Christopher Southan (The IUPHAR/BPS Guide to PHARMACOLOGY, UK) As of August 2017, the major automated patent chemistry extractions (in ascending size, NextMove, SCRIPDB, IBM and SureChEMBL) are included submitters for 21.5 million CIDs from the PubChem total of 93.8. The following aspects will be expanded in this presentation, starting with advantages; a) while the relative coverage between open and commercial sources is difficult to determine (PMID 26457120) it is clear that the majority of patent-exemplified structures of medicinal chemistry interest (i.e. from C07 plus A61) are now in PubChem b) this allows most first-filings of lead series and clinical candidates to be tracked d) the PubChem tool box has query, analysis, clustering and linking features difficult to match in commercial sources, e) many structures can be associated with bioactivity data f) connections between manually curated papers and patents can be made via the 0.48 million CID intersects with ChEMBL. However, looking more closely also indicates disadvantages; a) extraction coverage is compromised by dense image tables and poor OCR quality of WO documents, b) SureChEMBL is the only major open pipeline continuously running in situ but has a PubChem updating lag, c) automated extraction generates structural “noise” that degrades chemistry quality d) PubChem patent document metadata indexing is patchy (although better for SureChEMBL in situ) d) nothing in the records indicateas IP status, e) continual re-extraction of common chemistry results in over-mapping (e.g. 126,949 patents for aspirin and 14,294 for atorvastatin), f) authentic compounds are contaminated with spurious mixtures and never-made virtuals, including 1000s of deuterated drugs g) linking between assay data and targets is still a manual exercise. However, all things considered the PubChem patent “big bang” presents users with the best of both worlds (PMID 26194581). Academics or smaller enterprises who cannot afford commercial solutions can now patent mine extensively. For those who have such subscriptions, PubChem has become an essential adjunct/complementary source for the analysis of patent chemistry and associated bio entities such as diseases and drug targets.

1. www.guidetopharmacology.org
Looking at the gift horse: pros and cons of patent-
extracted structures in PubChem
Christopher Southan, IUPHAR/BPS Guide to PHARMACOLOGY, Centre for Integrative
Physiology, University of Edinburgh. ICIC Heidelberg, Monday 23rd Oct 2017
https://www.slideshare.net/secret/v4A5eUTuYvT28X
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22 million

2. Abstract (will be skipped for the presentation)
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As of August 2017, the major automated patent chemistry extractions (in ascending size,
NextMove, SCRIPDB, IBM and SureChEMBL) are included submitters for 21.5 million CIDs from
the PubChem total of 93.8. The following aspects will be expanded in this presentation, starting
with advantages; a) while the relative coverage between open and commercial sources is difficult
to determine (PMID 26457120) it is clear that the majority of patent-exemplified structures of
medicinal chemistry interest (i.e. from C07 plus A61) are now in PubChem b) this allows most
first-filings of lead series and clinical candidates to be tracked d) the PubChem tool box has
query, analysis, clustering and linking features difficult to match in commercial sources, e) many
structures can be associated with bioactivity data f) connections between manually curated
papers and patents can be made via the 0.48 million CID intersects with ChEMBL. However,
looking more closely also indicates disadvantages; a) extraction coverage is compromised by
dense image tables and poor OCR quality of WO documents, b) SureChEMBL is the only major
open pipeline continuously running in situ but has a PubChem updating lag, c) automated
extraction generates structural “noise” that degrades chemistry quality d) PubChem patent
document metadata indexing is patchy (although better for SureChEMBL in situ) d) nothing in the
records indicates IP status, e) continual re-extraction of common chemistry results in over-
mapping (e.g. 126,949 patents for aspirin and 14,294 for atorvastatin), f) authentic compounds
are contaminated with spurious mixtures and never-made virtuals, including 1000s of deuterated
drugs g) linking between assay data and targets is still a manual exercise. However, all things
considered the PubChem patent “big bang” presents users with the best of both worlds (PMID
26194581). Academics or smaller enterprises who cannot afford commercial solutions can now
patent mine extensively. Even for those with commercial subscriptions, PubChem has become
an essential adjunct/complementary source for the analysis of patent chemistry and associated
bio entities such as diseases and drug targets.

