Access to scientific information has changed dramatically as a result of the web and its underpinning technologies. The quantities of data, the array of tools available to search and analyze, the devices and the shift in community participation continues to expand while the pace of change does not appear to be slowing. RSC hosts a number of chemistry data resources for the community including ChemSpider, one of the community’s primary online public compound databases. Containing tens of millions of chemical compounds and its associated data ChemSpider serves data tens of thousands of chemists every day. The platform offers the ability for crowdsourcing enabling the community to deposit and curate data. This presentation will provide an overview of the expanding reach of this cheminformatics platform and the nature of the solutions that it helps to enable including structure validation and text mining and semantic markup. ChemSpider is limited in scope as a chemical compound database and we are presently architecting the RSC Data Repository, a platform that will enable us to extend our reach to include chemical reactions, analytical data, and diverse data depositions from chemists across various domains. We will also discuss the possibilities it offers in terms of supporting data modeling and sharing. The future of scientific information and communication will be underpinned by these efforts, influenced by increasing participation from the scientific community.

1. Experiences in Hosting Big
Chemistry Data Collections
for the Community
Antony Williams
July 30th
2014, NIST

2. Overview of Our Activities
• The Royal Society of Chemistry as a
provider of chemistry for the community:
• As a charity
• As a scientific publisher
• As a host of commercial databases
• As a partner in grant-based projects
• As the host of ChemSpider
• And now in development : the RSC Data
Repository for Chemistry

3. • ~30 million chemicals and growing
• Data sourced from >500 different sources
• Crowd sourced curation and annotation
• Ongoing deposition of data from our
journals and our collaborators
• Structure centric hub for web-searching
• …and a really big dictionary!!!

4. ChemSpider

5. ChemSpider

6. ChemSpider

7. Experimental/Predicted Properties

8. Literature references

9. Patents references

10. RSC Books

11. Google Books

12. Vendors and data sources

13. Crowdsourced “Annotations”
• Users can add
• Descriptions, Syntheses and Commentaries
• Links to PubMed articles
• Links to articles via DOIs
• Add spectral data
• Add Crystallographic Information Files
• Add photos
• Add MP3 files
• Add Videos

14. APIs

15. APIs

16. WebBook and ChemSpider

17. WebBook and ChemSpider

18. WebBook and ChemSpider

19. WebBook and ChemSpider

20. WebBook and ChemSpider

21. Javascript viewer NMR, MS, IR

22. Aspirin on ChemSpider

23. Many Names, One Structure

24. What is the Structure of Vitamin K?

25. MeSH
• A lipid cofactor that is required for normal
blood clotting.
• Several forms of vitamin K have been
identified:
• VITAMIN K 1 (phytomenadione) derived
from plants,
• VITAMIN K 2 (menaquinone) from bacteria,
and synthetic naphthoquinone provitamins,
• VITAMIN K 3 (menadione).

26. What is the Structure of Vitamin K?

27. The ultimate “dictionary”
• Search all forms of structure IDs
• Systematic name(s)
• Trivial Name(s)
• SMILES
• InChI Strings
• InChIKeys
• Database IDs
• Registry Number

28. Linking Names to Structures

29. Semantic Mark-up of Articles

30. Data Quality Issues
Williams and Ekins, DDT, 16: 747-750 (2011)
Science Translational Medicine 2011

31. Data quality is a known issue

32. Standardize
• Use the SRS as a guidance document for
standardization
• Adjust as necessary to our needs

33. Nitro groups

34. Salt and Ionic Bonds

35. Ammonium salts

36. CVSP Filtering and Flagging

37. Openness and Quality Issues
Williams and Ekins, DDT, 16: 747-750 (2011)
Science Translational Medicine 2011

38. Substructure # of
Hits
# of
Correct
Hits
No
stereochemistry
Incomplete
Stereochemistry
Complete but
incorrect
stereochemistry
Gonane 34 5 8 21 0
Gon-4-ene 55 12 3 33 7
Gon-1,4-diene 60 17 10 23 10

39. Crowdsourced Enhancement
• The community can clean and enhance the
database by providing Feedback and direct
curation
• Tens of thousands of edits made

40. Data Quality is Work
• Cholesterol
• Taxol

41. Maybe we can help?
• Is there an interest in data checking the
WebBook or other NIST data sources?

42. Publications-summary of work
• Scientific publications are a summary of work
• Is all work reported?
• How much science is lost to pruning?
• What of value sits in notebooks and is lost?
• Publications offering access to “real data”?
• How much data is lost?
• How many compounds never reported?
• How many syntheses fail or succeed?
• How many characterization measurements?

