Navigating between patents, papers, abstracts and databases using public sources and tools

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1 Navigating between patents, papers, abstracts and databases using public sources and tools Christopher Southan 1 and Sean Ekins 2 TW2Informatics, Göteborg, Sweden, Collaborative Drug Discovery, North Carolina, USA ACS, April 2013 [1]

2 [2]

3 ACS Abstract Engaging with chemistry in the biosciences requires navigation between journals, patents, abstracts, databases, Google results and connecting across millions of structures specified only in text. The ability to do this in public sources has been revolutionised by several trends a) ChEMBL's capture of SAR from journals c) the deposition of three major automated patent extractions (SureChem, IBM and SCRIPDB) in PubChem for over 15 million structures, d) open tools such as chemicalize.org, OPSIN, and OSCAR that enable the conversion of IUPAC names or images to structures e) the indexing of chemical terms (e.g. InChIKeys) that turn Google searches into a merged global repository of 40 to 50 million structures. Details of these trends, including PubChem intersect statistics, will be presented, along with practical examples from selected tools. New structure sharing trends will also be considered such as patent crowdsourcing, dropbox, blogs, figshare and open lab notebooks. [3]

4 Getting chemistry out of text and linking to data: some is done but we have to dig for the rest [4]

5 Estimates for chemical text tombs Journal chemistry public extraction, ~10 to 20 million entombed? Majority of useful patent chemistry already publically extracted, but, ~5 to 10 million still to go? PubMed abstracts and MeSH chemistry ~ 0.5 million still entombed? Other unique, useful, text-only (i.e. no database cross-references) chemistry on the web ~ 0.1 to 0.5 million entombed? [5]

6 What s out there: publically disinterred structures InChIKey in Google ~ 50 million PubChem = 48 million PubChem ROF Mw (lead-like) = 31 million ChemSpider = 28 million PubChem all docs (papers & patents) = 16 million PubChem patents = 15 million SureChemOpen = 13 million PubChem journal sources (PubMed + ChEMBL) = 1 million ~90% of all structures in databases have their primary origin in text sources [6]

Medicinal chemistry patents (tombs with lids off) 18,777,229 patents, 2,208,422 WO s (i.e. ~ 9 per family) WO, C07 or A61= 469,856 WO, C07D or A61K = 235,854 WO, C07D = 72,737 (assignee vs.

7 Medicinal chemistry patents (tombs with lids off) 18,777,229 patents, 2,208,422 WO s (i.e. ~ 9 per family) WO, C07 or A61= 469,856 WO, C07D or A61K = 235,854 WO, C07D = 72,737 (assignee vs. year plots below) [7]

8 PubMed at 22 mill: ~ 10% with chemistry (guarded tombs) Free full text = 575,513 (24%) [8]

9 Top-5 Med Chem journals (4% lids off tombs) Free full text = 2671 (4.3%) [9]

10 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) [10]

11 Patents in PubChem: post-bang total vs. unique content PubChem at 47.3 million CIDs, 32% include patents, 20% patent-only [11]

12 Citations: connections between tombs but still need to disinter structures Papers Abstracts Patents PubMed "relatedness" heuristics [12]

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

14 Post-document retrieval: basic questions 1. What is the name:iupac:image:other ratio in the document? 2. Which tools might be appropriate for first-pass extractions? 3. How many and what proportion of strucs can be extracted? 4. Which SAR /in vivo/clinical data is linked to strucs? 5. Which document sections include the key strucs? 6. Which database entries have links (back) to this document? 7. Which strucs have InChIKey matches in Google, & database entries? 8. Which strucs have synthesis data? 9. What other documents specify and/or cite this struc? 10. Which database records for this struc have links to other documents? 11. What realtionship connections can be made using similarity searches? 12. What intersects and differences are discernible within a document set? [14]

15 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]

Triage example: antimalarial starting point The MMV390048 code name is

16 Triage example: antimalarial starting point The MMV code name is linked to an image in press reports but is PubChem and PubMed -ve [16]

17 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]

18 Making connections: image > strucure > database > documents CID > ChEMBL > PubMed SureChem or chemicalize.org > patent [18]

19 Patent SAR from WO : Collating activities via SureChemOpen CID > [19]

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

21 Results > figshare [21]

Structures > MyNCBI http://www.ncbi.nlm.nih.

22 Structures > MyNCBI biougfudsdbhek5/. [22]

23 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]

24 InChIKey in Google: instant orthogonal joining [24]

25 Chemicalize.org: 413 strucs from WO CID > [25]

26 Using OPSIN and chemcalize.org to fix recalcitrant IUPACs from WO Can quasi-manually extract ~ 10 more split IUPAC examples [26]

27 Clustering document extraction sets: CheS-Mapper WO > chemicalize.org -> 413 cpds download -> CheS-Mapper -> cluster 8 -> export 53 cpds [27]

28 PubChem -> ChEMBL -> PMID -> assay -> strucs CHEMBL (structure) PMID (paper) CHEMBL (assay for 32 strucs from paper) The 32 CIDs all have patent matches [28]

Venny: intersects, diffs, de-dupes and merges 1) WO2011086531 matches in PubCHem 2) CheS-Mapper cluster 8 from

29 Venny: intersects, diffs, de-dupes and merges 1) WO matches in PubCHem 2) CheS-Mapper cluster 8 from WO ) ChEMBL assayed cpds from PMID (handles any regular strings e.g. db IDs, SMILES, IChI or InChIKey) [29]

30 The open toolbox facilitates extraction and collation of 10 to 30 million structures entombed in text [30]

31 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. [31]

32 You can find CDD Booth 205 PAPER ID: PAPER TITLE: Dispensing processes profoundly impact biological assays and computational and statistical analyses April 8 th 8.35am Room 349 PAPER ID: PAPER TITLE: Enhancing High Throughput Screening For Mycobacterium tuberculosis Drug Discovery Using Bayesian Models April 9 th 1.30pm Room 353 PAPER ID: PAPER TITLE: Navigating between patents, papers, abstracts and databases using public sources and tools April 9 th 3.50pm Room 350 PAPER ID: PAPER TITLE: TB Mobile: Appifying Data on Anti-tuberculosis Molecule Targets April 10 th 8.30am Room 357 PAPER ID: PAPER TITLE: Challenges and recommendations for obtaining chemical structures of industry-provided repurposing candidates April 10 th 10.20am Room 350 PAPER ID: PAPER TITLE: Dual-event machine learning models to accelerate drug discovery April 10 th 3.05 pm Room 350 [32]

Dispensing Processes Profoundly Impact Biological, Computational and Statistical Analyses

Dispensing Processes Profoundly Impact Biological, Computational and Statistical Analyses Sean Ekins 1, Joe Olechno 2 Antony J. Williams 3 1 Collaborations in Chemistry, Fuquay Varina, NC. 2 Labcyte Inc,