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BACCHUS

BACCHUS. B reeding A ctivity- C lustered C hemical H yperstruct U re S. Nathan Brown. Department of Information Studies University of Sheffield. Overview. Chemical hyperstructures Including activity information Validation experiments Colouring molecules by weights

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BACCHUS

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  1. BACCHUS Breeding Activity-Clustered Chemical HyperstructUreS Nathan Brown Department of Information Studies University of Sheffield

  2. Overview • Chemical hyperstructures • Including activity information • Validation experiments • Colouring molecules by weights • Possible applications • Conclusions & further work

  3. Introduction • Traditional QSAR approaches limited to: • Small datasets • Similar structural classes • Quantitative data • High-throughput screening tends to produce: • Large datasets • Diverse structural classes • Qualitative data

  4. Chemical Hyperstructures • Pseudo-molecule representing a library • Sequential mapping of molecules • Minimise structural redundancy • Maximum overlap set • Combinatorial explosion • Genetic algorithms

  5. Hyperstructure Generation Input molecule: Hyperstructure:

  6. Introducing BACCHUS Breeding Activity-Clustered Chemical HyperstructUreS • Correlation between structure & activity • Assign activity and inactivity weights to nodes and edges of hyperstructure • Weights reinforced through generation • Activity = acts / (acts + inacts)

  7. Inactive Active Active Inactive Including Activity Input molecule: Hyperstructure:

  8. Summarising BACCHUS

  9. Methodology & validation • 2,000 hyperstructures generated with: • 50 actives and 50 inactives from NCI AIDS • 1,000 from known activities • 1,000 from randomised activities • Summarise hyperstructures from both sets • Distributions and t-scores comparing: • Known activities against randomised activities

  10. Node Activities t = 54.57

  11. Node Inactivities t = 13.80

  12. Further Validation [4 activity classes from the IDAlert database]

  13. Active Molecules activeinactive

  14. Inactive Molecules activeinactive

  15. Possible Applications • Identification of common active features within the hyperstructure • cf. MCS-based pharmacophore detection methods • Use active subgraphs for similarity searching • cf. modal fingerprints • Score unknown molecules against an AWCH • cf. fragment-based substructural analysis

  16. Conclusions & Further Work • BACCHUS presented as approach for: • Analysing structural & bioactivity data • Visualisation of induced molecules • Current & future work • Substructural analysis • Parallel implementation

  17. Acknowledgements • Peter Willett and David J. Wilton • University of Sheffield • Richard A. Lewis • Eli Lilly and Company • CASE Award with EPSRC and Eli Lilly

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