Clustering the royal society of chemistry chemical repository to enable enhanced navigation across millions of chemicals
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The document discusses a clustering approach for the Royal Society of Chemistry's chemical repository, encompassing around 30 million chemicals sourced from over 500 different origins. It details the use of latent semantic analysis and chemical similarity measures like Tanimoto coefficient for organizing data, as well as various fingerprint types for molecular representation. The results indicate the feasibility of efficient navigation through the large chemical space, supported by an ongoing effort in crowdsourced curation and annotation.
![Structures similarity
27
2. Jaccard P., Bulletin del la Société Vaudoise des Sciences Naturelles (1901), 37, 547-579
3. Tanimoto T.T., IBM Internal Report (1957)
• Molecules as binary vectors
• Various chemoinformatics dis-/similiarity measures:
– Euclidean distance
– Cosine similarity (inner product)
• Most frequently used: Tanimoto Coefficient 2,3
– Corresponds to Jaccard index
– Metric
– [0.0, 1.0] (dissimilar similar)
Molecule Similarity](https://image.slidesharecdn.com/clusteringtheroyalsocietyofchemistrychemicalrepositorytoenableenhancednavigationacrossmillionsofchem-140819084833-phpapp02/85/Clustering-the-royal-society-of-chemistry-chemical-repository-to-enable-enhanced-navigation-across-millions-of-chemicals-27-320.jpg)
Clustering the royal society of chemistry chemical repository to enable enhanced navigation across millions of chemicals
- 1. Clustering the Royal Society of Chemistry chemical repository to enable enhanced navigation across millions of chemicals Valery Tkachenko, Ken Karapetyan, Antony Williams, Oliver Kohlbacher, Philipp Thiel, Colin Batchelor ACS, 248th National Meeting San Francisco, CA August 14th 2014
- 2. Chemical space - 1060
- 3. Navigation in chemical space
- 4. Clustering
- 6. • ~30 million chemicals and growing • Data sourced from >500 different sources • Crowdsourced curation and annotation • Ongoing deposition of data from our journals and our collaborators • A structure centric hub for web-searching
- 7. ChemSpider
- 8. Properties
- 12. RSC Archive – since 1841
- 13. DERA - Digitally Enabling RSC Archive
- 15. Twelve broad categories Largest category is 30 times the size of the smallest
- 17. How does it work? Latent Semantic Analysis to build feature sets for (1) articles (2) categories. Features: words, citations and pairs of words. Domain experts (Journal Development staff) build a category vector. All articles with a cosine similarity greater than an adjustable threshold go into the category.
- 25. Structures similarity Molecule Similarity Similarity ?Similarity ? Suitable in silico representation: 2D binary fingerprints Suitable in silico representation: 2D binary fingerprints 0 1 0 1 0 1 1 0Y: 0 1 1 0 1 1 0 1X: 25 0 1 2 3 4 5 6 7
- 26. Structures similarity Molecule Similarity 26 • Important fingerprint properties: 1. Length: length of the binary vector 2. Density: fraction of 1-bits • Various fingerprint types exist – Different atom typing and generation procedure – Different properties (length, density, ...) • Alternative representation: Feature list – Store only index numbers of vector positions – Memory-efficient storage 0 1 0 1 0 1 1 0 0 1 0 1 0 1 1 0 Length 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 Sparse fingerprint (sFP) 1 1 0 1 0 1 1 0 0 1 1 1 0 1 1 1 Dense fingerprint (dFP) 0 1 0 1 0 1 1 0 1,3,5,6
- 27. Structures similarity 27 2. Jaccard P., Bulletin del la Société Vaudoise des Sciences Naturelles (1901), 37, 547-579 3. Tanimoto T.T., IBM Internal Report (1957) • Molecules as binary vectors • Various chemoinformatics dis-/similiarity measures: – Euclidean distance – Cosine similarity (inner product) • Most frequently used: Tanimoto Coefficient 2,3 – Corresponds to Jaccard index – Metric – [0.0, 1.0] (dissimilar similar) Molecule Similarity
- 28. Full Similarity Matrix Clustering 28 Results: Clustering the Available Chemspace • ZINC all purchasable set: ~17x106 compounds (sFP) • Tanimoto cutoff analysis: 0.76 • Opteron, 64 threads, 100 GB main memory Total run-time: 64 hours CCs decomposition: 12 hours Total run-time: 64 hours CCs decomposition: 12 hours
- 30. Thank you Email: [email protected] Slides: http://www.slideshare.net/valerytkachenko16
Editor's Notes
- #19: Change to add more database, rearrange