ICIC 2013 Conference Proceedings Sebastian Radestock
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Making hidden data discoverable: How to build effective drug discovery engines? Sebastian Radestock (Elsevier, Germany) In a complex IT environment comprising dozens if not hundreds of databases and likely as many user interfaces it becomes difficult if not impossible to find all the relevant information needed to make informed decisions. Historical data get lost, not normalized data cannot be compared and maintenance becomes a nightmare. We will discuss a new approach to address this issue by showing various examples and use cases on how in-house data and public data can be integrated in various ways to address the unique and individual needs of companies to keep the competitive edge.
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ICIC 2013 Conference Proceedings Sebastian Radestock
- 1. DR. S. RADESTOCK | 14 OCTOBER 2013 | MAKING HIDDEN DATA DISCOVERABLE DRUG DISCOVERY TODAY ELN Known ligands Knowledge survey Therapeutic target Chemistry Generate chemistry ideas Analyze SAR DBs Check chemical feasibility In-house Synthesize or buy Report Test Check ADME/Tox Journals & Patents ELN Flatfiles Biology Docs Journals 1
- 2. Dr. Sebastian Radestock Product Manager Reaxys Elsevier Information Systems GmbH Frankfurt am Main Germany MAKING HIDDEN DATA DISCOVERABLE: HOW TO BUILD EFFECTIVE DRUG DISCOVERY ENGINES? 2
- 3. In-house DRUG DISCOVERY TOMORROW DBs Chemistry Federated search system Known ligands Knowledge survey Biology integrator Therapeutic target Generate chemistry ideas Check chemical feasibility Analyze SAR Journals & Patents ELN Synthesize or buy Report Literature management tool Chemistry integrator Test Check ADME/Tox Biology Integrator for biomedical data Integrator for drug safety Docs ELN 3 Flatfiles
- 4. DR. S. RADESTOCK | 14 OCTOBER 2013 | MAKING HIDDEN DATA DISCOVERABLE DRUG DISCOVERY TOMORROW TWO APPROACHES TO SOLVING THE CHALLENGE OF DATA ACCESS FEDERATED MODEL WAREHOUSE APPROACH CHEMISTRY INTEGRATOR Storage and capture ELN Analysis system User Capture Storage ELN 4
- 5. DR. S. RADESTOCK | 14 OCTOBER 2013 | MAKING HIDDEN DATA DISCOVERABLE EXAMPLE IMPLEMENTATION OF THE FEDERATED MODEL FEDERATED MODEL CHEMISTRY INTEGRATOR Storage and capture In-house Analysis system In-house Expansion of the existing system by integrating Reaxys Existing system for consolidating in-house structure and bioactivity data User 5
- 6. DR. S. RADESTOCK | 14 OCTOBER 2013 | MAKING HIDDEN DATA DISCOVERABLE THE REAXYS DATA BASE • Compounds, substance property data, preparations, reactions, and bibliographic information… …from 400 core chemistry journals …from relevant chemistry patents • Manual extraction of all the data • Coverage of 500 property data fields, from basics like boiling point or melting point, via crystal data and magnetic properties, to spectra • All together 750 million data points Scientific data model CONTAINS ALL PUBLISHED AND HISTORICAL CHEMISTRY DATA • Historical chemistry data… …dating back to 1771 6
- 7. DR. S. RADESTOCK | 14 OCTOBER 2013 | MAKING HIDDEN DATA DISCOVERABLE EXPANDED BIBLIOGRAPHIC CONTENT IN REAXYS SUPPORTING MULTI-DISCIPLINARY RESEARCH AGRICULTURAL AND BIOLOGICAL SCIENCES BIOCHEMISTRY, GENETICS AND MOLECULAR BIOLOGY CHEMICAL ENGINEERING DENTISTRY EARTH AND PLANETARY SCIENCES • Bibliographic data from 16.000 periodicals covering chemistry and related sciences have been loaded into Reaxys • This goes beyond journals and patents, it includes conference proceedings, business articles, reviews etc. old new ENERGY ENGINEERING ENVIRONMENTAL SCIENCE IMMUNOLOGY AND MICROBIOLOGY MATERIALS SCIENCE MEDICINE NEUROSCIENCE PHARMACOLOGY, TOXICOLOGY AND PHARMACEUTICS PHYSICS AND ASTRONOMY VETERINARY ETC. 7
