Currently, analysis of large patent sets is a tedious and cumbersome work. In order to improve and speed up this process we developed a patent curation-workflow, in which relevant chemical information, such as Markush structures and chemical compound collections (e.g. exemplified structures), are extracted from a patent set and successively enriched with text-mining retrieved data in semi-automatic manner. The outputs of cheminformatic, OCR/OSR and text-mining tools are combined by means of KNIME and the joined data are finally visualized side by side with the original documents using the ChemCurator application. As well as advanced visualization capabilities ChemCurator offers essential functions for validation and manual refining of the automatically extracted chemical information. The created project specific content gives a solid information base of value to any phase of a drug discovery project.

1. Matthias Negri , PhD
Scientific Information Center
Boehringer Ingelheim Pharma GmbH & Co. KG
Chemistry-Enriched Patent Curation
semi-automatic analysis and elaboration of patents
ChemAxon UGM 2015, Budapest, 20 May 2015
Árpád Figyelmesi
ChemAxon

2. Content
1. Chemistry in patents
2. Why do we need a patent curation workflow?
3. Semi-automatic Patent Curation Workflow - Overview
4. Linked tools/technologies
5. ChemCurator (ChemCC)
6. Semi-automatic Patent Curation Workflow – Step by Step
7. Lessons learned, weak-points, limitations
8. Outlook
2Negri Matthias, ChemAxon UGM 2015

3. Chemistry in patents
Chemistry appears within diverse form in patents:
1. TEXT - IUPAC names, common names, etc
2. IMAGES - embedded within or attached to the document
3. ATTACHMENTS (MOL/CDX)
4. TABLES
– as ONE-image file (tables with chemistry and bioactivity data)
– as chemistry-only image files embedded within table tags
5. Markush Structures/Formulas with R-groups
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 Currently NO commercial solution covers all these cases
 Most of the cases are considered in the patent curation workflow
(Markush/R-group Formulas recognized and stored separately)
3Negri Matthias, ChemAxon UGM 2015

4. Why do we need a patent curation workflow?
Motivations:
1. Linked chemistry-retrieval from patents (+ chemistry as images)
2. IUPAC-enriched XML patent files  as NEW source for text-mining
3. extraction of bioactivity data/targets/diseases/… in relation to chemistry
4. Similarity/Substructure frequency in compound sets of patents
5. …
4Negri Matthias, ChemAxon UGM 2015

5. Semi-automatic Patent Curation Workflow
Overview – current state
2 parallel branches
5
I2E API KNIME – Batch indexing, text-mining and (relational) data retrieval
SLOWER & memory intensive vs BUTHigher Quality, More Control & IUPAC-enriched XML
FASTER vs LESS informative/flexible - ChemCC as the (near) future perspectiveINPUT
Negri Matthias, ChemAxon UGM 2015

6. Linked tools/technologies
1. KNIME/XPATH
2. ChemAxon ChemCurator (ChemCC)
3. Other ChemAxon tools in KNIME nodes (document2structure/d2s,
Naming, Molconverter, Structure checker, Standardizer, …)
4. Text/data-mining – Linguamatics I2E (+I2E Chemistry)
5. Optical Structure Recognition – Keymodule CLiDE Batch
6Negri Matthias, ChemAxon UGM 2015

7. Content
1. Chemistry in patents
2. Why do we need a patent curation workflow?
3. Semi-automatic Patent Curation Workflow - Overview
4. Linked tools/technologies
5. ChemCurator (ChemCC)
6. Semi-automatic Patent Curation Workflow – Step by Step
7. Lessons learned, weak-points, limitations
8. Outlook
7Negri Matthias, ChemAxon UGM 2015

8. Computer-aided chemical data extraction
 English, Chinese and Japanese N2S
 Markush Editor
 Structure Checker
 Hit visualization
 Third party OSR technologies
ChemCurator (ChemCC)
8 Árpád Figyelmesi, ChemAxon UGM 2015

