Klaus Gubernator, Craig James, e Molecules Inc. ACS 232nd National Meeting Division of Chemical Information San Francisco, September 14, 2006 Chemical Structure Search Engines in Cyberspace

 The web has revolutionized the way we retrieve information  Chemistry is a late participant in this revolution Chemistry on the Internet

NN Search Google Images for “Aspirin”

Acta Cryst. (1975). B31, The crystal structure of 7-amino-2H,4H-vic-triazolo[4,5-c]-1,2,6- thiadiazine 1,1-dioxide (ATT) C. Foces-FocesC. Foces-Foces, F. H. Cano and S. García-BlancoF. H. CanoS. García-Blanco Buy online You may purchase this article in PDF and/or HTML formats. For purchasers in the UK, and for purchasers elsewhere in the European Community who do not have a VAT number, VAT will be added to the price of the article. Format* PDF (US $40, plus US $7 for EC purchases) Structure of “triazolo thiadiazine”

Datasets (which are, in contrast to other dataset lists, available in a structural format) This list will be expanded continuously. Please don't hesitate to make published datasets publicly available here. Currently available: 44 Datasets Note: The Briem/Lessel and Hert/Willett Dataset are only available as MDDR ID's due to license reasons. Please contact MDL for further information on the database. The datasets have nonethless been included here because they are standard datasets for similarity searching. – Andreas BenderMDL  Binary (active/inactive) datasets Binary (active/inactive) datasets  QSAR datasets QSAR datasets  QSPR datasets QSPR datasets  Toxicity datasets Toxicity datasets  Metabolism datasets Metabolism datasets  Permeability datasets Permeability datasets  Docking datasets Docking datasets  Mechanistic datasets Mechanistic datasets  Mixed/Other datasets Mixed/Other datasets Cheminformatics.org

CS(=O)(=O)Nc1ccc(cc1OC2CCCCC2)N(=O)=O CS(=O)(=O)Nc1cc2CCC(=O)c2cc1Oc3ccc(F)cc3F CS(=O)(=O)Nc1cc2CCC(=O)c2cc1Sc3ccc(F)cc3F CS(=O)(=O)Nc1ccc(cc1Sc2ccc(F)cc2F)C(=O)N CS(=O)(=O)Nc1ccc(cc1Sc2ccc(Cl)cc2Cl)S(=O)(=O)N COc1ccc(cc1)c2sc(nc2c3ccc(cc3)S(=O)(=O)C)c4ccccc4Cl COc1ccc(cc1)c2sc(nc2c3ccc(SC)cc3)c4ccccc4Cl CS(=O)(=O)c1ccc(cc1)n2nc(cc2c3ccc(F)cc3)C(F)(F)F CS(=O)(=O)c1ccc(cc1)n2nc(cc2c3ccc(Br)cc3)C(F)(F)F Cc1ccc(cc1)c2cc(nn2c3ccc(cc3)S(=O)(=O)N)C(F)(F)F CS(=O)(=O)c1ccc(cc1)c2snnc2c3ccc(F)cc3 CC(=O)c1nc(c(o1)c2ccc(c(F)c2)S(=O)(=O)N)c3ccccc3 Cc1nc(C2CCCCC2)c(o1)c3ccc(c(F)c3)S(=O)(=O)N CS(=O)(=O)c1ccc(cc1)c2[nH]c(nc2C3CCCCC3)C(F)(F)F CS(=O)(=O)c1ccc(cc1)c2[nH]c(nc2c3ccc(F)cc3)C(F)(F)F CS(=O)(=O)c1ccc(cc1)C2=C(C(=O)OC32CC3)c4ccccc4 CS(=O)(=O)c1ccc(cc1)C2=C(C(=O)OC32CCCC3)c4ccccc4 CS(=O)(=O)c1ccc(cc1)c2cnn(Cc3ccccc3)c(=O)c2c4ccccc4 CS(=O)(=O)c1ccc(cc1)c2nn(Cc3ccccc3)c(c2c4ccc(F)cc4)C(F)(F)F NS(=O)(=O)c1ccc(cc1)c2c(CO)onc2c3ccccc3 CS(=O)(=O)c1ccc(cc1)c2cc(Cl)nn2c3ccc(F)cc3 NS(=O)(=O)c1ccc(cc1)c2cc(nn2c3ccc(F)cc3)C(F)(F)F NS(=O)(=O)c1ccc(cc1)n2nc(cc2c3nc4cccc(F)c4s3)C(F)F Stahl dataset

Unnamed -MTS D V S C C C C N C O C O O C C … M END > $$$$ Yokoyama dataset

Search Genbank for “aattccgg”

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Why is so little chemistry on the web?  Tradition?  Strong providers of subscription services?  Searching for chemical structures is significantly more difficult than text searching?  Chemical identifiers are not standardized?

Open Access Chemical Search Engines PubChem - NIH ChemBank – Harvard ZINC – UCSF ChemDB – UC Irvine ChemExper - Lausanne ChemFinder – CambridgeSoft

www. e molecules.com  New Chemistry Search Engine  A large database of publicly available molecular structures  Launched November 2005  50,000 searches per month, rapidly growing

www. e molecules.com Free chemistry search site for publicly available chemical information

Advanced Search Powerful features:  hit list management  union, intersect, subtract, difference  manual selection  export lists in many formats  persistent hitlists

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Content: 16M entries, 5.6M structures Academic and government databases  NIST WebBook  DrugBank  Protein Ligands Chemical suppliers  150 electronic catalogs included Future goal  All publicly available chemical information

Why is it so fast?  Novel chemical search engine technology  Method represents a major departure from previously known algorithms - Molecular keys (MDL) - Fingerprints (Daylight) - Feature Trees (BioSolv)

Search engine technology  Analyze each molecule for distinguishing structural features  Generate all features algorithmically  Normalize features and use them for indexing  Result: very fast searches

Who is e Molecules? Klaus Gubernator Craig A. James Rashmi Mistry

Summary  Free for depositors and users  Very fast search engine  High quality user interface  Rich functionality  Complementary with other engines

Contact Information Klaus Gubernator Skype: emolecules