- 8. DR. S. RADESTOCK | 14 OCTOBER 2013 | MAKING HIDDEN DATA DISCOVERABLE REAXYS-TREE AND AUTOMATIC INDEXING THE NEXT STEP… COMING 2014 Title/Abstract Step 1 Index for chemistry terms Step 2 Add chemistry relevant keywords and identified chemical entities The Biosynthesis of Aristeromycin. Conversion of Neplanocin A to Aristeromycin by a Novel Enzymatic Reduction Partially purified cell-free extracts of the aristeromycin producer Streptomyces citricolor have been shown to catalyze the NADPHdependent reduction of neplanocin A to aristeromycin. Stereochemical studies revealed that the reduction proceeds with anti-geometry and involves transfer of the 4 pro-R hydrogen atom of NADPH to the 6'β position of aristeromycin. Reaxys Keywords: Aristeromycin – biosynthesis, enzymatic reduction, Neplanocin A Step 3 Translate chemical names into structures and make them searchable 8
- 9. DR. S. RADESTOCK | 14 OCTOBER 2013 | MAKING HIDDEN DATA DISCOVERABLE FEDERATED SEARCH SYSTEM WITH REAXYS LOOK-UP ACCESS TO REAXYS IS VIA THE REAXYS APPLICATION PROGRAMMING INTERFACE (API) How should the API look like? 9
- 10. DR. S. RADESTOCK | 14 OCTOBER 2013 | MAKING HIDDEN DATA DISCOVERABLE THE REAXYS APPLICATION PROGRAMMING INTERFACE (API) LESSONS LEARNED • Customers want to have access to all data in Reaxys • Substances and substance property data • Reactions and reaction details • Citations • Customers want to have access to all functionality of Reaxys • Exact structure and reaction searching, similarity and substructure searching • Factual queries • Further processing of hitsets • The Reaxys API was designed to be based on exchanging XML code between the user and the Reaxys server via HTML POST requests • Security and usage tracking is an issue • Secure communication via HTTPS POST is supported • The Reaxys API is stateful, and login is required 10
- 11. DR. S. RADESTOCK | 14 OCTOBER 2013 | MAKING HIDDEN DATA DISCOVERABLE LIMITATIONS OF THE FEDERATED MODEL SOME DISADVANTAGES TO CONSIDER FEDERATED MODEL CHEMISTRY INTEGRATOR Storage and capture In-house Analysis system Performance and availability of the system is dependent on the source systems No clean-up and normalization of the data In-house ELN User Architecture allows easy expansion when new data source becomes available 11
- 12. DR. S. RADESTOCK | 14 OCTOBER 2013 | MAKING HIDDEN DATA DISCOVERABLE EXAMPLE IMPLEMENTATION OF THE WAREHOUSE APPROACH Bioactivity data was normalized, and structures were deduplicated WAREHOUSE APPROACH CHEMISTRY INTEGRATOR Structures A customer set up a system that contains structure and bioactivity data, and IP information All data was extracted, translated and loaded to fit into one unified data model (UDM) Analysis system Capture Storage User In-house 12
- 13. DR. S. RADESTOCK | 14 OCTOBER 2013 | MAKING HIDDEN DATA DISCOVERABLE PLATFORM FOR STRUCTURE AND BIOACTIVITY DATA STRUCTURE DATA FROM REAXYS COMES FROM THE REAXYS STRUCTURE FLAT FILE 13
- 14. DR. S. RADESTOCK | 14 OCTOBER 2013 | MAKING HIDDEN DATA DISCOVERABLE LIMITATIONS OF THE OF THE WAREHOUSE APPROACH SOME DISADVANTAGES TO CONSIDER WAREHOUSE APPROACH CHEMISTRY INTEGRATOR Structures Long implementation time and associated high cost Difficult to accommodate differences or changes in data types Analysis system Capture Storage User In-house 14
- 15. DR. S. RADESTOCK | 14 OCTOBER 2013 | MAKING HIDDEN DATA DISCOVERABLE EXAMPLE IMPLEMENTATION OF A FLEXIBLE APPROACH A CONTENT INTEGRATION SOLUTION THAT IS NOW AVAILABLE TO ALL REAXYS USERS FEDERATED MODEL Real-time commercial availability and pricing information comes via an eMolecules API WAREHOUSE APPROACH CHEMISTRY INTEGRATOR Capture Storage and capture Storage Structures Pricing Structures Structures from PubChem and eMolecules have been integrated into Reaxys User Using multiple storage systems eliminates the need for one UDM 15
