My presentation for the Drug Repurposing workshop at the upcoming Bio-IT World Expo. http://www.bio-itworldexpo.com/Bio-It_Expo_Content.aspx?id=124256 Presentation abstract: PubChem has a wealth of chemical structure and biological activity information. In conjunction with NCBI’s other resources such as PubMed and GenBank, PubChem is a vast source of information relevant to repurposing not only of established drugs but any compounds with in vivo pharmacology and/or clinical results. The challenge is how to take advantage of this knowledge. The ability to explore not only chemical similarity but relationships between diseases and disease targets has crucial value in repurposing. While focused investigations are already possible within the existing Entrez system, navigation across these linked information spaces can be difficult to do on a large scale with current tools. We are actively developing new infrastructure to support such analyses, and pursuing new methods of exploring inter- and intra-database relationships between chemicals, targets, diseases, and patents. Progress and some future direction in these areas will be presented.

1. Dr. Paul A. Thiessen, NCBI
2013/03/21 draft

2. What is a “Knowledge Space”?
 May be a database
 But may be a concept not encapsulated
in a database
Genes Diseases
Literature
(PubMed) Chemicals
(PubChem)
Assays Targets
Patents (PubChem) (sequences)
Drugs

3. Connecting the Spaces
 Database cross-links
Assays
(PubChem)
Literature
(PubMed)
Active
MeSH
Inactive
Targets
Depositor (sequences)
Chemicals
(PubChem)

4. Moving Within a Space
 Neighbors… some examples
Same Similar sets
parent of screened
Similar Assays chemicals
by 2D (PubChem)
or 3D Chemicals
(PubChem)
Similar
target
Same (BLAST)
connectivity

5. Drug Repurposing as a Spatial
Transformation
 One possible route…
Search Diseases
Drugs
(known)
Similarity
Diseases Targets
(hypothesized)

6. What is in PubChem
 117M Substances (SIDs)
 Information from depositors, including links
to PubMed, sequences, structures, patents,
etc.
 47M Compounds (CIDs)
 Derived from Substances (including links)
 Computed properties
 650k Assays (AIDs)
 ~200M test results on SIDs
 Links to target sequences

7. Some PubChem Statistics
 All CIDs 46,814,409
 Unique parents by connectivity 36,806,372
 Rule of 5 34,343,056
 Rule of 5 but MW 250-800 31,483,865
 Active in any BioAssay 824,028
 Tested in any BioAssay 1,872,313
 Experimental 3D (mainly PDB) 41,406
 Computed 3D (multiple confs + neighbors) 42,252,570
 Pharmacological Actions 11,531
 Biosystems 9,703
 Chemical vendors 28,852,943
 NIH Molecular Libraries 402,076
 Patent sources 14,512,499
 Patent links 5,978,538
… as of 2013/03/20

8. What is in NCBI Entrez
 Many other databases…
 PubMed
 Protein/Nucleotide sequences
 Genes
 Biosystems (metabolic pathways)
 PDB structures (with VAST neighbors)
 Text and numeric search fields
 Cross-links
 Between databases
 Within databases (neighbors)

9. How Entrez Works
 Search results = list of identifiers
 Boolean operations on lists (query
refinement)
 Links from one database to another
PubChem CID
Search List
Link
PMID
to PubMed List
PubChem CID
Search List

10. Limitations of Entrez
 Only text or numeric search
 Search fields hard to discover
 Search fields and defaults vary by database
 Chemical structure search, and other
specialized algorithms, must be done
outside Entrez
 The kicker: links are incomplete
 Only 500-10,000 ids!
 Limit also varies by database

11. Working Around the Limitations
 Scripting
 E-Utils, PUG SOAP/REST, etc.
 Break queries into smaller chunks
 Specialized services
 PubChem’s ID Exchange
 Classification trees (with associated IDs)

12. What is not in Entrez
 … as a database per se, but which may
be imported and linked to PubChem
 Drugs
 (sort of but not really)
 Targets
 (again sort of)
 Diseases
 Patents

13. Some Public Sources of Information
Relevant to Drugs and Repurposing
 United States (FDA, NLM, NCBI, …)
 ClinicalTrials.gov
 NDF(-RT)
 RxNorm
 HSDB
 MeSH
 DailyMed
 PubMed, PubMed Health
 USPTO
 Europe
 ChEBI / ChEMBL
 EPO / WIPO
 Canada
 DrugBank
 Japan
 KEGG
… not an exhaustive list
… some are linked to PubChem
… some are works in progress

14. MeSH and ChEBI
 Chemical
structure
classification
 Biological role
 Pharmacological
action

15. KEGG and DrugBank
 Drug
classification
 Targets

16. Patents
 PubChem depositors
 Per SID:
○ Patent IDs
○ PubMed IDs
 Classifications
 ECLA
 IPC
 USPC
 CPC

17. Aside: Patent Summaries

18. NDF-RT
 Molecular interactions
 Drug ingredients
 Diseases (with drugs)
 Physiological effects
 Has links to MeSH
 … which leads to CIDs

19. NDF-RT linked to SID, CID

20. Classifications as Navigation
Tools
 Where are the CIDs in the tree?
• Example: chemicals
affecting serotonin
transporters
according to KEGG

21. Classifications for Query
Refinement
 Where are MY CIDs in the tree?
• Example: what
diseases are linked
by NDF to KEGG’s
serotonin transport
drugs?

22. Big Classifications…
Some Engineering
Required
WIPO IPC
• 72,000 tree
nodes
• 6,000,000 CIDs
• 124,000,000
node-CID links
Filtering on the fly:
• 22,000 CIDs
from PDB
… interactive!

23. More Space to Explore
Genes
Literature Assays
(PubMed) (PubChem)
Chemicals
(PubChem)
Targets
Patents (sequences)
Drugs Diseases … and beyond

24. Conclusions
 PubChem is…
 A very generalized system
 Based on open data
 Part of the larger Entrez collection
 We strive to…
 Make analysis across multiple knowledge
spaces accessible and powerful
 Enable hypothesis generation for drug
repurposing (as one scenario among many)
 Feedback is always welcome!
 info@ncbi.nlm.nih.gov

25. Acknowledgements
 Evan Bolton
 Steve Bryant
 Asta Gindulyte (classification front end)
 Chris Southan

… Thank You!