Progress in the biomedical sciences is critically dependent on explicit chemical structures and bioactivity results described in text. This applies across drug discovery, pharmacology, chemical biology, and metabolomics. However the entombing of the majority of these structures and associated data within patents, papers, abstracts and web pages has been a major barrier to progress. This presentation introduces the current public information flow from documents and its associated barriers, such as inadequate author specification of structures, journal pay walls precluding text mining and the patchiness of MeSH chemistry annotation for PubMed-to-PubChem connectivity. It then reviews trends that are lowering these barriers. These include the Google merge of over 50 million InChIKey(s) from PubChem, ChemSpider and UniChem, ChEMBL containing SAR for 0.8 million structures from 50K medicinal chemistry papers, over 20 million abstracts in PubMed, and full-text open patent chemistry in SureChemOpen bringing PubChem patent-extracted structures to 15 million. In addition, options such as Open Lab Books and figshare are expanding the choices for surfacing new structures. Methods will be outlined for establishing document-to-document and document-to-database links via chemical structures. These include the PubChem toolbox, protein targets in UniProt, PubChem BioAssay, ChEMBL indexing in UK PMC, SureChemOpen, chemicalize.org for text name-to-structure conversion , OSRA for image-to-structure conversion, Venny for set comparisons and InChIKey searching in Google [1]. Combined use of these approaches to make joins between patents, papers, abstracts chemical database entries, SAR data and drug target protein sequences will be illustrated with recent novel antimalarial lead compounds, patent-only BACE2 inhibitors and company code numbers in the NCATS repurposing list.

1. [1]
Closing the gap between chemistry and
biology: Joining between text tombs and
databases
Presentation for Uppsla University Department of Neuroscience, Sept 2013
By Christopher Southan
Curator for IUPHARdb, http://www.guidetopharmacology.org/
Queen's Medical Research Institute, University of Edinburgh
Email: cdsouthan@hotmail.com
Twitter: http://twitter.com/#!/cdsouthan
Blog: http://cdsouthan.blogspot.com/
LinkedIN: http://www.linkedin.com/in/cdsouthan
TW2Informatics: http://www.cdsouthan.info/Consult/CDS_cons.htm
Publications: http://www.citeulike.org/user/cdsouthan/order/year,,/publications
Presentations: http://www.slideshare.net/cdsouthan

2. [2]
Abstract
• Progress in the biomedical sciences is critically dependent on explicit chemical structures
and bioactivity results described in text. This applies across drug discovery, pharmacology,
chemical biology, and metabolomics. However the entombing of the majority of these
structures and associated data within patents, papers, abstracts and web pages has been a
major barrier to progress. This presentation introduces the current public information flow
from documents and its associated barriers, such as inadequate author specification of
structures, journal pay walls precluding text mining and the patchiness of MeSH chemistry
annotation for PubMed-to-PubChem connectivity. It then reviews trends that are lowering
these barriers. These include the Google merge of over 50 million InChIKey(s) from
PubChem, ChemSpider and UniChem, ChEMBL containing SAR for 0.8 million structures
from 50K medicinal chemistry papers, over 20 million abstracts in PubMed, and full-text
open patent chemistry in SureChemOpen bringing PubChem patent-extracted structures to
15 million. In addition, options such as Open Lab Books and figshare are expanding the
choices for surfacing new structures. Methods will be outlined for establishing document-to-
document and document-to-database links via chemical structures. These include the
PubChem toolbox, protein targets in UniProt, PubChem BioAssay, ChEMBL indexing in UK
PMC, SureChemOpen, chemicalize.org for text name-to-structure conversion , OSRA for
image-to-structure conversion, Venny for set comparisons and InChIKey searching in
Google [1]. Combined use of these approaches to make joins between patents, papers,
abstracts chemical database entries, SAR data and drug target protein sequences will be
illustrated with recent novel antimalarial lead compounds, patent-only BACE2 inhibitors and
company code numbers in the NCATS repurposing list.

3. [3]
The Chem < - > Bio Join
• Chemistry that does something: drug discovery, drug development,
toxicology, pharmacology, systems chemical biology (probes), structural
biology, metabolomics, chemical ecology, etc etc ….
• With the exception of some PubChem Bioassays, the majority of data is sill
primarily archived in documents

4. [4]
Getting chemistry out of text is difficult

5. [5]
That’s why we used to have to pay
73 million
4,059,232
5.1 million
~ 20,000

6. [6]
The Chemical Representational Hextet:
Different usage between documents and databases
?

7. [7]
A recent NRDD article
• Just images and code numbers
• No PubChem or ChemSpider IDs
• No SMILES or InChIs
• No molfiles for download
• No links in or out
• No MeSH > PubChem substances
• Some cited sources might have IUPAC names

8. [8]
You can dig out structures from text for free:
- but its hard work

9. [9]
What’s out there for free
• InChIKey in Google ~ 50 million
• PubChem = 48 million
• PubChem ROF + 250-800 Mw (lead-like) = 31 million
• ChemSpider = 28 million
• PubChem all docs (papers & patents) = 16 million
• PubChem patents = 15 million
• SureChemOpen = 14.5 million
• PubChem journal sources (PubMed + ChEMBL) = 1 million

