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Data Mining and Computational Modeling of High-Throughput Screening Datasets

  • Sean EkinsEmail author
  • Alex M. Clark
  • Krishna Dole
  • Kellan Gregory
  • Andrew M. Mcnutt
  • Anna Coulon Spektor
  • Charlie Weatherall
  • Nadia K. Litterman
  • Barry A. Bunin
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1755)

Abstract

We are now seeing the benefit of investments made over the last decade in high-throughput screening (HTS) that is resulting in large structure activity datasets entering public and open databases such as ChEMBL and PubChem. The growth of academic HTS screening centers and the increasing move to academia for early stage drug discovery suggests a great need for the informatics tools and methods to mine such data and learn from it. Collaborative Drug Discovery, Inc. (CDD) has developed a number of tools for storing, mining, securely and selectively sharing, as well as learning from such HTS data. We present a new web based data mining and visualization module directly within the CDD Vault platform for high-throughput drug discovery data that makes use of a novel technology stack following modern reactive design principles. We also describe CDD Models within the CDD Vault platform that enables researchers to share models, share predictions from models, and create models from distributed, heterogeneous data. Our system is built on top of the Collaborative Drug Discovery Vault Activity and Registration data repository ecosystem which allows users to manipulate and visualize thousands of molecules in real time. This can be performed in any browser on any platform. In this chapter we present examples of its use with public datasets in CDD Vault. Such approaches can complement other cheminformatics tools, whether open source or commercial, in providing approaches for data mining and modeling of HTS data.

Key words

ADME Bayesian models CDD models CDD vault Visualization Collaborative database Data mining 

Notes

Acknowledgments

We acknowledge that the Bayesian model software within CDD was developed with support from Award Number 9R44TR000942-02 “Biocomputation across distributed private datasets to enhance drug discovery” from the NIH NCATS. The CDD TB has been developed thanks to funding from the Bill and Melinda Gates Foundation (Grant#49852 “Collaborative drug discovery for TB through a novel database of SAR data optimized to promote data archiving and sharing”). The work was partially supported by a grant from the European Community’s Seventh Framework Program (grant 260872, MM4TB Consortium) to S.E. S.E. gratefully acknowledges Biovia (formerly Accelrys) for providing Discovery Studio and Dr. Alexander Perryman and Dr. Joel Freundlich for their feedback and collaboration on CDD models. We sincerely acknowledge our many colleagues, collaborators, and advocates who have contributed to the development of CDD over the years.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Sean Ekins
    • 2
    Email author
  • Alex M. Clark
    • 1
    • 3
  • Krishna Dole
    • 1
  • Kellan Gregory
    • 1
  • Andrew M. Mcnutt
    • 1
  • Anna Coulon Spektor
    • 1
  • Charlie Weatherall
    • 1
  • Nadia K. Litterman
    • 1
  • Barry A. Bunin
    • 1
  1. 1.Collaborative Drug Discovery, Inc.BurlingameUSA
  2. 2.Collaborations Pharmaceuticals, Inc.RaleighUSA
  3. 3.Molecular Materials Informatics, Inc.MontrealCanada

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