Chemoinformatics

1. Chemoinformatics David Wild, djwild@indiana.edu Bioinformatics Retreat, Feb 2nd, 2007 David Wild, Geoffrey Fox, Bioinformatics retreat, February 2007. Page 1

2. Current state of chemoinformatics research • What works and what doesn’t • Fingerprints, clustering and diversity • QSAR - predictive and descriptive methods, virtual screening • 3D similarity, pharmacophores & docking • Visualization, organization and navigation of chemical datesets • Current buzz areas in chemoinformatics • How can we use our internal strengths to do something new, important and impressive? David Wild, Geoffrey Fox, Bioinformatics retreat, February 2007. Page 2

3. What works and what doesn’t • 2D structure and similarity searching well established • Lots of papers comparing fingerprints for similarity • Some recent evidence Scitegic ECFPs better for recall of actives • Clustering well established but definite room for improvement • Traditional methods Wards, K-means, Jarvis Patrick • Recently single pass similarity cutoff methods used for very fast organization - >0.85 for similar activity, >0.55 for QSAR • Data mining methods - ROCK, Chameleon, Cure, etc unexplored • Diversity hot -> cold -> smart • QSAR - poor relation of academic work to industry usefulness • Lots of papers: “this method works best on this dataset” • Random forests appear practically to work rather well • Interpretability vs predictive ability • Predictive methods for LogP, pKa, solubility, etc work reasonably • Virtual screening virtually useless unless tied in with HTS screening process. However, is useful for exploring around leads. David Wild, Geoffrey Fox, Bioinformatics retreat, February 2007. Page 3

4. What works and what doesn’t • Mostly, 3D methods haven’t worked out yet • Similarity & QSAR - Almost every paper: 2D better for recall and precision but 3D methods give “interesting ideas”. Useful for “lead hopping” • Pharmacophore searching not widely used • Docking - very useful for visual inspection, poor correlation of scoring functions with binding • Visualization, organization and navigation of datasets • Still not clear how to work with datasets > few hundred compounds • Dot plots, spreadsheet-based methods work minimally • Need for UI design and research David Wild, Geoffrey Fox, Bioinformatics retreat, February 2007. Page 4

5. The current buzz in chemoinformatics • Decorporatization and commoditization of data and software • MLSCN, PubChem, open source, small companies • Crisis for the software companies, nice for academia • Pharma companies in the brown stuff without a paddle • Integration with other “ics” • Data mining chemical/genomic information • Linking compounds -> proteins -> pathways, etc (e.g. KEGG) • Fuzzy boundaries, integration with science and informatics • Microsoft 2020 vision for science • Integration of text and structure searching • Semantic web, services and mashups will probably have a BIG impact: exporting best of breed… what happens to the rest? David Wild, Geoffrey Fox, Bioinformatics retreat, February 2007. Page 5

6. Suggested collaboration areas • Chem/bio/complex systems mashups using web services in each of the areas: nice, confined projects for students once you have the infrastructure • Chem and complex can work together on integrating text and structure-based searching, indexing and crawling (e.g. networks of web services and databases), and intelligent agents • Data mining of chemogenomic information • Integration of advanced chemoinformatics methods with systems biology and pathway mapping tools • Performing research to establish best practices for areas of chemoinformatics • Tackling algorithmic problems for which there is currently no good solution - docking and scoring David Wild, Geoffrey Fox, Bioinformatics retreat, February 2007. Page 6

7. Cyberinfrastructure Geoffrey Fox Computer Science, Informatics and Physics David Wild, Geoffrey Fox, Bioinformatics retreat, February 2007. Page 7

8. Cyberinfrastructure • Supports distributed science – data, people, computers • Exploits Internet technology (Web2.0) adding (via Grid technology) management, security, supercomputers etc. • It has two aspects: parallel – low latency (microseconds) between nodes and distributed – highish latency (milliseconds) between nodes • Parallel needed to get high performance on individual 3D simulations, data analysis etc.; must decompose problem • Distributed aspect integrates already distinct components • Cyberinfrastructure is in general a distributed collection of parallel systems • Cyberinfrastructure is made of services (usually Web services) that are “just” programs or data sources packaged for distributed access

9. TeraGrid: Integrating NSF Cyberinfrastructure Buffalo Wisc UC/ANL Cornell Utah Iowa PU NCAR PSC IU NCSA Caltech ORNL USC-ISI UNC-RENCI SDSC TACC TeraGrid is a facility that integrates computational, information, and analysis resources at the San Diego Supercomputer Center, the Texas Advanced Computing Center, the University of Chicago / Argonne National Laboratory, the National Center for Supercomputing Applications, Purdue University, Indiana University, Oak Ridge National Laboratory, the Pittsburgh Supercomputing Center, and the National Center for Atmospheric Research. Today 100 Teraflop; tomorrow a petaflop; Indiana 20 teraflop today.