3. Outline
• History of patent chemistry feeds to PubChem
• Relative source contributions
• Caveats with automated extraction
• Source intersects
• Fragmentation
• Source extraction comparisons
• Circularity for virtuals
• Mixtures
• Lag times
• Conclusions
• References
• Workshop alert
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4. 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
• As well as patents, IBM run PubMed abstracts and PMC
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5. History of patent chemistry feeds into PubChem
• 2006 Thomson (now Clavariat) Pharma, manual extractions from patents
and papers, 4.3 mil (but ceased Jan 2016)
• 2011 IBM phase 1 Chemical Named Entity Recognition (CNER) 2.5 mil
• SLING Consortium EPO extraction 0.1 mil
• 2012 SCRIPDB, CNER + Complex Work Units (CWU) 4.0 mil
• 2013 SureChem, CNER + image, 9.0 mil
• 2014 BindingDB manual activity curation 0.13 mill
• 2015 (CNER+images + CWU)
• SureChEMBL 13.0 mil
• IBM phase 2, 7.0 mil,
• NextMove Software 1.4 mil synthesis mapping
• 2016 SureChEMBL 15.8 mil
• 2017 IBM Phase 3, 6.0 mill
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6. 2011 “fizzle” > 2015 “big bang”
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7. Pro: Oct 2017, from 93.89 mill PubChem CIDs
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8. Pro: PubChem indexes IPC splits
Con: document indexing is USPTO
dominated (i.e. early WO’s missed)
Con: Entrez cant handle the joins
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9. Con: Mw plots reveal CNER fragmentation
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ChEMBL + Thomson
Pharma = 5.6 million
manual extraction
Patent CNER
= 21.8 million

10. Con: those “Chessbordanes” still hanging around……
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11. Pros & cons arising from intersects and filters
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12. Intersects and diffs for major CNER sources
Pro: corroboration, Con: divergence
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IBM = 10.7
SCRIPDB = 4.0
SureChEMBL = 17.6
2.9
2.4
4.7 10.1
0.6 0.4
0.50
Counts (Oct 2017)
are CIDs in millions
Union = 21.7
3-way = 2.4
3 + 2-way = 8.1
Unique= 13.5

13. Con: circular extraction of virtual enumerations
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1511 codeine
records, mainly 563
deuterations from
Auspex US7872013
> 3-source
multiplexing
652 InChI key inner
layer records via 266
stereos of vorapaxar
via Schering
US20080085923 >
4-source multiplexing
in UniChem

14. Pro: good coverage, con: not complete
• Compared SureChEMBL and IBM with SciFinder and Reaxys for a small
patent set (i.e. open vs commercial)
• Concluded; “50–66 % of the relevant content from the latter was also
found in the former”
• Equivalent comparisons in the latest PubChem would record a higher overlap
• Probability of completely missing a recently exemplified series completely
getting lower
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Managing expectations: assessment of chemistry databases generated by
automated extraction of chemical structures from patents, Senger, et al. J.
Cheminf. 2015, 7:49 doi:10.1186/s13321-015-0097-z (GSK and SureChEMBL)
http://www.ncbi.nlm.nih.gov/pubmed/26457120

15. Examining extraction
selectivity for same patent
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16. Coverage from US9181236
Pro: convergence, Con: divergence
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• 173 BindingDB CIDs
curated from PubChem via
US9181236
• 405 substances SDF from
SciFinder OpenBabel > 391
IK > 362 CIDs
• 1657 rows > 834
SureChEMBL IDs > 664
CIDs
• 3-way Venn of CIDs

17. Con: the common chemistry problem
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Spurious patent < > cpd indexing: aspirin = 131,410, atorvastatin = 14,968,
ethanol = 72,027

18. Con: the mixtures problem
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19. Con: no open automated SAR extraction
Pro: DIY manual SAR extraction aligned to PubChem structures
Pro: ~2K patents have target-mapped BindingDB curated SAR
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• SAR table from WO2016096979, Jansen BACE1 inhibitors
• Left to right, page from the PDF, SureChEMBL mark-up and Excel paste-across

20. Con: Lag in SureChEMBL> PubChem synch times
• Internal UniChem load at EBI, 10 Oct = 18691416
• PubChem submission, 07 Oct = 17687607
• Latest in situ entries below for 12 Oct
• Extraction in SureChEMBL within a week or less of pub date
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21. Con: IBM CNER > 80% of all PubChem < > PMID links
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• IBM extracts PubMed abstracts as
well as patents
• PubChem < > structures to PMID
• Automated associations swamp
out expert-curated assignments
• Specificity/accuracy is equivocal

22. Conclusions
• For the PubChem patent chemistry “Big Bang” the pros massively outweigh
the cons (i.e. it’s not a bad horse …)
• Contributors are to be congratulated and PubChem for wrangling them
• However, it is important to look closely at the gift horse…..
• Users need to understand CNER quirks, pitfalls and confounding artefacts
• PubChem slicing and filtering can partially ameliorate these
• Activity-to-target mapping for SAR extraction still pinch point
• Open extraction is a crucial comparator for commercial efforts
• Those without commercial sources are well enabled for patent mining
• Those with commercial sources can synergise with open searching
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23. Info
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http://cdsouthan.blogspot.com/ many posts have the tag “patents”
http://www.ncbi.nlm.nih.gov/pubmed/26194581
http://www.guidetopharmacology.org/
http://www.sciencedirect.com/science/article/pii/B9780124095472138144

24. Questions? (but wait …. there’s more, a Tuesday tutorial)
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