This document discusses using clustering techniques to group molecules based on their structural similarity. It provides examples of hierarchical clustering of molecules using their maximum common substructure (MCS). MCS clustering allows intuitive visualization of molecule hierarchies and relationships. The document compares the performance of different clustering algorithms and notes that MCS-based clustering provides a more interpretable and human-understandable approach compared to other methods.

1. Clustering made human
Miklos Vargyas
•Solutions for Cheminformatics

2. Cluster in computing
Computer cluster
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3. 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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4. Cluster in Chemistry/Physics
Nanoscale particles
• Fullerenes
• Nano machines
Images produced by MarvinSpace
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5. Star cluster
gravitationally bound groups of stars
Image from Wikipedia, the free encyclopedia
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6. 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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7. 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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8. 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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9. 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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10. Example in 2D
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11. 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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12. 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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13. 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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14. The most populated cluster
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15. 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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16. Another cluster
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17. 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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18. 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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19. MCS of a structure set
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20. Hierarchical star clusters
star
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21. Hierarchical star clusters
star cluster
• star
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22. Hierarchical star clusters
galaxy
• star cluster
– star
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23. Hierarchical star clusters
local group
• galaxy
– star cluster
 star
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24. Hierarchical star clusters
supercluster
• cluster
– local group
 galaxy
» star cluster
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25. Visualisation of hierarchy
Dendrogram
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26. Hierarchical MCS
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27. Intuitive visualisation
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28. SAR table view
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29. R-group deconvolusion
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30. 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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31. 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
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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
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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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34. Find out more
Product descriptions & links
www.chemaxon.com/products.html
Forum
www.chemaxon.com/forum
Presentations and posters
www.chemaxon.com/conf
Download
www.chemaxon.com/downlo
ad.html
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