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Dynamic SA/Reports - ACS Philadelphia 2012

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Dynamic SA/Reports - ACS Philadelphia 2012

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Analyzing Current Project and High-Throughput Screening Data by Interactive Selection of Frequently-Occurring Scaffolds. Methods described: how to tweak the MOE SA/Report tool to interactively discover scaffolds in large and diverse HTS-like chemical datasets (code on SVL exchange), and how to automate creation of SA/Reports from project data using KNIME.

Analyzing Current Project and High-Throughput Screening Data by Interactive Selection of Frequently-Occurring Scaffolds. Methods described: how to tweak the MOE SA/Report tool to interactively discover scaffolds in large and diverse HTS-like chemical datasets (code on SVL exchange), and how to automate creation of SA/Reports from project data using KNIME.

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Dynamic SA/Reports - ACS Philadelphia 2012

  1. 1. Dynamic SA/Reports: Analyzing Current Project and HTS Data by Interactive Selection of Frequently-occurring Scaffolds Deepak Bandyopadhyay Development help: Chris Louer, Ceara Rea, Jerome Verlin, Alain Deschenes, Nels Thorsteinson, Guido Kirsten, Bernd Wiswedel Project testing: Ami Lakdawala, Chaya Duraiswami, Guanglei Cui, Kaushik Raha, Kristin Brown, Neysa Nevins, Xuan Hong, Constantine Kreatsoulas Star cast:
  2. 2. Find viable chemical series from project HTS data or other large/diverse datasets –Ideally, from single-shot data:  –Pragmatically, full-curve data: ∫∫∫∫∫∫∫∫∫∫ …↗ ∫∫∫∫∫∫∫∫∫∫∫∫ Usually: scaffold-agnostic (clustering) analysis –But clusters do not map 1:1 to chemotypes Our goal: R-group analysis of HTS data –Provide SAR in a more user-friendly format Tool of choice: MOE SA/Report Problem statement
  3. 3. Outline SA/Report Background –Problem with out-of-box analysis of HTS data  Frequent fragment scaffold selection – Automated and interactive solutions  Customizations for project data delivery – Custom units to visualize arbitrary data types – KNIME workflows for automated generation Case studies (project and public datasets) Conclusion
  4. 4. What is a Structure-Activity Report? SAR analysis and visualization tool in MOE (chemcomp.com) Input: MOE database (created from CSV, SD-file, etc.) – Structure and multiple activity/property columns – Pick/guess column data types (pIC50, IC50, percent,…) Scaffolds: Auto-detect or specify; R-groups optional Output: tabbed web page – Summary tab: arranges molecules by scaffolds and R-groups, showing details on mouse-over or clicking on R-groups Clark AM, Labute P. J Med Chem. 2009 52(2):469-83. Agrafiotis DK et al., J Med Chem. 2007 50(24):5926-37 Below: SA/Report on PubChem pyruvate kinase screen, Assay ID 361
  5. 5. What is a Structure-Activity Report? SAR analysis and visualization tool in MOE (chemcomp.com) Input: MOE database (created from CSV, SD-file, etc.) – Structure and multiple activity/property columns – Pick/guess column data types (pIC50, IC50, percent,…) Scaffolds: Auto-detect or specify Output: tabbed web page – Summary tab: arranges molecules by scaffolds and R-groups, showing details on mouse-over or clicking on R-groups – Activity tab: grid, R1 vs. R2 or scaffold vs. R1. – Multiple activities visualized simultaneously as color bars or concentric pie charts (“cartwheels”) Clark AM, Labute P. J Med Chem. 2009 52(2):469-83. Agrafiotis DK et al., J Med Chem. 2007 50(24):5926-37 Below: SA/Report on PubChem pyruvate kinase screen, Assay ID 361
  6. 6. SA/Report: auto-detect on HTS data Auto-detect does not find all frequently-occurring series in diverse datasets (eg. HTS hits, >4000 compds, >10 series) –Eg. PubChem AssayID 361, 4265 Pyruvate Kinase inhibitor hits – Two scaffolds found; known series with more exemplars missed What to do?: –Specify manually OR –Use automated or interactive method to find scaffolds Clark AM, Labute P. J Med Chem. 2009 52(2):469-83.
  7. 7. Outline SA/Report Background –Problem with out-of-box analysis of HTS data  Frequent fragment scaffold selection – Automated and interactive solutions  Customizations for project data delivery – Custom units to visualize arbitrary data types – KNIME workflows for automated generation Case studies (project and public datasets) Conclusion
  8. 8. Scaffolds from Fragment Decomposition Use frequent fragments as scaffolds –Schuffenhauer hierarchical decomposition  –Compounds sorted by frequency of fragment at each level. A. Schuffenhauer et al., J. Chem. Inf. Modeling 47:47-58, 2007
  9. 9. Interactive scaffold picking Users prefer scaffold suggestions, not full automation – Exclude known nuisance or cross-target-active fragments – Exclude scaffolds that don’t make chemical sense – Prefer one among overlapping or multiple scaffolds in a molecule – Want to analyze a subset of the scaffolds found Interactive “common fragment selection” GUI –“Analyze…” button next to “Browse…” on patched version of SA/Report cmnfrag.svl (A. Clark/A. Deschenes, CCG; *available* on SVL exchange)