10. [10]
Medicinal chemistry patents (tombs with lids off)
• WO, C07D = 72,737 (assignee vs. year plots below)
• ~ 50 novel structures with SAR per patent = ~ 3.5 million bioactives
• Paradoxically now completely open for chemistry or any mining

11. [11]
PubMed: ~ 10% with chemistry (guarded tombs)
“Free full text” = 575,513
(24%)

12. [12]
Growth:
(escaping the
tombs)
• Patent “big bang”
(SureChem &
SCRIPDB in
2012)
• Literature “slow
burn” (ChEMBL
2009 jump)
• Paradox -
patents:papers
15:1
(both sets of CIDs
cumulative)

13. [13]
Databases <> structures < > documents:
links, but few reciprocal
Abstracts
Patents
Papers
15 mill
0.2 mill (mainly MeSH)
0.8 mill
(ChEMBL)
12K

14. [14]
Triaging document or webpage chemistry
• Identify the structure specification types, e.g.
– Semantic names (all sources)
– Code names (press releases, papers and abstracts)
– IUPAC names (papers, patents and abstracts)
– Images (papers, patents, & Google images)
– SMILES (open lab books)
– InChi strings (open lab books)
– SDF files (open lab books, & github)
Convert these to a structure (e.g. SDF, SMILES, InChI) then:
– Search InChIKey in Google
– Search major databases
– Search SureChemOpen
– Compare extracted sets for intersects and diffs
– Extend exact match connectivity with similarity searching

15. [15]
Triage example: a
new antimalaria
The MMV390048 code
name is linked to an
image in press reports
but is PubChem and
PubMed -ve

16. [16]
Images: convert and search
Real chemists sketch them in a jiffy;
the rest of us can use OSRA: Optical Structure Recognition Application
(after editing, CS(=O)(=O)c3ccc(C2=CN=C(N)C(C1=CCC(C(F)(F)F)N=C1)C2)cc3)

17. [17]
Making connections:
image > strucure > database > documents
CID 53311393 > ChEMBL > PubMed
SureChem or chemicalize.org > patent

18. [18]
Patent SAR from WO2011086531:
Collating activities via SureChemOpen
CID 53311393 >

19. [19]
Patent SAR results: top-20 from 39 IC50s

20. [20]
Results > figshare
http://figshare.com/articles/Patent_SAR_for_MMV390048/657979

21. [21]
Structures > MyNCBI
http://www.ncbi.nlm.nih.gov/sites/myncbi/collections/public/1zWhcobieZ
bIouGfUdsdbHek5/.

22. [22]
SAR Table: iOS app
from Molecular
Materials
Informatics
SureChemOpen strucs ->
manual data collation ->
PubChem CIDs -> SDF ->
Dropbox -> SAR Table
-> edit in data, R-group
decompose
-> share

23. [23]
InChIKey in Google: instant orthogonal joining

24. [24]
Chemicalize.org: 413 strucs from WO2011086531
CID 53311393 ->

25. [25]
Using OPSIN and chemcalize.org to fix
recalcitrant IUPACs from WO2011086532
Can quasi-manually extract ~ 10 more “split IUPAC” examples

26. [26]
Clustering document extraction sets: CheS-Mapper
WO2011086531 -> chemicalize.org -> 413 cpds download ->
CheS-Mapper -> cluster 8 -> export 53 cpds

27. [27]
PubChem -> ChEMBL -> PMID -> assay -> strucs
• CHEMBL2041980 (structure)
• PMID 22390538 (paper)
• CHEMBL2045642 (assay for 32 strucs
from paper)
• The 32 CIDs all have patent matches

28. [28]
Venny: intersects, diffs, de-dupes and merges
1) WO2011086531
matches in PubCHem
2) CheS-Mapper
cluster 8 from
WO2011086532
3) ChEMBL assayed
cpds from PMID
22390538
(handles any regular
strings e.g. db IDs,
SMILES, IChI or
InChIKey)

29. [29]

30. [30]
NCATS/MRC: the joy of codes with no structures
http://cdsouthan.blogspot.se/2012/09/mrc-22-vs-ncats-58-repurposing-lists.html

31. [31]
Code name-to-structure mapping:
Dig out the code names
PubChem Substance
PubChem Compound
PubMed/MeSH
Google Scholar
Google Images
Google open (filtered)

32. [32]
Sometimes the system works

33. [33]
PubMed > ChEMBL

34. [34]
Sometimes you get missing and cryptic links

35. [35]
NVP-Bxd552: Google results

36. [36]
BACE2: Almost no chemistry in papers

37. [37]
BACE2
1. WO2013054291 > chemicalize.org
2. Download 450 structures
3. Upload to PubChem search

38. [38]
Scibite > Alerts for new chemistry

39. [39]
Conclusions
• The ability to extract chemical structures from text and web sources
has been transformed by an expansion of the public toolbox
• The PubChem big-bang increases probability of extraction having
database exact or similarity matches
• Paradoxically, the patent corpus is now completely open while access
to journal text is still restricted
• However, ChEMBL has extracted ~ 0.8 mill. SAR-linked and target
mapped structures from ~ 50K papers
• The submission of ~15 mill. patent structures to PubChem ensures at
least representation from the majority of medicinal chemistry patents
(many of which spawned the subsequent ChEMBL papers)
• Those who want to share their structures globally (e.g. OSDD) have an
expanding set of options for surfacing their results.

40. [40]
References