, University of SalernoInstitute for Genomics and Bioinformatics, School of Information and Computer Sciences, University of California-Irvine Email author 
  • , Roberto TagliaferriAffiliated withDepartment of Informatics, University of Salerno
  • , Pierre BaldiAffiliated withInstitute for Genomics and Bioinformatics, School of Information and Computer Sciences, University of California-Irvine

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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.

Keywords

molecular fingerprits chemical database binary vector search