Chemical Informatics & Cyberinfrastructure Collaboratory HTS Data Analysis & Virtual Screening

1. Chemical Informatics & Cyberinfrastructure CollaboratoryHTS Data Analysis & Virtual Screening David J. Wild Visiting Assistant Professor Indiana University School of Informatics djwild@indiana.edu http://www.informatics.indiana.edu/djwild/ David Wild – ECCR Meeting, October 2005. Page 1

2. Content • Web services framework for HTS data analysis • Long-term approach • Priorities for web service development • Rapid dataset organization using cluster analysis • Interface tools for navigation and analysis • Virtual screening David Wild – ECCR Meeting, October 2005. Page 2

3. Thoughts relating to Pubchem HTS analysis(and more widely applicable) • Existing approaches do not scale up well • Scientists’ questions are probably not going to be conceptually complex, but finding the answers can currently be very time consuming and/or complex (for a human) • “who else is working on this chemical structure I just made (or similar ones)?” • “are there any compounds in Pubchem (or elsewhere) that might bind to the active site of this protein I just resolved?” • “do any compounds related to this one exhibit toxic side effects?” • We need to figure out just what the questions are!(Contextual Inquiry, Use cases) • Answers are often “stale” after a short period of time – questions need to be re-answered as new information is generated • Almost all available systems are passive, and follow the(web) browsing model David Wild – ECCR Meeting, October 2005. Page 3

4. Wild, D.J., Strategies for Using Information Effectively in Early-stage Drug Discovery, in Ekins, S. (ed), Computer Applications in Pharmaceutical Research and Development, submitted July 2005 David Wild – ECCR Meeting, October 2005. Page 4

5. human interfaces intelligent agents web services databases & tools Wild, D.J., Strategies for Using Information Effectively in Early-stage Drug Discovery, in Ekins, S. (ed), Computer Applications in Pharmaceutical Research and Development, submitted July 2005 David Wild – ECCR Meeting, October 2005. Page 5

6. Request from Human Interface USE-CASE SCRIPT Invoke New Structure Service Convert structures to 3D Dock results & protein file Extract any hits Return links for visualization AGENT / SMART CLIENT Parse request Select appropriate use cases and/or web service(s) Schedule as necessary “find me all the structures that fit the enclosed protein for The next three months” UDDI WSDL SOAP New Structure Service Search online databases for recent structures Search local databases for recent structures Merge Results Online database (e.g. PubChem) Local database 3D Docking Tool 2D-3D converter 3D visualizer atomic services aggregate services David Wild – ECCR Meeting, October 2005. Page 6

7. Priorities for web service development • Rapid dataset search and organization • Search of PubChem (SOAP interface already exists) • Search of local gNova / PostgreSQL database • Clustering using BCI (Digital Chemistry) Divisive K-Means • BCI Markush searching • Interface tools for navigation and analysis • Integration with Spotfire • ChemTK (or other spreadsheet-metaphor product) • Develop entirely new interface tools (usability studies) • Virtual Screening • Molecular docking with OpenEye FRED • Property calculation with Molinspiration / Chemaxon • PDB Search (EMBL) • Activity prediction modules (Molinspiration / RP / SVMs etc) David Wild – ECCR Meeting, October 2005. Page 7

8. Visualization & interface level tools • No matter how clever the smarts underneath, the overriding factor in usefulness will be the quality of scientists’ interaction with the system • Contextual Design, Interaction Design (Cooper) and Usability Studies have proven effective in designing the right interfaces for the right peoplein chemical informatics, and deserve investigation for future use in this project • Possibility of multiple interfaces for different people groups(Cooper’s “primary personas”) • Don’t assume the browser interface – email / NLP ? • Start with the basics • 2D chemical structure drawing (input) • Visualization of large numbers of chemical structures in 2D • 3D chemical structure visualization • Planning on evaluation of NLP, email, RSS, etc. as well asbrowser-based interfaces David Wild – ECCR Meeting, October 2005. Page 8

