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Cheminformatics analysis and learning in a data pipelining environment

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Summary

Workflow technology is being increasingly applied in discovery information to organize and analyze data. SciTegic's Pipeline Pilot is a chemically intelligent implementation of a workflow technology known as data pipelining. It allows scientists to construct and execute workflows using components that encapsulate many cheminformatics based algorithms. In this paper we review SciTegic's methodology for molecular fingerprints, molecular similarity, molecular clustering, maximal common subgraph search and Bayesian learning. Case studies are described showing the application of these methods to the analysis of discovery data such as chemical series and high throughput screening results. The paper demonstrates that the methods are well suited to a wide variety of tasks such as building and applying predictive models of screening data, identifying molecules for lead optimization and the organization of molecules into families with structural commonality.

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Abbreviations

MCSS:

maximal common substructure search

ECFP:

extended connectivity fingerprints

FCFP:

functional class fingerprints

MDDR:

MDL drug data report

WDI:

world drug index

CATS:

chemically advanced template search

BKD:

binary kernel discrimination

CDK2:

cyclin-dependent kinase 2

DHFR:

escherichia coli dihydrofolate reductase

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Authors and Affiliations

  1. SciTegic, Inc., 10188 Telesis Court, Suite 100, San Diego, CA, 92121, USA

    Moises Hassan, Robert D. Brown & David Rogers

  2. Accelrys, Inc., 10188 Telesis Court, Suite 100, San Diego, CA, 92121, USA

    Shikha Varma-O’Brien

Authors
  1. Moises Hassan
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  2. Robert D. Brown
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  3. Shikha Varma-O’Brien
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  4. David Rogers
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Correspondence to Moises Hassan.

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Hassan, M., Brown, R.D., Varma-O’Brien, S. et al. Cheminformatics analysis and learning in a data pipelining environment. Mol Divers 10, 283–299 (2006). https://doi.org/10.1007/s11030-006-9041-5

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  • DOI: https://doi.org/10.1007/s11030-006-9041-5

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