On the Discovery of Novel Drug-Target Interactions from Dense SubGraphs by Maria-Esther Vidal

1. On the Discovery of Novel
Drug-Target Interactions
from Dense SubGraphs
Alejandro Flores
Maria-Esther Vidal
Guillermo Palma
Universidad Simón Bolívar
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2. Agenda
 Motivation
 The esDSG problem
 Experimental Evaluation
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3. Motivation
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Network of Drug-Target Interactions.
Drug (Green); Target (Yellow).
Densest Subgraphs
from where novel drug-
target interactions can
be suggested.

4. Prediction Hypothesis
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t1
Similar Targets Similar Drugs
t2
t3
d1
d2
d3
Similar Targets Similar Drugs
d1
d2
d3
t2
t3
t1

5. THE ESDSG PROBLEM
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6. The esDSG Problem
• Given a bipartite graph G=(D U T, E)
The esDSG problem identifies the subgraph
G* G such that the edge-similarity density
of G* is maximized.
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7. Aggregated Similarity
Single-Node Similarity Edge Similarity
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8. Graph-TA 2015 8
Aggregated Similarity
Edge Similarities (Red)
Single-Node Similarities (Blue)
Drug-Drug Similarities
Target-Target Similarities

9. esDGS
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10. EMPIRICAL EVALUATION
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11. Evaluation on Drug-Target Interactions
 900 Drugs, 1,000 Targets and 5,000
Interactions: Nuclear receptor, Gprotein-
coupled receptors (GPCRs), Ion channels, and
Enzymes.
 DrugBank
K. Bleakley and Y. Yamanishi. Supervised prediction of drug target interactions using bipartite local
models. Bioinformatics, 25(18).2009.
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Nuclear
Receptor
GPCR Ion Channel Enzyme
Drugs 54 223 210 445
Targets 26 95 204 664
Interactions 90 635 1,476 2,926
Avg Interaction
per Target
3.46 6.68 7.23 4.4
Avg Interaction
per Drug
1.66 2.84 7.02 6.57

12. Validated Drug-Target Interactions
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13. Validated Drug-Target Interactions
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14. Conclusions and Future Plans
 The esDSG problem allows for the
prediction of novel interactions that
cannot be suggested by state-of-the-art
approaches.
 Extend esDSG to traverse different
connected components to identify
esDSGs from each component.
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