9. Visualization methods for datasets & clusters • Partitions • Spreadsheets • Enhanced Spreadsheets • 2D or 3D plots • Hierarchies • Dendograms • Tree Maps • Hyperbolic Maps David Wild – ECCR Meeting, October 2005. Page 9

10. Supplemental Slides David Wild – ECCR Meeting, October 2005. Page 10

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13. Use Case #1Are there any good ligands for my target? • A chemist is working on a project involving a particular protein target, and wants to know: • Any newly published compounds which might fit the protein receptor site • Any published 3D structures of the protein or of protein-ligand complexes • Any interactions of compounds with other proteins • Any information published on the protein target David Wild – ECCR Meeting, October 2005. Page 13

14. Use Case #1Are there any good ligands for my target? • A chemist is working on a project involving a particular protein target, and wants to know: • Any newly published compounds which might fit the protein receptor site  gNova / PostgreSQL, PubChem search, FRED Docking • Any published 3D structures of the protein or of protein-ligand complexes  PDB search • Any interactions of compounds with other proteins  gNova / PostgreSQL, PubChem search • Any information published on the protein target  Journal text search David Wild – ECCR Meeting, October 2005. Page 14

15. Use Case #2Who else is working on these structures? • A chemist is working on a chemical series for a particular project and wants to know: • If anyone publishes anything using the same or related compounds • Any new compounds added to the corporate collection which are similar or related • If any patents are submitted that might overlap the compounds he is working on • Any pharmacological or toxicological results for those or related compounds • The results for any other projects for which those compounds were screened David Wild – ECCR Meeting, October 2005. Page 15

16. Use Case #2Who else is working on these structures? • A chemist is working on a chemical series for a particular project and wants to know: • If anyone publishes anything using the same or related compounds ~ PubChem search • Any new compounds added to the corporate collection which are similar or related  gNova CHORD / PostgreSQL • If any patents are submitted that might overlap the compounds he is working on~ BCI Markush handling software • Any pharmacological or toxicological results for those or related compounds  gNova CHORD / PostgreSQL, MiToolkit • The results for any other projects for which those compounds were screened  gNova CHORD / PostgreSQL, PubChem search David Wild – ECCR Meeting, October 2005. Page 16

17. Use Case - PubchemWhich of these hits should I follow up? • An MLI HTS experiment has produced 10,000 possible hits out of a screening set of 2m compounds. A chemist at another laboratory wants to know if there are any interesting active series she might want to pursue, based on: • Structure-activity relationships • Chemical and pharmacokinetic properties • Compound history • Patentability • Toxicity • Synthetic feasibility David Wild – ECCR Meeting, October 2005. Page 17

18. Use Case – PubChemWhich of these hits should I follow up? • An HTS experiment has produced 10,000 possible hits out of a screening set of 2m compounds. A chemist on the project wants to know what the most promising series of compounds for follow-up are, based on: • Series selection  BCI cluster analysis • Structure-activity relationships lots of methods • Chemical and pharmacokinetic propertiesmitools, chemaxon • Compound history gNova / PostgreSQL / Pubchem search • Patentability  BCI Markush handling software • Toxicity • Synthetic feasibility • + requires visualization tools! David Wild – ECCR Meeting, October 2005. Page 18

19. Cluster Analysis and Chemical Informatics • Used for organizing datasets into chemical series, to build predictive models, or to select representative compounds • Organizational usage has not been as well studies as the other two, but see • Wild, D.J., Blankley, C.J. Comparison of 2D Fingerprint Types and Hierarchy Level Selection Methods for Structural Grouping using Wards Clustering, Journal of Chemical Information and Computer Sciences., 2000, 40, 155-162. • Essentially helping large datasets become manageable • Methods used: • Jarvis-Patrick and variants • O(N2), single partition • Ward’s method • Hierarchical, regarded as best, but at least O(N2) • K-means • < O(N2), requires set no of clusters, a little “messy” • Sphere-exclusion (Butina) • Fast, simple, similar to JP • Kohonen network • Clusters arranged in 2D grid, ideal for visualization David Wild – ECCR Meeting, October 2005. Page 19

