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Nicolas Jacq HealthGrid Association, France Credit: WISDOM initiative

World-wide in silico drug discovery against neglected and emerging diseases on grid infrastructures. Nicolas Jacq HealthGrid Association, France Credit: WISDOM initiative. Content. Overview of the WISDOM application Deployment on the EGEE grid and experience Conclusion. WISDOM.

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Nicolas Jacq HealthGrid Association, France Credit: WISDOM initiative

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  1. World-wide in silico drug discovery against neglected and emerging diseases on grid infrastructures Nicolas Jacq HealthGrid Association, France Credit: WISDOM initiative

  2. Content • Overview of the WISDOM application • Deployment on the EGEE grid and experience • Conclusion Jacq, 16.04.2007

  3. WISDOM • WISDOM (http://wisdom.healthgrid.org/) • Developing new drugs for neglected and emerging diseases with a particular focus on malaria. • Reduced R&D costs and accelerated R&D for emerging and neglected diseases • Three large calculations: • WISDOM-I (Summer 2005) • Avian Flu (Spring 2006) • WISDOM-II (Autumn 2006) Jacq, 16.04.2007

  4. In silico drug discovery presents unique challenges for Information Technologists and computer scientists DRUG DISCOVERY Clinical Phases (I-III) IN SILICO DRUG DISCOVERY Jacq, 16.04.2007

  5. Simplified virtual screening process by docking Successful examples • rapid, • cost effective… But there are limitations • Need for CPU and storage Docking: predict how small molecules bind to a receptor of known 3D structure Jacq, 16.04.2007

  6. Grid-enabled high throughput virtual screening by docking • 1 to 30 mn per docking • A few MB by output • 100 CPU years, 1 TB Millions of chemical compounds Docking software • Challenges: Speed-up the process Manage the data • Large scale deployment on grid infrastructure A few target structures Jacq, 16.04.2007

  7. Example: In silico drug discovery on avian flu • The goal is to study in silico the impact of selected point mutations on the efficiency of existing drugs and to find new potential drugs • A collaboration of 5 grid projects: Auvergrid, BioinfoGrid, EGEE-II, Embrace, TWGrid • Significant parameters: • 1 docking software: Autodock • 8 conformations of the target (N1 neuraminidase) • 300,000 selected compounds • 105 year CPU to dock all configurations on all compounds • Timescale: • First contacts: March 1st 2006 • kick-off: April 1st 2006 • Duration: 6 weeks H5 N1 Credit: Y-T Wu Jacq, 16.04.2007

  8. Results Jacq, 16.04.2007

  9. Example : In silico results from avian flu data challenge • 5 out of 6 known effective inhibitors can be identified in the first 15% of the ranking and in the first 5% reranked (2,250 compounds) • Enrichment = 5.5 and 111 (<1 in most cases) • Most known effective inhibitors lose their affinity in binding with a mutated target Original type E119A mutated type E119A GNA 11.5% GNA 2.4% 11.5% 15% cut off Jacq, 16.04.2007

  10. Example : In vitro results from avian flu data challenge • Experimental assay confirms 7 actives out of 123 purchased “potential hits” (interacting complexes with higher affinities and proper docked poses), which proved the usefulness of our work. NA Jacq, 16.04.2007

  11. Content • Overview of the WISDOM application • Deployment on the EGEE grid and experience • Conclusion Jacq, 16.04.2007

  12. Requirements for a large scale deployment on grid • Adaptation of the application to the grid • Access to a large infrastructure providing maintained resources • Use of a production system providing automated and fault-tolerant job and file management Jacq, 16.04.2007

  13. Adaptation of the application to the grid • The applications are not designed for grid computing. • The application code can not be modified. • A common strategy is to split the application into shorter tasks • License management for commercial software is not yet adapted for large infrastructure Jacq, 16.04.2007

  14. Access to a large infrastructure (1/3) • A resource estimation is needed before the deployment • The application package requires installation (and testing) • An efficient and responsive user support of the infrastructure is required Jacq, 16.04.2007

  15. Access to a large infrastructure (2/3) : the EGEE infrastructure • EGEE added value: • Large computing and storage resources (>30000 CPUs, 50Pb) • 24 hours a day availability of resources • User support • Job and Data Management • Information and Monitoring • Security • Limitations for life science applications • Short jobs • Data confidentiality • Reliability of services • … Real Time Monitor Jacq, 16.04.2007