9. ChemCurator (ChemCC)
Name to Structure
 Support for many nomenclatures (common, drug names, …)
 IUPAC names
 Custom dictionaries
 English (2008)
 Chinese (2013)
 Japanese (2014)
9 Árpád Figyelmesi, ChemAxon UGM 2015

10. Compound Extraction View
Compound listProject explorer
Annotated document
Selected structures
ChemCurator (ChemCC)
10

11. Markush Extraction View
Markush editor
Example structures
Annotated document
Project explorer
Selected structures
Structure checker
ChemCurator (ChemCC)
11

12. General Document Curation
Extract Markush Structures from patents
Extract specific structures
 Journal articles
 Company reports
 Patent examples
Structure extraction wizards
 Exclude fragments, chemical elements, etc.
ChemCurator (ChemCC)
12 Árpád Figyelmesi, ChemAxon UGM 2015

13. ChemCurator (ChemCC)
Integration & Information Sharing
Other ChemAxon products:
 Direct IJC schema connection
 Project sharing function
 Accessible from Plexus, IJC, etc.
Third party tools:
 Standard file formats
 Export functions
 Easily processable projects
13 Árpád Figyelmesi, ChemAxon UGM 2015

14. Content
1. Chemistry in patents
2. Why do we need a patent curation workflow?
3. Semi-automatic Patent Curation Workflow - Overview
4. Linked tools/technologies
5. ChemCurator (ChemCC)
6. Semi-automatic Patent Curation Workflow – Step by Step
7. Lessons learned, weak-points, limitations
8. Outlook
14Negri Matthias, ChemAxon UGM 2015

15. Semi-automatic Patent Curation Workflow
a) input sources and b) bibliographic data
a) Input sources
 files with patent-IDs list
 XML collection
 …
b) Retrieval of bibliographic information and attachment data
 family ID, patent references, expiration date, etc
 Attachment files MOL/CDX (US-patents only), TIF files
 ….
15Negri Matthias, ChemAxon UGM 2015

16. Semi-automatic Patent Curation Workflow
c) chemistry retrieval/extraction/filtering
1. ChemCurator branch
 data retrieval (XML, attachments) from IFI Claims Direct BI-server
 ChemCurator project creation/sharing/annotation  html output
 Chemistry extraction name2structure/document2structure  sdf output
 Generation of pre-annotated patent set stored as ChemCC projects
 Faster, but lower quality within the chemistry extraction process
16Negri Matthias, ChemAxon UGM 2015

17. 2. KNIME branch
- OCR-errors CLEAN-UP in KNIME  improved chemistry recognition
- MOL/CDX/TIF - standardizer, structure checker  filter formulas, solvents, R-groups
 Higher quality and more control in chemistry extraction process
Semi-automatic Patent Curation Workflow
c) chemistry retrieval/extraction/filtering
17Negri Matthias, ChemAxon UGM 2015

18. 2. KNIME branch
 MOL  IUPAC
 CDX  IUPAC
 TIFF (via CLiDE)  IUPAC
Semi-automatic Patent Curation Workflow
c) chemistry retrieval/extraction/filtering
18Negri Matthias, ChemAxon UGM 2015

19. Merging and Comparison of the converted chemistry
output of MOL/CDX/TIF – 2 “quality” checks
 IUPAC
 string length (different output order of chemicals
in multiple molecules image/multiMOL files
 OCR-correction (“dictionary” based)
2. KNIME - Chemistry “Normalization”
 (within KNIME) set up a relation between each TIFF/attachment file
1. to (one or more) IUPAC name(s)
2. to a position/section in the text/document
Semi-automatic Patent Curation Workflow
c) chemistry retrieval/extraction/filtering
19
Merge IUPAC Clean-Up IUPAC
If NO IUPAC  IMG-name is set
“Normalize” IUPAC names
Negri Matthias, ChemAxon UGM 2015