20. Limitations of Ward’s method forlarge datasets (>1m) • Best algorithms have O(N2) time requirement (RNN) • Requires random access to fingerprints • hence substantial memory requirements (O(N)) • Problem of selection of best partition • can select desired number of clusters • Easily hit 4GB memory addressing limit on 32 bit machines • Approximately 2m compounds David Wild – ECCR Meeting, October 2005. Page 20

21. Scaling up clustering methods • Parallelisation • Clustering algorithms can be adapted for multiple processors • Some algorithms more appropriate than others for particular architectures • Ward’s has been parallelized for shared memory machines, but overhead considerable • New methods and algorithms • Divisive (“bisecting”) K-means method • Hierarchical Divisive • Approx. O(NlogN) David Wild – ECCR Meeting, October 2005. Page 21

22. Divisive K-means Clustering • New hierarchical divisive method • Hierarchy built from top down, instead of bottom up • Divide complete dataset into two clusters • Continue dividing until all items are singletons • Each binary division done using K-means method • Originally proposed for document clustering • “Bisecting K-means” • Steinbach, Karypis and Kumar (Univ. Minnesota)http://www-users.cs.umn.edu/~karypis/publications/Papers/PDF/doccluster.pdf • Found to be more effective than agglomerative methods • Forms more uniformly-sized clusters at given level David Wild – ECCR Meeting, October 2005. Page 22

23. BCI Divkmeans • Several options for detailed operation • Selection of next cluster for division • size, variance, diameter • affects selection of partitions from hierarchy, not shape of hierarchy • Options within each K-means division step • distance measure • choice of seeds • batch-mode or continuous update of centroids • termination criterion • Have developed parallel version for Linux clusters / grids in conjunction with BCI • For more information, see Barnard and Engels talks at: http://cisrg.shef.ac.uk/shef2004/conference.htm David Wild – ECCR Meeting, October 2005. Page 23

24. Comparative execution timesNCI subsets, 2.2 GHz Intel Celeron processor 7h 27m 3h 06m 2h 25m 44m David Wild – ECCR Meeting, October 2005. Page 24

25. Clustering a 1 million compound dataseton a 2.2 GHz Celeron Desktop Machine Results from AVIDD clusters & Teragrid coming soon…. * Time for a single run may vary due to different selection of seeds. Runtimes can be shortened e.g. by using a max. number of iterations or a % relocation cutoff. David Wild – ECCR Meeting, October 2005. Page 25

26. Divisive Kmeans: Conclusions • Much faster than Ward’s, speed comparable to K-means, suitable for very large datasets (millions) • Time requirements approximately O(N log N) • Current implementation can cluster 1m compounds in under a week on a low-power desktop PC • Cluster 1m compounds in a few hours with a 4-node parallel Linux cluster • Better balance of cluster sizes than Wards or Kmeans • Visual inspection of clusters suggests better assembly of compound series than other methods • Better clustering of actives together than previously-studied methods • Memory requirements minimal • Experiments using AVIDD cluster and Teragrid forthcoming(50+ nodes) David Wild – ECCR Meeting, October 2005. Page 26

27. Visualization & interface level tools • No matter how clever the smarts underneath, the overriding factor in usefulness will be the quality of scientists’ interaction with the system • Contextual Design, Interaction Design (Cooper) and Usability Studies have proven effective in designing the right interfaces for the right peoplein chemical informatics [collaboration with HCI?] • Possibility of multiple interfaces for different people groups(Cooper’s “primary personas”) • Don’t assume the browser interface – email / NLP ? • Start with the basics • 2D chemical structure drawing (input) • Visualization of large numbers of chemical structures in 2D • 3D chemical structure visualization • Planning on evaluation of NLP, email, RSS, etc. as well asbrowser-based interfaces David Wild – ECCR Meeting, October 2005. Page 27