  16. Access to a large infrastructure (3/3) : Biomedical Virtual Organization status • Biomed VO leader : V. Breton • ~80 participants, see http://egeena4.lal.in2p3.fr • Three active subgroups • Medical imaging (J. Montagnat) • Bioinformatics (C. Blanchet) • Drug discovery (V.Breton) • Biomedical VO manager: Y. Legré, legre@clermont.in2p3.fr • See http://cic.in2p3.fr (VO information, publication of data challenge…) • 1 VOMS server, 1 LFC, +20 RBs • +100 CEs, +8,000 CPUs (but many users) • +110 SEs, ~Tens of TB available on disk • 27 countries Jacq, 16.04.2007

  17. Use of a production system • Managing thousands of jobs and files is a manually labor-intensive task • Job preparation, submission and monitoring, output retrieval, failure identification and resolution, job resubmission… • The rate of submitted jobs must be carefully monitored • In order to avoid Resource Brokers overload • In order to efficiently use the resources • The amount of transferred data impacts on grid performance • The data must be installed on the grid • Storing subsets of the database instead of large unique compound files • Grid process introduces significant delays • The submitted jobs must be sufficiently long in order to reduce the impact of this middleware overhead Jacq, 16.04.2007

  18. Use of a production system • Other production system from HEP experiments on EGEE • The ATLAS production system - The ATLAS experiment • BOSS and CRAB - The CMS experiment • Alien - The Alice experiment • DIRAC - The LHCb experiment • DIANE - CERN • Ganga, a user interface • GridICE and Monalisa, two monitoring services for users Jacq, 16.04.2007

  19. Schema of the WISDOM production environment User Interface User Interface CEs &WNs SEs Submits the jobs CEs &WNs SEs D M S WMS WMS WISDOM production system FlexX job FlexX Checks job status Resubmits Statistics Structure file FLEXlm FlexLM Compounds file Statistics license license Output file Docking information Local server HealthGrid Server Web Site Web Site WISDOM DB Output DB inputs outputs Jacq, 16.04.2007

  20. A huge international effort for WISDOM-II Significant contributions from EELA, EUMedGRID and EUChinaGRID Over 420 CPU years in 10 weeks A record throughput of 100,000 docked compounds per hour WISDOM calculations used FlexX from BioSolveIT (6k free, floating licenses) Jacq, 16.04.2007

  21. Origin of failures during the WISDOM-I deployment Grid success rate 63% After substracting license server and WISDOM failures Jacq, 16.04.2007

  22. Success rates of the deployments • WISDOM-I • User success rate :46% • License server is a bottleneck • Grid success rate :63% • Heterogeneous and dynamic nature of the grid • Power cut, air-conditionning, mis-configuration, overload… • Stress usage • Automatic jobs (re)submission (“sink-hole” effect) • WISDOM against avian flu • Grid success rate:80% • Constant and slower job submission flow • Manual control of resubmission process • WISDOM fault-tolerance improved • Grid reliability improved (Workload Management System) Jacq, 16.04.2007

  23. Content • Overview of the WISDOM application • Deployment on the EGEE grid and experience • Conclusion Jacq, 16.04.2007

  24. Summary (1/2) • The experiments demonstrated how grid infrastructures have a tremendous capacity to mobilize very large CPU resources for well targeted goals during a significant period of time • 1st large scale deployment of life sciences application on a grid infrastructure • The deployments have been a very useful experience in identifying the limitations and bottlenecks of the EGEE infrastructure and middleware • The reliability is still the major issue for the WISDOM production system and the EGEE middleware • Large scale deployment still requires to be grid expert Jacq, 16.04.2007

  25. Summary (2/2) • WISDOM data challenge has demonstrated that collaborative production grids can be used for steps in the drug discovery process • 1st production of biochemical results on a grid infrastructure • The impact has significantly raised the interest of the research community on malaria. • Output data collection and presentation require improvements to speed-up the post-docking analysis • Storage of output metadata from the jobs in a relational database • Access to this database and to the docking output files is required Jacq, 16.04.2007

  26. Thank you • To all members of the WISDOM collaboration for their contribution to the project • To all grid nodes which committed resources and allowed the success of the initiative • To all projects which supported the initiative by providing either computing resources or manpower to develop the WISDOM environment • To BioSolveIT by offering up to 6000 free licenses of FlexX Jacq, 16.04.2007

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