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- 2. Acknowledgements • John Barnard • Joseph Scheiber • Daniel Lowe • David Wild • Nina Jeliazkova
- 3. Outline • Chemical structure representaLon(s) • Processing chemistry‐related documents • Structure retrieval • Chemical informaLon toolkits
- 6. Visual RepresentaLons • 2D structure diagram is the lingua franca of bench chemists • Display is supported by nearly every cheminformaLcs system • Summarizes – ConnecLvity – Stereochemistry – Charge/isotope • We can use 2D representaLons as the starLng point for many cheminformaLcs tasks
- 8. Chemical Numbers • Also termed registry numbers • Arbitrary numbers assigned to one (or more) structures • SomeLmes a hierarchy might be present – Parent compound – stereoisomer – salt … • CAS numbers / PubChem SID & CID / InChI Key • Only way to get back structure is lookup
- 9. Structures are Graphs (mostly) • For many cases, a chemical structure can be considered as a graph – Atoms are nodes, bonds are edges – IdenLcal graphs imply idenLcal molecules • But there are limits to this representaLon – Polymers, inorganic compounds, stereochemistry • And chemical phenomena can create problems for a graph theoreLcal approach
- 10. AromaLcity & Graphs • These two molecules are idenLcal • Yet their molecular graphs would suggest a different connecLvity • In fact, all atoms and bonds in benzene are equivalent due to resonance • In this case, we would (should) perceive each C‐C bond as aromaLc
- 13. • A simple line notaLon, preey much the lingua franca of cheminformaLcs • Atoms are represented by their symbols – Lower case indicates aromaLc atom • Single bonds are implicit, double bonds are “=“, triple bonds are “#” and aromaLc are “:” • Rings indicate by “ring closure numbers” – In C1CC1, the two carbons marked by “1” are connected SMILES
- 14. Canonical SMILES • Given a structure, you can write a SMILES representaLon in mulLple ways CC(C)CC CCC(C)C • As a result, comparing molecules or searching for molecules based on arbitrary SMILES can give misleading or wrong results • To avoid this we canonicalize SMILES
- 15. Canonical SMILES • Given two structures that have idenLcal atoms, bonds and connecLvity their canonical SMILES will be idenLcal • In general, any permutaLon of atom index will not affect the canonical SMILES • CanonicalizaLon is a key feature of structure registraLon – You want to be sure that you have a single, unique representaLon of each structure in the database
- 16. GeneraLng Canonical SMILES • All toolkits are capable of generaLng these • Using OpenBabel at the command line is easy • Can convert lots of other file formats to canonical SMILES • Note that different products have different canonicalizaLon algorithms $ echo "c1(O)ccccc1" | /usr/local/openbabel/bin/babel -ismi -ocan –c Oc1ccccc1 $ echo "c1(ccccc1)O" | /usr/local/openbabel/bin/babel -ismi -ocan –c Oc1ccccc1
- 18. Tautomerism • Due to the rapid equilibrium the molecule exists in both forms – which one do we store? – Consider the most stable tautomer – Just go with a ‘canonical’ tautomer • In general, you need to generate the canonical SMILES for a canonical tautomer • Using InChI, you can choose whether to consider tautomers or not or just ignore tautomer informaLon
- 20. Markush Structures • Compact representaLon of a set or class of specific compounds with common structural features • Used in – chemical patents – query structures in substructure search systems – QuanLtaLve Structure‐AcLvity RelaLonship (QSAR) analysis • class of related compounds with acLvity data – combinatorial libraries • rapid synthesis of large numbers of related compounds – legislaLon (controlled drugs, chemical weapons)
- 22. Markush Structures • S‐variaLon (subsLtuent variaLon) – List of alternaLve values for an R‐group • P‐variaLon (posiLon variaLon) – Variable point of aeachment • F‐variaLon (frequency variaLon) – MulLple occurrence of groups • H‐variaLon (homology variaLon) – Generically described group (e.g., alkyl) – A (possibly) infinite set of S‐variaLons
- 23. Markush Structures • S‐variaLon – R1 is methyl or ethyl • H‐variaLon‐ R2 is alkyl • P‐variaLon – R3 is amino • F‐variaLon – 3 (but more generally can be any number, say m)
- 24. Markush Structures • Can be considered as formal “grammar” for generaLng valid molecules (“sentences”) • EnumeraLon of coverage usually impracLcal and open impossible (infinite sets) • Appropriate algorithms for handling take advantage of Markush representaLon – Avoid enumeraLon (especially infinite sets) – Compare finite grammars rather than infinite sets of valid sentences
- 27. Chemical Similarity • When are two molecules similar? – They are both 6‐membered rings – Both have carbons – Both have only single bonds • In the second case, both have a N • Many ways to define similarity • CriLcally dependent on representaLon