28. Usability of 2D structure drawing tools • Key difference between “sequential” and “random” drawers • Huge difference in intuitiveness • Key factor how badly you can mess things up • Marvin Sketch ≈ JME > ChemDraw >> ISIS Draw David Wild – ECCR Meeting, October 2005. Page 28

29. Visualization methods for datasets & clusters • Partitions • Spreadsheets • Enhanced Spreadsheets • 2D or 3D plots • Hierarchies • Dendograms • Tree Maps • Hyperbolic Maps David Wild – ECCR Meeting, October 2005. Page 29

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32. VisualiSAR – with a nod to Edward Tufte. See http://www.daylight.com/meetings/mug99/Wild/Mug99.html David Wild – ECCR Meeting, October 2005. Page 32

33. Tree Maps – very Tufte-esque David Wild – ECCR Meeting, October 2005. Page 33

34. External support • ECCR grant ($500,000) • 20% Co-PI with Fox for development of web services for HTS data organization and visualization • May lead to $5m/5 years grant for full center • Applied for Microsoft Smart Clients for eScience grant ($50,000) • Including Marlon Pierce in the Community Grids lab • Peter Murray-Rust group, Cambridge – offering expertise and assistance with web services • IO-Informatics – provision of Sentient software and consulting • BCI – clustering, structure enumeration & toolkit, consulting • OpenEye – a range of calculation tools, FRED docking • Molinspiration – MiTools Toolkit • gNova – CHORD chemical database system • Possible financial support from company in the UK David Wild – ECCR Meeting, October 2005. Page 34

35. Technology • Perl SOAP::Lite • Will be used for initial web service development • Doesn’t really implement WSDL & UDDI • Apache Axis & Tomcat • Deploy WSDL for web services • BPEL4WS – Business Process Execution Language • For aggregation of web services • http://www-128.ibm.com/developerworks/library/specification/ws-bpel/ • Microsoft .NET & C# David Wild – ECCR Meeting, October 2005. Page 35

36. Current activities • Core activities • Development of use-cases • Development of initial web services (Perl SOAP::Lite) • Use of Taverna to prototype use-case scripts • Basic research on future components • Organizing large amounts of chemical informationfor human consumption • Development of very fast parallel clustering techniques – to be exposed as web services • Selection of interface-level tools for basic interaction • Chemical structure drawing, display • Investigation of email, NLP, RSS, and browser interfaces • Interface-level tools for visualization, navigation and analysis • Cluster and dataset visualization, natural language interfaces) David Wild – ECCR Meeting, October 2005. Page 36

37. Sentient - an alternative approachto managing heterogenous data sources • Collaboration with IO-Informatics (along with Cornell, and UCSD) for the investigation of service-oriented architectures in life sciences research using Sentient software • Aim to integrate several sources of information relating to Alzheimer’s Disease (brain imaging, morphology, gene expression) so that cross-dataset biomarkers can be identified • Sentient usies Intelligent Multidimensional Objects (IMOs) to define and query data sources and the tools used toaccess them • Still a browsing approach, but with a layer of coherenceand “intelligence” • Hope to expand to include chemistry data • Can also be used as an interface-level tool David Wild – ECCR Meeting, October 2005. Page 37

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40. Conclusions so far • Effective exploitation of large volumes and diverse sources of chemical information is a critical problem to solve, with a potential huge impact on the drug discovery process • Most information needs of chemists and drug discovery scientists are conceptually straightforward, but complex (for them) to implement • All of the technology is now in place to implement may of these information need “use-cases”: the four level model using service-oriented architectures together with smart clients look like a neat way of doing this • The aggregation and interface levels offer the most challenges • In conjunction with grid computing, rapid and effective organization and visualization of large chemical datasets is feasible in a web service environment • Some pieces are missing: • Chemical structure search of journals (wait for InChI) • Automated patent searching • Effective dataset organization • Effective interfaces, especially visualization of large numbers of 2D structures(we’re working on it!) David Wild – ECCR Meeting, October 2005. Page 40