20. Semi-automatic Patent Curation Workflow
d) TIF/attachment replacement with IUPAC names
Chemistry present as text is recognized and extracted either via
- Textmining (I2E chemistry – d2s is working in behind) or
- Within KNIME/ChemCC using annotate/molconvert
Replacement:
<chemistry> vs IUPAC
IUPAC-enriched XML
20Negri Matthias, ChemAxon UGM 2015

21. OCR-errors in chemical names
Semi-automatic Patent Curation Workflow
d) TIF/attachment replacement with IUPAC names
TIF
CDX
MOL
This text-chunk is replaced by the IUPAC name
21Negri Matthias, ChemAxon UGM 2015

22. XPATH/XML parsing and extraction of:
 Tables
 Rows - XML tags & strings
 Entries - XML tags & strings
Semi-automatic Patent Curation Workflow
e) Bioactivity/tabular data extraction with KNIME/XPATH
22Negri Matthias, ChemAxon UGM 2015

23. IUPAC-enriched XML as source for I2E API/textmining
 indexing
 pre-defined queries
 results retrieval
 saved as SDF files (KNIME)
Semi-automatic Patent Curation Workflow
f)Text-/datamining with Linguamatics I2E via KNIME
Text-mining retrieved (chemistry-related) information
 Example Nr.
 Bioactivity data from tables
 Claims, regions where chemistry appears in patents
 Genes, diseases
23Negri Matthias, ChemAxon UGM 2015

24. 1. Example Nr. – IUPAC
Table:Image:
For comparison – chemistry in PDF:
Semi-automatic Patent Curation Workflow
f) Bioactivity Data using I2E multi-queries – 2 steps
Source: (IUPAC-enriched) XML
2. Example Nr. – Bioactivity data
24
IUPAC
Bioactivity
Example Nr.

25. Semi-automatic Patent Curation Workflow
g) Visualize data-/textmining results in ChemCC
 SDF file imported into ChemCC project + automatic mapping to existing chemistry
25Negri Matthias, ChemAxon UGM 2015

26. Lessons learned, weak-points, limitations
1. Advantages KNIME Full-Mode (MOL/CDX/TIF) vs ChemCC branch
 chemistry check/normalization – 3 input sources  improved quality
 improved chemistry recall - ALL images (incl. tables and drawings)
 More filtering options in KNIME workflow vs ChemCurator only
 IUPAC-enriched XML as new source for I2E
Advantages ChemCC vs KNIME Full-Mode (MOL/CDX/TIF)
 faster
 Image processing using CLiDE is already incorporated with naming
26Negri Matthias, ChemAxon UGM 2015

27. Lessons learned, weak-points, limitations
2. No full automation of the workflow due to lack of homogenicity in patent data (US
vs WO, EP, etc..)
 Missing attachment files
 No tables present in XML
 Error rate in chemistry recognition (OPSIN vs n2s/d2s)
 …
 NEEDS: different workflows/branches, patent-files clean-up (OCR)
3. Time & Computational Resources-consuming process
27Negri Matthias, ChemAxon UGM 2015

28. Outlook
1. KNIME Workflow
 Add new data fields to Chemicals: BI-internal codes, genes, targets, etc..
 Usage of ChemCC html output as source for textmining
 Ontology mapping
 Expand workflow by including other sources (internal PDF, literature full-text)
 Use KNIME to interconnect to BI-intern workflows, DB, etc
 chemistry-linked information in a patent-DB  improved (semantic) search
28Negri Matthias, ChemAxon UGM 2015

29. Outlook
2. ChemCurator
 Improved n2s
 New command-line functions
 Complex-phrase requests from IFI server
 Improved SDF import
 Preprocessing wizards
Árpád Figyelmesi, ChemAxon UGM 201529

30. Thank You !
Negri Matthias, ChemAxon UGM 2015 30
INPUT
Árpád Figyelmesi, ChemAxon UGM 2015