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OMPOL – Visualisation of large chemical spaces
Peter Corbett, Colin Batchelor, Alexey Pshenichnov, Valery Tkachenko
Royal Society of Chemistry
ACS Spring 2016San Diego, CAMarch 17th 2016
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CompoundsReactionAnalytical DataText and References
ChemSpider Synthetic Pages
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Chemical space - 1060
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Dimensions and complexity of science
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RSC Data Repository
Data Repository
Properties Names and Identifiers Spectra Articles Data
Collections Patents Etc
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RSC CompoundsRSC ReactionsRSC SpectraRSC CrystalsRSC PolymersRSC MaterialsRSC AssaysRSC AlgorithmsRSC Models…and on…
RSC Databases
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Record labels
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Need to be able to see what sorts of structures are in a collection, how they relate to each other, etc.Could use something like clusteringDimensionality Reduction – chemical structures -> fingerprints -> large dimensional space -> small dimensional spaceStandard technique – Principal Components Analysis (PCA)
Visualising Chemical Space
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Dimensionality Reduction – First make a molecule-feature matrix
1 0 0 0 0 0 0 0 … 0
0 0 1 0 0 0 0 0 … 0
1 1 0 0 1 0 0 0 … 1
1 1 0 1 0 0 0 0 … 1
1 1 0 0 0 0 0 0 … 0
1 0 0 0 0 0 0 1 … 0
1 0 1 0 1 1 0 0 … 0
1 0 0 1 0 0 0 0 … 1
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PCA/SVD
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The result0.209 0.078 -0.368 …
0.030 0.297 0.174 …
0.509 0.005 0.343 …
0.514 -0.394 0.172 …
0.320 -0.034 -0.198 …
0.228 0.108 -0.791 …
0.338 0.812 0.151 …
0.403 -0.281 0.003 …
<--- Most important Least important --->
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Plot on a graph
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Need an interactive scatterplotWeb delivery => JavaScript
Need, at minimum, to click, mouseover, pan and zoomExisting scatterplot libraries, e.g. flot.js, are plentiful and
well supported……but do not scale well – become slow and unresponsive
with ~40,000 data points
The problem
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Make your own graph-plotting toolOMPOL – One Million Points Of Light – an aspiration for scalability
HTML5 Canvas“Google maps” style drawing
Divide graph into panelsDraw panels as they come onto the screenAssemble display from pre-drawn panels
Opportunity for better ways of exploring the data
The solution
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ChEBI~50000 compounds, of “Biological Interest”Has an ontology of compound types
Example data
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Display data from dimensional reductionSelecting data points, sets of data points“Narrowing down” a cluster of compounds based on distribution in multiple dimensionsExporting dataUsing name and ontology information to select groups of points
What we’re going to show
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Works very nicely with ~50000 data points and all featuresDuring development, was able to work with 1M and on occasion 10M data points
Only in 2D, didn’t have all features turned enabled
How scalable?
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Interacting with large (tens of thousands to millions of data points) multidimensional data sets is now a definite possibility
Conclusion