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Molecular Diversity

, Volume 10, Issue 3, pp 283–299 | Cite as

Cheminformatics analysis and learning in a data pipelining environment

  • Moises HassanEmail author
  • Robert D. Brown
  • Shikha Varma-O’Brien
  • David Rogers
Full–length paper

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.

Key words

Bayesian models bioactivity prediction data mining data pipelining maximal common substructure search molecular fingerprints molecular similarity virtual screening 

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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Copyright information

© SpringerScience + Business Media, Inc. 2006

Authors and Affiliations

  • Moises Hassan
    • 1
    Email author
  • Robert D. Brown
    • 1
  • Shikha Varma-O’Brien
    • 2
  • David Rogers
    • 1
  1. 1.SciTegic, Inc.San DiegoUSA
  2. 2.Accelrys, Inc.San DiegoUSA

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