  10. 10. Interactive scaffold picking, step 1 Top 12 best frequent fragments presented to the user to choose from –Rank= frequency heavy atom count (1+ (similarity to existing scaffolds)) –↓ User picks #2: PubChem dataset: AID 893, HSD17B4, hydroxysteroid (17-beta) dehydrogenase 4
  11. 11. Frequent scaffold picking, iterative step 1. Add picked fragment to scaffold list 2. Remove molecules that map to it from consideration 3. Re-analyze remaining molecules for frequent scaffolds 4. Repeat until satisfied
  12. 12. Frequent scaffold picking, final iteration 1. Add picked fragment to scaffold list 2. Remove molecules that map to it from consideration 3. Re-analyze remaining molecules for frequent scaffolds 4. Repeat until satisfied
  13. 13. Run SA/Report with scaffolds picked from frequent fragment hierarchy, automatically or interactively HTS SAR analysis
  14. 14. Outline SA/Report Background –Problem with out-of-box analysis of HTS data  Frequent fragment scaffold selection – Automated and interactive solutions  Customizations for project data delivery – Custom units to visualize arbitrary data types – KNIME workflows for automated generation Case studies (project and public datasets) Conclusion
  15. 15. Customization 1: units for visualization SA/Report built to visualize activity (pIC50/pKi, IC50/Ki, percent, fractions) New applications: –visualize data where weak actives are significant –optimize compound properties, along with activity Solution: –Define custom units for all commonly measured/calculated properties in a GUI – Examples: –CLogP(5/3/1) –Permeability: 0/100/300 –Solubility(uM): 0/100/300 …SAReport_custom_units.svl, A. Deschenes, *available* from SVL exchange 6 pie sectors = 6 cpds with these R-groups Scaffold R6 pIC50 cLogP permeability
  16. 16. Customization 2: Dynamic SA/Reports SA/Reports need to be regenerated in MOE whenever new compounds are synthesized – In an active project, this happens relatively frequently… One solution to stay current: automated workflow – KNIME, an open source workflow tool, with comp chem nodes available from multiple vendors
  17. 17. Automating SA/Report production SA/Report KNIME node –Inputs: data (port 0), scaffolds (optional, port 1) –Activity fields can be configured –Custom units can be defined and incorporated
  18. 18. Example KNIME workflow for SA/Report  Many aspects can be customized Generate SA/Report Save URL (Cron job to run this nightly or weekly) Input scaffolds Input molecule data Filter by scaffold / properties Data manipulation
  19. 19. Outline SA/Report Background –Problem with out-of-box analysis of HTS data  Frequent fragment scaffold selection – Automated and interactive solutions  Customizations for project data delivery – Custom units to visualize arbitrary data types – KNIME workflows for automated generation Case studies (project and public datasets) Conclusion
  20. 20. GSK project example 1: HTS data analysis 28 scaffolds found in data by interactive scaffold analysis – prioritized for follow-up based on aggregate properties, believable SAR trends –  Color patterns: spot good R-group combinations –Example inference for benzothiophene scaffold: R6=OMe favored over H R8=NH2 active with >½ other substituents Combine to fill SAR holes… > > >
  21. 21. GSK project example 2: Mitigating hERG Lead series has hERG liability –Find R-groups that reduce hERG, maintain activity, selectivity selectivity hERG activity R3R10        ↓ H CH3 Cl NH2
  22. 22. PubChem example: Pyruvate Kinase screen Primary assay: AID 361: Pyruvate kinase (PyK, 4265 inhibitors) Five secondary assays: –2 orthologs: AID 1631 (human muscle isoform 2 PyK), 1721 (L. Mexicana PyK) –2 assays to eliminate false positive hits (luciferase, cytotoxicity) –1 selectivity cross-target (MT1-MMP)  Interactive scaffold selection – Chose 25, covering >50% cpds   Final report: – 6 pIC50s (listed above) – several calculated properties with custom units: MolWt, ClogP, LogD, predicted solubility/permeability
  23. 23. PubChem SAR trend elucidation Biaryl amide scaffold: R6=H, Me, OMe, OEt often hit luciferase/cytotoxicity cross-screens, are false positives R6=Et, F do not hit these assays 361_PyK_pIC50 411_lucif_pIC50 924_p53cyTox_pIC50
  24. 24. PubChem example: SAR trend elucidation SAR trends across similar scaffolds: –Active/selective R-groups on one scaffold (e.g. R10=OMe on benzothiazole) used to suggest analogs with the same R-group on related scaffolds. ? ? ? ?
  25. 25. Conclusions  MOE SA/Reports can be intuitive and valuable for project SAR analysis: –Extensions to find scaffolds –Visualize physicochemical properties –Automated generation using project data  Interactive scaffold analysis enables: –Quick identification of interesting series among HTS hits –Understanding any SAR –Comparing them to existing series from other hit ID methods, the literature and public datasets.  Automated generation of SA/Reports from current data greatly enhances their appeal as a user-friendly SAR analysis tool
  26. 26. Backup
  27. 27. Semi-automated frequent fragment scaffold picking Plot scalar fields “freq_1”, “freq_2” etc. –Pick a compd in each freq plateau above a threshold (eg. 50 out of 4000) –Choose largest fragment size i with freq_i > threshold as scaffold freq_1 freq_2 freq_3 freq_4

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