The Royal Society of Chemistry has an archive of hundreds of thousands of published articles containing various types of chemistry related data – compounds, reactions, property data, spectral data etc. RSC has a vision of extracting as much of these data as possible and providing access via ChemSpider and its related projects. To this end we have applied a combination of text-mining extraction, image conversion and chemical validation and standardization approaches. The outcome of this project will result in new chemistry related data being added to our chemical and reaction databases and in the ability to more tightly couple web-based versions of the articles with these extracted data. The ability to search across the archive will be enhanced as a result. This presentation will report on our progress in this data extraction project and discuss how we will ultimately use similar approaches in our publishing pipeline to enhance article markup for new publications.

1. Data enhancing the Royal
Society of Chemistry
publication archive
Antony Williams, Colin Batchelor,
Peter Corbett, Ken Karapetyan and
Valery Tkachenko
ACS Dallas
March 2014

2. Data Enhancing the RSC
Archive
• Publications summarise
data acquisition, analysis
and conclusions.
• Much detail in the data
• Improved navigation
includes data access
• Reanalysis of data is
limited in PDFs

4. 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

5. How is DERA going? TEXT
• We have text-mined all 21st
century articles…
>100k articles from 2000-2013
• Mostly marked up with XML, more structured,
easier to handle. Markup mostly published onto
the HTML forms of the articles
• Required multiple iterations based on
dictionaries, markup, OSCAR extraction
• New visualization approaches in development

6. Chemical Validation and
Standardization

7. The RSC Data
Repository
Deposition Gateway
Staging
databases
Compounds
Reactions
Spectra
Materials
Articles / CSSP
Compounds
Module
Spectra
Module
Reactions
Module
Materials
Module
Textmining
Module
͙
Module
Web UI for unified depositions
DropBox, Google Drive,
SkyDrive, etc
LabTroveand other templated
data
Documents
API, FTP, etc
Raw data Validated data
Staging
databases
Alldatabases are
sliced by data
sources/data
collections and
havesimple
security model
where each data
slice/sourceis
private, public or
embargoed

8. Text-Mining

9. ChemSpider Reactions

10. Reactions
• We will put reactions from our databases into
the Reactions Repository
• We will use “Reaction Validation” procedures
to clean up Daniel Lowe’s USPTO patent set
of over a million extracted reactions
• We will move ChemSpider SyntheticPages
content to the Reactions Repository
• We will use the RXNO Ontology to classify
the reactions

11. Reaction Deposition/Validation

12. ESI – Text Spectra

13. Lots of “Textual Spectra”

14. 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)

15. 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)

16. How is DERA going? Text Spectra
• Overall progress is good
• Improved algorithms for extraction of spectra
• Extraction of associated compound name
with spectrum – name to structure
conversion now
• MestreLabs have provided us with batch
conversion tool
• Work in progress – manual and automated
validation. In theory auto-assignment also

17. Visualization of Spectra
• For spectra associated with compounds we
would like to view “interactive spectra”

18. Javascript viewer with JMol

19. Figure Spectra into “Real
Spectra”?
• We are turning text into structures
• We are turning text into spectra
• And we are turning figures into spectra

20. Turn “Figures” Into Data
EXTRACTED
DATA
FIGURE

22. EXTRACTED
DATA
FIGURE

23. How is DERA going? Figures
• Validation tests performed with William
Brouwer. Good enough to proceed with
larger test set
• Ready to run process across larger collection
• Focus on 21st
century articles only for now

24. Early Test Experiments

Input : 74 supplementary data documents/ 3444 pages

Output : p2t extracted content in 1069 page instances
− 578 molecules

~ 10% false positives eg., classifies Bruker logo as
chemical object

~ 20% false negatives eg., missing some symbols
from structure
− 1151 spectra

> 80% of peaks extracted to within 1-2 decimal
places (ppm)

25. Validating Spectra
• How will we check data consistency?
• How do we know the structure and the
spectra match? Comparing image to
spectrum is NOT enough!!!
• Predict spectra, use spectral verification, use
algorithmic checking.
• Flag “dodgy data” and use crowdsourcing for
data checking
• MULTIPLE prediction technologies now
available – VERIFICATION is tougher

26. What are we extracting?
• Compounds from compound names
• Reactions from the text
• Spectral extraction – from figures and text
• Extraction of data from “tables” – not only
CSV files but literal tables in the publication –
specifically data from MedChemComm as
proof of concept

27. Building out the technology
• We are presently Open-Sourcing a chemical
registration system developed for OpenPHACTS
• We will then Open Source the Chemical
Validation and Standardization Platform
• We are working with Bob Hanson and Bob
Lancashire on Jmol/JSpecView Open Source
• We will deliver a set of Open Source widgets for
structure handling/visualization

28. Javascript viewer NMR, MS, IR

29. Grand Target
• Fingers crossed to get 21st
century spectra
converted
• Spectra associated with compounds will go
into ChemSpider
• Spectra converted from Figures but without
compound association will be captured with
Figures into the Data Repository
• Focus on IR, Raman, UV-Vis & 1D NMR

30. DERA is FINE for an archive
The WRONG WAY otherwise!
• We should NOT be mining data out of future
publications
• Structures should be submitted “correctly”
• Spectra should be digital spectral formats,
not images
• ESI should be RICH and interactive
• Data should be open, available, with meta
data and provenance

31. We can solve for Authors here
Will it be used though???

32. Advanced ESI

33. Conclusions
• Great progress in mining the archive and 21st
century articles are being enhanced on the
publishing platform iteratively
• Spectral Data is the next focus – directly
connected to our work on the data repository
• Reaction extraction, processing and
validation from articles is progressing more
slowly
• Results are content, software components
and and Open Source Contributions

34. Acknowledgments
• Bill Brouwer – Plot2Txt Development
• Carlos Cobas and Santi Dominguez
• Bob Hanson and Bob Lancashire for
Jmol/JSpecView Javascript version
• Leah McEwan and Will Dichtel
• ACD/Labs – Provider of spectroscopy tools

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