- 16. Results are available from different data source tabs The substance crosslinking icon allows to switch to corresponding substances in other data sources 16
- 17. Results are available from different data source tabs Filter for substances that are/aren’t contained in other data sources 17
- 18. Results are available from different data source tabs Filter for substances that are/aren’t contained in other data sources 18
- 19. The commercial availability icon allows to check real-time pricing information from eMolecules 19
- 20. PubChem property headers with direct links to PubChem 20
- 21. DR. S. RADESTOCK | 14 OCTOBER 2013 | MAKING HIDDEN DATA DISCOVERABLE FLEXIBLE APPROACH FOR INTEGRATION LESSONS LEARNED • Reaxys has proven to be extremely powerful as analysis and database system • Separation of the data from different data sources into multiple storage systems is the way to go… • … if a powerful crosslinking mechanism is in place • Some pieces of information that are subject to frequent updates should be integrated using the federated model 21
- 22. DR. S. RADESTOCK | 14 OCTOBER 2013 | MAKING HIDDEN DATA DISCOVERABLE EXAMPLE IMPLEMENTATION OF A FLEXIBLE APPROACH A CONTENT INTEGRATION SOLUTION THAT ELSEVIER BUILT FOR ROCHE FEDERATED MODEL Reaxys with PubChem and eMolecules integrated WAREHOUSE APPROACH CHEMISTRY INTEGRATOR Capture Storage and capture Storage Structures Pricing Structures Integration of Roche proprietary data on chemistry experiments User ELN In-house Reactions 22
- 23. DR. S. RADESTOCK | 14 OCTOBER 2013 | MAKING HIDDEN DATA DISCOVERABLE INTEGRATION OF ROCHE IN-HOUSE DATA THE SITUATION AT ROCHE YESTERDAY… AND TODAY 23
- 24. https://reaxys.roche.com Up to four Roche reaction data sources are supported Roche-specific data is included in the Output (PDF, MS-Word etc.) Filters on Roche-specific data fields Data on references and/or experiments, including PDF links Normalized Roche-specific reaction data 24
- 25. https://reaxys.roche.com A Roche icon allows to switch to a Roche in-house repository The reaction crosslinking icon allows to switch to corresponding reactions in other data sources 25
- 26. https://reaxys.roche.com Start building a synthesis tree by clicking on the synthesize link 26
- 27. https://reaxys.roche.com Synthesis planner opens up The first step of the synthesis plan is selected from the Roche data source 27
- 28. https://reaxys.roche.com Add a second step One step has been added 28
- 29. https://reaxys.roche.com The second step of the synthesis plan is selected from Reaxys New reactions are loaded 29
- 30. https://reaxys.roche.com Another step has been added Roche Experimental details of the “mixed” synthesis plan are summarized in a table. 30
- 31. DR. S. RADESTOCK | 14 OCTOBER 2013 | MAKING HIDDEN DATA DISCOVERABLE INTEGRATION OF ROCHE IN-HOUSE DATA CUSTOMER FEEDBACK • Usability and acceptance tests by Roche showed: • Increased productivity of researchers at Roche • Increased discoverability of the Roche reaction content • Reduced maintenance effort for Roche: • Legacy systems were decommissioned • Roche gets on-going maintenance and functionality improvements by Elsevier • Not compromise in security • Flexible approach: • Additional data sources have been added 31
- 32. In-house DRUG DISCOVERY TOMORROW DBs Chemistry Federated search system Known ligands Knowledge survey Therapeutic target Generate chemistry ideas Check chemical feasibility Analyze SAR ELN Synthesize or buy Report Test Check ADME/Tox Biology MedScan Journals & Patents Docs ELN 32 Flatfiles
- 33. THANK YOU – QUESTIONS? Dr. Sebastian Radestock Product Manager Reaxys Elsevier Information Systems GmbH Frankfurt am Main, Germany s.radestock@elsevier.com 33