Clustering Made Human: US UGM 2008
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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.
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Clustering Made Human: US UGM 2008
- 1. Clustering made human Miklos Vargyas •Solutions for Cheminformatics
- 2. Cluster in computing Computer cluster 3
- 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 4
- 4. Cluster in Chemistry/Physics Nanoscale particles • Fullerenes • Nano machines Images produced by MarvinSpace 5
- 5. Star cluster gravitationally bound groups of stars Image from Wikipedia, the free encyclopedia 6
- 6. Clustering cars Live demonstration Group by property • Shape, size, type, brand, colour • Many possible arrangement, multiple aspects Group by similarity • Categorial perception 7
- 7. Why is clustering stars easy? God did the job for us! • Stars have an apparent spatial arrangement • Distance between stars defines clusters 8
- 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?! 9
- 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 10
- 10. Example in 2D 11
- 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 12
- 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 13
- 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 14
- 14. The most populated cluster 15
- 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 16
- 16. Another cluster 17
- 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 18
- 18. Maximum Common Substructure Largest substructure shared by two molecules MCS Simple concept! More human, visual. Yet hard (= expensive (= slow)) to compute.. 19
- 19. MCS of a structure set 20
- 21. Hierarchical star clusters star cluster • star 22
- 22. Hierarchical star clusters galaxy • star cluster – star 23
- 23. Hierarchical star clusters local group • galaxy – star cluster star 24
- 24. Hierarchical star clusters supercluster • cluster – local group galaxy » star cluster 25
- 26. Hierarchical MCS 27
- 27. Intuitive visualisation 28
- 28. SAR table view 29
- 29. R-group deconvolusion 30
- 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 31
- 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 32
- 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
- 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 34
- 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 35