1.
Chemical Interaction Matrix:
Gerald Lushington / LiS Consulting
http://geraldlushington.com / glushington@yahoo.com

2.
Personalized Medicine
Comprehensive Biochemical
& Chemical Biology Understanding
Big data: NGS, medical outcomes, etc.

3.
Personalized Medicine
Comprehensive Biochemical
& Chemical Biology Understanding
Informatics
& Creativity
HTS &
Chemical
Proteomics
Big data: NGS, medical outcomes, etc.

4.
Example Challenges:
●Toxicology: single toxin may modulate several
different biochemical processes
●Cancer: malignant cells have multiple biochemical
sensitivities that may be targeted
●Spectral disorders (e.g., Autism, Alzheimers, etc.):
distinct phenotypes produce similar symptoms
Discovery Paradigm:
Chemical screening prospective hits
Chemical proteomics prospective targets
How to attain comprehensive understanding?

5.
Data Comprehension Reality
TargetsCompounds

6.
How to make sense of diffuse multimode
data?
Mechanism of Action (MOA) discovery:
find compound subsets that conserve
common mechanism
Excellent (but imperfect) example:
TEST (Toxicology Estimation Software Tool)
http://www.epa.gov/nrmrl/std/qsar/qsar.html

7.
TEST
Multiple data sets covering toxicity outcomes
for numerous compounds
Predict toxicity of query compounds via on-the-fly
training to similar pre-characterized analogs

8.
TEST
Multiple data sets covering toxicity outcomes
for numerous compounds
Predict toxicity of query compounds via on-the-fly
training to similar pre-characterized analogs
Use Tanimoto distances over molecular
fingerprints: no validated relevance specific
outcomes

9.
Procedure:
1. Assemble Matrix of compounds vs.
activity & features
MOA method: feature / compound selection

10.
Procedure:
1. Assemble Matrix of compounds vs.
activity & features
2. Normalize
MOA method: feature / compound selection

11.
Procedure:
1. Assemble Matrix of compounds vs.
activity & features
2. Normalize
3. Fold activity into features as per:
Ci = |Act* - Xi*|
X values: 0 = perfect correlation
1 = perfect anticorrelation
MOA method: feature / compound selection

12.
Procedure:
1. Assemble Matrix of compounds vs.
activity & features
2. Normalize
3. Fold activity into features as per:
Ci = |Act* - Xi*|
4. Bicluster
MOA method: feature / compound selection

13.
Procedure:
1. Assemble Matrix of compounds vs.
activity & features
2. Normalize
3. Fold activity into features as per:
Ci = |Act* - Xi*|
4. Bicluster
Clusters Contiguous correlative or anticorrelative
regions or matrix
Within clusters: molecules may share
MOA; features may correlate with
activity
Confidence: correlative & predictive
quality of model derived from cluster
MOA method: feature / compound selection

14.
Example: Oral Bioavailability
Oral update depends on:
● Polar solubility
● Membrane permeability
● Interaction with various transporters
Data (from Tingjun Hou): 773 molecules
http://modem.ucsd.edu/adme/databases/databases_bioavailability.htm
Descriptors (from VolSurf and DVS): 298 features
passing information content and linear independence (R < 0.90) filters

15.
Example: Oral Bioavailability
Oral update depends on:
● Polar solubility
● Membrane permeability
● Interaction with various transporters
Data (from Tingjun Hou): 773 molecules
http://modem.ucsd.edu/adme/databases/databases_bioavailability.htm
Descriptors (from VolSurf and DVS): 298 features
passing information content and linear independence (R < 0.90) filters
Preliminary Model (Weka: Bootstrap Aggregating / RepTree):
Q2
(5-fold) = 0.4712

16.
Example: Oral Bioavailability
Oral update depends on:
● Polar solubility
● Membrane permeability
● Interaction with various transporters
Data (from Tingjun Hou): 773 molecules
http://modem.ucsd.edu/adme/databases/databases_bioavailability.htm
Descriptors (from VolSurf and DVS): 298 features
passing information content and linear independence (R < 0.90) filters
Preliminary Model (Weka: Bootstrap Aggregating / RepTree):
Q2
(5-fold) = 0.4712 CFS & RF:
reduced to
27 features
Q2
(5-fold) = 0.4739

17.
Biclustering: Before and After

18.
Clusters as local training sets:

19.
Clusters as local training sets:
Condense to 18 high quality clusters that cover almost
entire training space (omit only 10 of 768 cpds)

20.
Conclusions
Correlative & predictive performance of subset models
gives strong confidence in MOA conservation in
clusters
Head-to-head comparison with chemical proteomics
data should provide strong basis for target
identification
Questions / Suggestions?
glushington@yahoo.com