Clustering made human
Miklos Vargyas
•Solutions for Cheminformatics

Cluster in computing
Computer cluster
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Cluster in Chemistry
Transition metal carbonyl clusters
Dimanganese-decacarbonyl di-tungsten tetra(hpp)
Transition metal halide clusters
Boron hydrides
Gas-phase clusters and fullerenes
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Cluster in Chemistry/Physics
Nanoscale particles
• Fullerenes
• Nano machines
Images produced by MarvinSpace
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Star cluster
gravitationally bound groups of stars
Image from Wikipedia, the free encyclopedia
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Clustering cars
Live demonstration
Group by property
• Shape, size, type, brand, colour
• Many possible arrangement, multiple aspects
Group by similarity
• Categorial perception
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Why is clustering stars easy?
God did the job for us!
• Stars have an apparent spatial arrangement
• Distance between stars defines clusters
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Why is clustering cars hard?
Lack of innate spatial arrangement
• Artificial arrangement
• Various approaches, no superior one
• “Cars come in all shapes and sizes”
Problem of dimensionality
• Why 2?!
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So what about Molecules
Are they like stars or rather like cars?
• They come in all shapes and sizes
• Vast number of properties
Chemical spaces
• Select molecular properties
• Estimate or measure them
• Use them as coordinates
• Place your molecules as points in this abstract space
• Group that are close to each other to form clusters
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Example in 2D
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Further attempts in 2D
300
250
200
logP
150
100
50
300 0
0 200 400 600 800 1000
250 tpsa
200
mass
150
100
50
0
-2 0 2 4 6 8 10 12
tpsa
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Molecule clusters by similarity
Jarvis-Patrick clustering
• Fast SC1000.cfp -m 0 -f 1024 -t 0.6 -c
jarp -i 0.1
• Tanimoto -o SC1000.jarp.t0.6.c0.1 –g
-y -z similarity
• Globular clusters
Number of objects = 999
• Tendency to create large singletons) =
Number of clusters (without
number of 2
singletons
Number of singletons = 8
• Molecular properties & fingerprint
Average dissimilarity = 0.66208726
Minimum dissimilarity = 0.0
Maximum dissimilarity = 0.9411765
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Parameter tuning
t c Clusters singletons
0.6 0.1 2 8
0.3 0.1 179 248
0.5 0.1 7 36
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The most populated cluster
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Parameter tuning
t c Clusters singletons
0.6 0.1 2 8
0.3 0.1 179 248
0.5 0.1 7 36
0.5 0.5 10 37
0.5 0.8 81 115
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Another cluster
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So what’s wrong with that?
1. manual tuning
2. lack of interpretability
3. need:
4. automated (unsupervised) techniques
5. easy to grasp simple to understand “explanations”
6. one possible solutions: MCS based clustering
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Maximum Common Substructure
Largest substructure shared by two molecules
MCS
Simple concept! More human, visual.
Yet hard (= expensive (= slow)) to compute..
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MCS of a structure set
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Hierarchical star clusters
star
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Hierarchical star clusters
star cluster
• star
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Hierarchical star clusters
galaxy
• star cluster
– star
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Hierarchical star clusters
local group
• galaxy
– star cluster
 star
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Hierarchical star clusters
supercluster
• cluster
– local group
 galaxy
» star cluster
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Visualisation of hierarchy
Dendrogram
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Hierarchical MCS
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Intuitive visualisation
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SAR table view
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R-group deconvolusion
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Speed-up achieved last year
4000
3500
2006
3000 2007
Linear (2007)
Running time (sec)
2500
2000
1500
1000
500
0
-500
0 5000 10000 15000 20000 25000 30000 35000
Structure count
Presented at UGM’07
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Speed-up achieved this year
4000
3500
2006
3000 2007
2008
Running time (sec)
2500
2000
1500
1000
500
0
0 5000 10000 15000 20000 25000 30000 35000
Structure count
32

Speed-up this year
10000
1000
Running time (sec)
100 2006
2007
2008
10
1
0.1
0 5000 10000 15000 20000 25000 30000 35000
Structure count
33

Clustering performance comparison
90
80
LibraryMCS
Running time (min)
70
60 Jarvis-Patrick
Ward-Murtagh
50
40
30
20
10
0
0 20000 40000 60000 80000 100000 120000
Structure count
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