This document summarizes a presentation about analyzing over 20 million chemical structures extracted from patents that are publicly available. It discusses both the benefits and limitations of automated chemical structure extraction from patents (chemical named entity recognition, or CNER) as well as opportunities to supplement CNER with manual curation. While CNER has made a huge amount of previously private medicinal chemistry data public, detailed analysis shows there are also errors and caveats to be aware of, such as extracted mixtures, duplicates, and lacking bioactivity information. With an understanding of the sources and limitations, the data still provides great value for compound design and other applications.

1. www.guidetopharmacology.org
20 million public patent-extracted chemical
structures: a look at the gift horse
Christopher Southan, IUPHAR/BPS Guide to PHARMACOLOGY,
Centre for Integrative Physiology, University of Edinburgh
http://www.guidetopharmacology.org/index.jsp
Prepared for Global Health Compound Design webinar, 30th Nov
Recording should become available below
http://www.mmv.org/research-development/computational-chemistry/global-health-compound-
design-webinars
http://www.slideshare.net/cdsouthan/20-mill-public-patent-structures-looking-at-the-gift-horse
1

2. Outline
• Good and bad news about chemistry from patens
• Chemical Named Entity Recognition, pros and cons
• Major submitters to PubChem
• New WIPO initiative
• Overlaps between sources
• Examples of CNER caveats
• Roll your own extractions
• Curated activity-to-target mappings
• MMV example
• Conclusions
• References
2

3. Looking at informatics gift horses
• We will look at just patent chemistry here
• But any source repays detailed analysis
• What are the statistics of entity and relationship capture?
• Can we assess real-world comparative utility?
• No source is free of caveats, overlaps, complexities, quirks and errors
• So can we ameliorate these during exploitation?
• PubChem submitters can be sliced, diced and compared in detail
• Public sources welcome feedback but may not have resources to implement
• The example below shows the analysis of four “horses” at once
3

4. Medicinal chemistry from patents:
good news, part I
• This presentation will focus on bioactivity value, not IP assessments (but I
can try to address IP-related questions)
• Patents are a Cinderella scientific data source with underestimated utility by
academics
• They typically publish between two-to-five years before a paper with some
of the same examples
• They may contain anywhere between 2x to 10x the amount of SAR than an
eventual paper
• For some filings from world-class medicinal chemistry teams, (academic or
commercial) the SAR never appears anywhere else
4

5. Good news, part II
• Paradoxically, documents are more “open” than papers (e.g. for text mining)
• The non-redundant primary med. chem. data corpus (first-filings with
composition of matter, classified as C07+A61) is well below 100K
• Examiners search reports and inventivness assessments are public
• Citations of papers and other patents usually extensive
• Massive synthetic protocol and analytical data archive
• Estimated total bioactive compounds ~ 4- 6 million
• A treasure trove for compound design, chemical property extraction (see
slides from previous speaker, Igor Tetko) and many other uses
5

6. Bad news: part I
• Data mining is more difficult than for papers
• Access historically dominated by commercial products
• Need to engage with quirks of patent family redundancy, Kind Codes,
patent classifications, 100s pages of turgid legal text, Markush nests
• Major portals pushing towards 50 million documents
• Some applicants are guilty of varying degrees of obfuscation to make data
mining more difficult (e.g. the “Novel Compound” titles)
• What gets into public databases are not patented structures, merely
structures extracted from patents
6

7. Bad news: part II
• Finding first-filings can be difficult
• Judging data quality is a challenge
• Few journal authors cite their patents
• A large proportion of SAR data is “binned” rather than discrete values
• Some applicants don’t declare data values at all
• From public extractions so far, the proportion of bioactive examples:
“other” (including non-med. chem. and artefacts) is ~ 5:15 million
• Comparing sources indicates constitutive divergence of extraction
• Automated extraction has inadvertently contaminated public databases
with a variety of artefactual structures, running into millions
7

8. Chemical Named Entity Recognition (CNER)
• Automated process of documents in > structures out
• SureChEMBL pipeline shown above, other sources similar
• Name-to-Struc (n2s) by look-up and/or IUPAC translation, image-to-
struc (i2s) and mol files from USPTO Complex Work Units (CWUs)
• Indexing usually added e.g. abstract, descriptions, claims
8