10. Cyberinfrastructure at IU • Interpreted broadly (Web presences), there are many activities at IU • Interpreted narrowly as the “programmable web” or “using Grid technologies” there are large projects in atmospheric, earthquake, ice-sheet sciences, network systems, particle physics, Crystallography and Cheminformatics • IU has an international reputation in both parallel and distributed Cyberinfrastructure including education, research and resources • IU has #31 Supercomputer in world and is part of two major National activities TeraGrid and Open Science Grid • There are several well known Bioinformatics Grids such as BIRN (mainly images) and caBIG (cancer databases) from NIH and MyGrid from UK (EBI) • Could be opportunities to link Biology and Informatics/CS in Cyberinfrastructure projects

11. Programmableweb.com 363 Web 2.0 API’sNeed Similar Life Science Portal for Tools and Data Cyberinfrastructure motivated by Web 2.0 • Capture the power of interactive Web/Grid sites enabling people to create, collaborate and build on each others work

12. Web services, workflows, portals and ontologies • Web Services allow us to quickly develop and deploy new tools, interfaces that cross disciplines and are broadly accessible • Can use simple HTTP and ignore Web Service complications • Workflows (called mashups in Web 2.0) allow us to string together collections of web services to do computation that is tailored to the science (as a one-off or for re-use). • Develop core capabilities as services and use in many different ways as in 770 Google map mashups • API’s/Languages/Data structures/Ontologies (WSDL AJAX JSON at low level) allow us to describe workflows and services in discoverable, standard ways, such that reasoning tools can piece them together to match queries • Portals enable composable reusable user interfaces • Distributed posting of services and easily available composition tools enable “everybody” to contribute • Interesting implications for “broader participation” David Wild, Geoffrey Fox, Bioinformatics retreat, February 2007. Page 12

13. Model and Data Sharing • Cyberinfrastructure requires agreed sharing standards (data structures, API’s, protocols, ontologies, languages) as intrinsically internationally distributed • There are agreed data structures for taking SequenceProteinFoldingInteraction Transparently, e.g. BLAST • Nothing at the level where genomics and proteomics is important: cells and tissues. • Partial answers: CellML, FieldML, SBML which do not link to relevant standards outside Biology • Need to connect models at these levels. Need Standard ontologies/data structures for cell behaviors to allow connections and validation • Need to connect Models like SBW (Systems Biology Workbench)/BioSpice ->Cell-level models (Compucell) ->Tissue level models (Physiome) • Model builders at these scales not CS-sophisticated. Models NOT interoperable and don’t use useful general ideas • Glazier organizing activity in this area with H. Sauro (U. Washington), W. Li (UCSD-SDSC), Hunter (U. Auckland) and NIH • Link to Open Grid Forum standard setting and community activities

14. http://www.chembiogrid.org • Database enabled quantum chemistry computations • Services to link PubChem, Supercomputers, results of high throughput Screening centers • Education; IU has unique Cheminformatics degrees • Portals

15. Chemical Informatics web service infrastructure • Database Services • Local NIH DTP Human Tumor Cell Line set • Local PubChem mirror • Derived properties database • Pub3D, PubDock • Synonym service • VARUNA quantum chemistry database • Statistics (based on R) • Regression, Neural Nets, Random Forest • LDA • K-means clustering • Plotting • T-test and distribution sampling • Computation Services • OpenEye FRED, OMEGA, FILTER, … • Cambridge OSCAR3 • BCI fingerprint generation, Ward’s, Divisive K-means clustering • Tox Tree • Similarity & fingerprint calculations (CDK) • Descriptor calculation (CDK) • 2D structure diagrams (CDK) • 2D->3D File format conversions David Wild, Geoffrey Fox, Bioinformatics retreat, February 2007. Page 15

16. Workflows - Taverna (taverna.sourceforge.net) David Wild, Geoffrey Fox, Bioinformatics retreat, February 2007. Page 16

17. David Wild, Geoffrey Fox, Bioinformatics retreat, February 2007. Page 17

18. PubDock - Chimera-based interface David Wild, Geoffrey Fox, Bioinformatics retreat, February 2007. Page 18

19. Kemo - A ChatBot for PubChem • Uses ALICE chatbot www.alicebot.org • AIML used to define knowledge base, e.g. reaction to common phrases like FIND ME, WHAT IS THE LOGP OF, etc • Can iteratively improve knowledge base • Accesses PubChem through web service interface David Wild, Geoffrey Fox, Bioinformatics retreat, February 2007. Page 19

20. Workflow in Xbaya - a meteorology tool! http://www.extreme.indiana.edu/xgws/xbaya/ David Wild, Geoffrey Fox, Bioinformatics retreat, February 2007. Page 20

21. Indexing the world’s chemical informationAND computational functionality • Crawl and index web pages, journal articles, etc. for • Structures (InChIs, SMILES) • Images (converted using Clide or ChemReader) • Names (converted using OSCAR3 or similar package) • Other information (IR spectra, reactions, etc…) • Technology still immature, but improving quickly • Problem with access to journal articles: we will assume open access in the future! • Expose computational functionality as web services, contextualize in an OWL-S ontology (semantics), and publish in a UDDI • Now we know what information we have, and what we can do with it • Develop bots and intelligent agents to automatically do useful things David Wild, Geoffrey Fox, Bioinformatics retreat, February 2007. Page 21