43. What are we building?
• We are building the “RSC Data Repository”
• Containers for compounds, reactions, analytical
data, tabular data
• Algorithms for data validation and standardization
• Flexible indexing and search technologies
• A platform for modeling data and hosting existing
models and predictive algorithms

44. Deposition of Data

45. Compounds

46. Reactions

47. Analytical data

48. Crystallography data

49. Can we get historical data?
• Text and data can be mined
• Spectra can be extracted and converted
• SO MUCH Open Source Code available

50. Text Mining
The N-(β-hydroxyethyl)-N-methyl-N'-(2-trifluoromethyl-1,3,4-
thiadiazol-5-yl)urea prepared in Example 6 , thionyl chloride
( 5 ml ) and benzene ( 50 ml ) were charged into a glass
reaction vessel equipped with a mechanical stirrer ,
thermometer and reflux condenser .
The reaction mixture was heated at reflux with stirring , for a
period of about one-half hour .
After this time the benzene and unreacted thionyl chloride
were stripped from the reaction mixture under reduced
pressure to yield the desired product N-(β-chloroethyl)-N-
methyl-N'-(2-trifluoromethyl-1,3,4-thiaidazol-5-yl)urea as a
solid residue

51. Text Mining
The N-(β-hydroxyethyl)-N-methyl-N'-(2-trifluoromethyl-1,3,4-
thiadiazol-5-yl)urea prepared in Example 6 , thionyl chloride
( 5 ml ) and benzene ( 50 ml ) were charged into a glass
reaction vessel equipped with a mechanical stirrer ,
thermometer and reflux condenser .
The reaction mixture was heated at reflux with stirring , for a
period of about one-half hour .
After this time the benzene and unreacted thionyl chloride
were stripped from the reaction mixture under reduced
pressure to yield the desired product N-(β-chloroethyl)-N-
methyl-N'-(2-trifluoromethyl-1,3,4-thiaidazol-5-yl)urea as a
solid residue

52. Text spectra?
13C NMR (CDCl3, 100 MHz): δ = 14.12 (CH3),
30.11 (CH, benzylic methane), 30.77 (CH,
benzylic methane), 66.12 (CH2), 68.49 (CH2),
117.72, 118.19, 120.29, 122.67, 123.37, 125.69,
125.84, 129.03, 130.00, 130.53 (ArCH), 99.42,
123.60, 134.69, 139.23, 147.21, 147.61, 149.41,
152.62, 154.88 (ArC)

53. 1H NMR (CDCl3, 400 MHz):
δ = 2.57 (m, 4H, Me, C(5a)H), 4.24 (d, 1H, J = 4.8 Hz,
C(11b)H), 4.35 (t, 1H, Jb = 10.8 Hz, C(6)H), 4.47 (m, 2H,
C(5)H), 4.57 (dd, 1H, J = 2.8 Hz, C(6)H), 6.95 (d, 1H, J =
8.4 Hz, ArH), 7.18–7.94 (m, 11H, ArH)

54. Turn “Figures” Into Data

55. Make it interactive

56. SO MANY reactions!

57. Extracting our Archive
• What could we get from our archive?
• Find chemical names and generate structures
• Find chemical images and generate structures
• Find reactions
• Find data (MP, BP, LogP) and deposit
• Find figures and database them
• Find spectra (and link to structures)

58. Models published from data

59. Text-mining Data to compare

60. How is DERA going?
• We have text-mined all 21st
century articles…
>100k articles from 2000-2013
• Marked up with XML and published onto the
HTML forms of the articles
• Required multiple iterations based on
dictionaries, markup, text mining iterations
• New visualization tools in development – not
just chemical names. Add chemical and
biomedical terms markup also!

61. Work in Progress

62. Work in Progress

63. Work in Progress

64. Work in Progress

65. Dictionary
(ontologies)RSC ontologies
(methods,
reactions)
Dictionary
(chemistry)
Text-mining
Curated dictionaries for known names
ACD N2S
OPSIN
Unknown names: automated
name to structure conversion
XML ready for
publication
Marked-up
XML
Production
processes
CDX integration
(coming soon)
Chemical
structures SD
file
Is It Easy?

66. Acknowledgments
• Regarding InChI – Steve Stein, Steve
Heller, Dmitrii Tchekhovskoi, Igor Pletnev

67. Email: williamsa@rsc.org
ORCID: 0000-0002-2668-4821
Twitter: @ChemConnector
Personal Blog: www.chemconnector.com
SLIDES: www.slideshare.net/AntonyWilliams
Thank you