9. History of patent chemistry feeds into PubChem
• 2006 -Thomson (Reuters) Pharma (TRP) manual extraction of patents
and papers, 2016 4.3 mil ~40% patents, guess ~1.5 mill – now static :)
• 2011- IBM phase 1 CNER 2.5 mil
- SLING Consortium EPO extraction 0.1 mil (static)
• 2012 - SCRIPDB, CNER 4.0 mil (static)
• 2013 - SureChem, CNER 9.0 mil (> SureChEMBL)
• 2014 - BindingDB USPTO manual assay mapping 0.1 mil (active)
• 2015- CNER
• SureChEMBL 13.0 mil (active)
• IBM phase 2, 7.0 mil, (static)
• NextMove Software 1.4 mil synthesis mapping (static)
• 2016 (Nov) all large sources above = 19.46 mill + ~ 1.5 mill Thomson
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10. CNER: good news and bad news
• SureChEMBL is the major contribution to public patent chemistry by far
• 17.51 million cpds in UniChem on 22 Nov
• 16.25 million in PubChem up to August
• 8.43 million are novel (i.e. source-uniqe CIDs)
• In situ chemistry is indexed and downloadable within days of publication
• Complemented by SciBites automated “bio-entity” indexing (on the fly)
• Powerful query interface
• UniChem cross-indexing (e.g. to PubChem and/or ChEMBL)
But
• SurChEMBL remains the only active CNER source – others are static
• Current feed hiccups are being addressed
• Extraction performance compromised by poor OCR quality in WO
documents and instances of very dense image tables
• Some types of CNER artefacts are introduced in subsequent slides
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11. Major PubChem CNER patent sources at the CID level:
corroboration but also divergence
11
SCRIPDB = 4.0
(SID:CID 1.5)
IBM = 7.9
(SID:CID 1.2)
SureChEMBL = 14.6
(SID:CID 1.0)
0.66
2.12
0.67 8.56
0.53 3.26
1.95
Compound Identifiers (CIDs)
in millions with a union of
17.8 (in 2015)

12. Patent CNER vs. manual bioactivity sources in PubChem:
corroboration along with (expected) divergence
12
SCRIPDB + IBM
+ SureChEMBL = 17.8
Thomson (Reuters) Pharma = 4.3
ChEMBL = 1.4
16.13
0.18
0.12 0.90
1.35 0.26
2.55Counts (2015)
are CIDs in millions

13. A “new horse” (Oct 2016)
13
• ~ 7 million structures so far from WO and US from 1978
• WIPO collaboration with InfoChem and NextMove

14. CNER fragmentation
14
• Mainly split IUPAC strings but some authentic intermediates
• Compare with selective manual extraction by Thomson/Derwent

15. Bioactivity-gap: most patent chemistry has no linked data
15
Comparing the total
CNER patent set with
a bioactivity-centric
source e.g. Guide to
PHARMACOLOGY
(GtoPdb) at 6037
CIDs (2015 numbers)

16. Patent-unique structures: strange big things
16
https://www.blogger.com/blogger.g?blogID=2155351992730855318#editor/target=post;postID=89592136438562
00429;onPublishedMenu=allposts;onClosedMenu=allposts;postNum=2;src=postname post on “chessbordane”

17. Mixtures from patents: more confounding than useful
17
PubChem ameliorates the issue by splitting SID mixtures to component CIDs
while maintaining the mappings

18. Continual re-extraction of common chemistry
18

19. US6589997: missing punctuation > CNER > mixtures
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20. Virtuals I: stereo enumerations from US 20080085923
20
260 CIDs > 581 SIDs from IBM,
SureChEMBL, SCRIPDB, Thomson
Pharma and Discovery Gate

21. Virtuals II: deuterated enumerations from US20080045558
21
986 deuterated CIDs > 2818
SIDs from IBM, SureChEMBL
and SCRIPDB,
http://www.slideshare.net/cdsouthan/causes-and-consequences-of-automated-extraction-of-
patentspecified-virtual-deuterated-drugs

22. Some good news: supplementing CNER with DIY extraction
Either for unprocessed patent documents (e.g. on publication day) or where
the extraction of examples by CNER is clearly gapped
22

23. More good news:
expert activity-to-target patent mapping complements CNER
23

24. Expert activity-to-target mapping II
24
http://www.guidetopharmacology.org/GRAC/ObjectDisplayForward?objectId=2331

25. Utility example from MMV
25
Pick up from the
SureChEMBL interface
with MMV as applicant
or C07 + malaria

26. Following through:
SureChEMBL > PubChem
26
• CID > “similar compounds” (Tanimoto
90% neighbours) 58 CIDs > cluster
• Generally picks out analogue series
from same patent (i.e. the 118s)
• But note structures from other
sources nesting into the cluster
(e.g. 426, 509, 920, 280 and 308)

27. Conclusions
• The open patent chemistry “Big Bang” value massively outweighs the
caveats (i.e. it’s a very nice horse - thanks…)
• The majority of med. chem. exemplifications are now out there
• All contributing sources are to be congratulated, and PubChem for
wrangling most of them
• But, it is important to look closely at the gift horse
• We can then resolve and understand quirks, artefacts and pitfalls
• PubChem slicing and filtering can partially ameliorate these
• Activity-to-target mapping for SAR extraction is the main pinch point
• Those without commercial sources are now more enabled for patent mining
• Those with commercial sources can now synergise with open ones
27

28. References
28
http://cdsouthan.blogspot.com/ 19 posts have the tag “patents”
http://www.ncbi.nlm.nih.gov/pubmed/26194581 http://www.ncbi.nlm.nih.gov/pubmed/23506624
N.b. from the reproducibility aspect, anyone needing technical tips to
reproduce or extend the PubChem queries used for these slides is welcome
to contact me
www.ncbi.nlm.nih.gov/pubmed/25415348 //nar.oxfordjournals.org/content/early/2015/10/11/nar.gkv1037
Southan C: Examples of SAR-centric patent mining using open resources, in Elsevier
COMPREHENSIVE MEDICINAL CHEMISTRY III, July 2017, in press