Presented at the 2016 ACS Fall Meeting in Philadelpha, session "Effectively Harnessing the World's Literature to Inform Rational Compound Design", on 8/21/16.

1. Bibliological data science
and drug discovery
Knowing the knowns*
Effectively Harnessing the World’s Literature To Inform Rational Compound Design - ACS National Meeting, Philadelphia, Aug 21-24, 2016
Jeremy J Yang
Translational Informatics Division
School of Medicine
University of New Mexico
Integrative Data Science Lab
School of Informatics & Computing
Indiana University
*phrase borrowed from Edgar Jacoby, Janssen.

2. In science, luck favors the prepared.
- Louis Pasteur
The main thing was not to . . . "foul up."
- The Right Stuff, by Tom Wolfe, about John Glenn.

3. Overview of talk
● Formulation of problem
● Resources and examples:
TIN-X, Target Importance and Novelty Explorer (&IDG)
Chem2Bio2RDF
OPDDR, Open Phenotypic Drug Discovery Resource
DrugCentral

4. Formulation of problem
● "World's Literature" redefined by online revolution
● Rational Compound Design = improving our odds
● For given research question, what are the known knowns?
● Connect the dots and weigh the evidence from global
knowledge graph.

5. TIN-X

6. TIN-X
Target Importance & Novelty Explorer
● Bibliometric application developed for Illuminating the
Druggable Genome (IDG) project
● Text mining from Novo Nordisk Center for Protein
Research (U. Copenhagen) lab of Lars Juhl Jensen.
● Algorithm and client developed at UNM (Cristian Bologa,
Daniel Cannon)
● Disease Ontology (DO) classification
● Drug Target Ontology (DTO) protein classification

7. Illuminating the Druggable Genome (IDG)
7
Knowledge Mgmt Center PI:
Tudor Oprea, MD, PhD
pharos.nih.gov

8. TIN-X
http://newdrugtargets.org

9. TIN-X

10. TIN-X
http://newdrugtargets.org

11. Target Novelty:
Fk
= 1 / Tk
● Tk
= # targets in paper (k)
● Fk
= fractional score of paper (k)
● for papers where Tk
> 0
Ni
= 1 / ∑(Fk
)
● Ni
= novelty, target (i)
● sum over papers where target (i) mentioned
Target-Disease Importance:
Fk
= 1 / (Tk
* Dk
)
● Tk
= # targets in paper (k)
● Dk
= # diseases in paper (k)
● Fk
= fractional score of paper (k)
Iij
= ∑(Fk
)
● Iij
= importance, target (i) for disease (j)
● sum over papers where both mentioned
Target Importance and Novelty Explorer (TIN-X), Daniel Cannon, Jeremy Yang, Stephen Mathias, Oleg Ursu, Subramani
Mani, Anna Waller, Stephan Schürer, Lars Juhl Jensen, Larry Sklar, Cristian Bologa, and Tudor Oprea (manuscript in
preparation).
TIN-X

12. TIN-X
Target Importance & Novelty Explorer
● Text mining is a valuable tool for monitoring literature,
filtering and ranking, and detecting trends.
● Automation can infer patterns regarding community
trends and consensus.
● Interactive visualization tools help navigate big data.
● Good big data text miners care about small data too!

13. TIN-X
Key contributors
Cristian Bologa Daniel Cannon Lars Juhl Jensen

14. Chem2Bio2RDF

15. ● 24 sources, 52
datasets, 78M triples
● Semantically linked
● Chen, B, et al, BMC
Bioinformatics (2010).
● Chen, B et al, PLoS
Comp Bio (2012).
● Fu, G et al, BMC
Bioinfo (2016).
● Related projects:
Bio2RDF, LOD
http://chem2bio2rdf.org

16. Classes:
biological
chemical
chemogenomics
literature
phenotype
systems
disease
pathway
polypharmacology
PPI
side effect
BindingDB
BindingMOAD
IU
ChEBI
ChEMBL
CTD
DCDB
DIP
DrugBank
HGNC
HPRD
KEGG
MATADOR
OMIM
PDBe
PDSP
PharmGKB
PubChem
PubMed
Reactome
SIDER
TTD
UniProt
Sources:

18. Linked Open
Data (LOD)
http://linkeddata.org/

19. Chem2Bio2RDF apps: (1) SLAP, (2) Metapaths
2012
2016

20. ● Data semantics essential for integration of
heterogeneous sources
● Strong evidence requires strong semantics
● Semantic Web Technologies common framework
enabling -- but not assuring -- community progress
● Chem2Bio2RDF v2.0 to leverage major community
advances (esp. Open PHACTS)
● Data ecosystems, coop-tition & prisoner's dilemma

21. Key contributors
Bin Chen Ying Ding David Wild

22. OPDDR

23. OPDDR
Open Phenotypic Drug Discovery Resource

24. https://ncats.nih.gov/expertise/preclinical/pd2

25. OPDDR
collaboration

26. Example: OIDD HeLa cell based assay
Integrated RDF
bioassay:AID1117350
skos:exactMatch
oidd_assay:17 .
bioassay:AID1117350
dcterms:source source:ID846 ;
dcterms:title "Increased chromatin
condensation in HeLa cells-IC50"@en .
bioassay:AID1117350 rdf:type bao:BAO_0002786 .
bioassay:AID1117350 rdf:type bao:BAO_0000010 .
bioassay:AID1117350 rdf:type bao:BAO_0000219 .
endpoint:SID170464897_AID1117349
vocabulary:PubChemAssayOutcome vocabulary:active ;
sio:has-value "0.0656"^^xsd:float ;
a bao:BAO_0000190 ;
rdfs:label "IC50"@en .
substance:SID170464897
skos:exactMatch
chembl_molecule:CHEMBL1483 .
chembl_assay:OIDD00017
cco:hasCellLine
chembl_cell_line:CHEMBL3308376 .

27. D2D builds apps,
tools and solutions
for knowledge
discovery powered
by fast, scalable
network analytics
and rigorous
semantics.
d2discovery.com
Predictive Phenotypic Profiler (P3) prototype

28. openphacts.org

29. OPDDR
● OPDDR phenotypic assays have been linked and
integrated via community semantics to both
phenotypic (cell lines) and molecular
(genomic/protein targets)
● New phenotypic knowledge domain offers additional
value in drug discovery and pharmacological
informatics
● Open PHACTS excellent, well suited platform

30. DrugCentral

31. DrugCentral
● DrugCentral is a free, open, curated resource about
approved drugs, designed for research
● Compounds, products, labels, targets, IDs, names
● DrugCentral developed over several years at UNM
● DrugCentral recently released with new interface
● License: CC-BY-SA
http://drugcentral.org

32. http://drugcentral.org

33. http://drugcentral.org

34. DrugCentral
● Free, open, accurate, comprehensive drug reference
for biomolecular and biomedical informatics research
Compounds 4444
Products 84787
Synonyms 20522
Structures 4231
Targets 3651
Bioactivities 15620
MoA 3484
SNOMED 45349

35. "DrugCentral: online drug compendium", Oleg Ursu, Jayme Holmes, Jeffrey Knockel, Cristian Bologa,
Jeremy Yang, Stephen Mathias, Stuart Nelson, Tudor Oprea (manuscript submitted).

36. In Conclusion
● New resources continue to emerge and evolve, providing
opportunities for knowledge driven drug discovery
● Community standards → more intelligent web
● Adapt to new data environment for success
● Private + public data must be integrated to
○ Be prepared (like Pasteur)
○ Not "foul up" (like Glenn)