- 28. Why Chemical Similarity? • Much of medicinal chemistry is based on the similarity principle – Similar molecules exhibit similar acLviLes – J. Med. Chem., 2002, 45, 4350-4358 • But there are many excepLons • Even then, looking for similar molecules gives us a useful starLng point in many cases
- 31. Fingerprint Similarity • InformaLon loss – fragments presence and absence instead of counts • Bit string saturaLon – within a large database almost all bits are set • The average similarity appears to increase with the complexity of the query compound • Larger queries are more discriminaLng (flaeer curve, Tanimoto values spread wider) • Smaller queries have sharp peak, unable to disLnguish between molecules Flower D., On the Properties of Bit String- Based Measures of Chemical Similarity, J. Chem. Inf. Comput. Sci., Vol. 38, No. 3, 1998 The distribution of Tanimoto values found in database searches with a range of query molecules Nina Jeliazkova, hep://vedina.users.sourceforge.net/publicaLons/2005/ChemicalSimilarity.ppt
- 32. Physical Similarity • Keyed fingerprints are inevitably lossy • Hashed and circular fingerprints can be beeer • But in the end they both ignore the 3D structure of a molecules • Shape and surface‐property based similariLes can be more relevant – Slower to evaluate – OpenEye ROCS is a tool to evaluate shape similarity
- 34. Similarity Indices Association indices Correlation indices J. D. Holliday, C-Y. Hu† and P. Willett,(2002) Grouping of Coefficients for the Calculation of Inter- Molecular Similarity and Dissimilarity using 2D Fragment Bit-Strings, Combinatorial Chemistry & High Throughput Screening,5, 155-166 155 Nina Jeliazkova, hep://vedina.users.sourceforge.net/publicaLons/2005/ChemicalSimilarity.ppt
- 38. Exact Structure Retrieval Q Give a structure X, does the database have any instances of X? • The trivial way to do this is via string matching • Could match on canonical SMILES • But then you have to ensure that the query and the database employ the same canonicalizer
- 39. Exact Structure Retrieval Q Give a structure X, does the database have any instances of X? • The trivial way to do this is via string matching • Beeer to use a hash code such as InChI • Need to be careful that you and the database are using the same seungs (i.e., standard InChI)
- 40. Exact Structure Retrieval • This type of query is generally only useful during database registraLon • Is also handy when trying to match up one collecLon against and another • But can trip you up – Database stores stereochemistry, your query has none – You won’t find a match, if you use a full InChI – Similar problems with tautomerism
- 41. Similar Structure Retrieval Q Give a structure X, does the database have any molecules similar to X? • This can open up a can of worms! • Most common case is to find structurally similar molecules in terms of 2D • However, one can also consider 3D structural (i.e., shape) similarity • Finally, one could also idenLfy similar molecules based on similar physicochemical properLes
- 43. Substructure Retrieval Q Give a structure X, does the database have any molecules that contain X? • This is basically subgraph isomorphism • There are a number of variaLons • Find an exact substructure • Fuzzy substructure (e.g., ignore atom type) • Maximum common substructure
- 44. Substructure Retrieval • The basic subgraph isomorphism algorithms are quite well known • All cheminformaLcs toolkits support this • In the simplest approach, we can specify a SMILES string as a query – c1ccccc1C(=O) as a query looks for molecules containing a benzaldehyde moiety
- 45. Generic Substructure Queries • Using a SMILES as a query implies that you look for a specific substructure • What about finding molecules containing an aromaLc ring connected to carbonyl via a N or a C? • Valid molecules would be • We can perform these queries using SMARTS
- 46. SMARTS Queries • Regular expressions for chemical structures • The previous query is achieved by c1ccccc1[c,C,n,N]C=O • Very powerful system and fundamental to many cheminformaLcs methods • Also see SMARTSViewer to visualize SMARTS queries and the Daylight matcher to test them
- 48. Maximum Common Substructure • Largest subgraph common to two structures • NP‐complete problem • Many approaches try to idenLfy an approximate soluLon • MulLple MCS can be used to idenLfy core scaffolds
- 52. Parsing Chemical Documents • I have minimal experLse in this area and more of a user than a developer of such tools • Two step process – Chemical enLty extracLon (from text or images) – EnLty (i.e., name) to structure conversion • Variety of tools for both steps
- 53. Parsing Chemical Documents Text en;ty recogni;on Image recogni;on (a) Extractors (IUPAC names) ‐ TEMIS Chemical EnLty RelaLonships Skill Cartridge ‐ Accelrys Pipeline Pilot extractor (NoLora) ‐ Fraunhofer (ProMiner Chemistry) ‐ Chemaxon (chemicalize.org) ‐ Oscar (Corbee, Murray‐Rust et al.) ‐ SureChem ‐ IBM ChemFrag Annotator (b) Converter ‐ CambridgeSop name=struct ‐ Openeye Lexichem ‐ Chemaxon ‐ OSRA (NIH) ‐ Clide Pro (Keymodule Ltd.) ‐ Fraunhofer chemoCR ‐ ChemReader hep://www.chemaxon.com/library/user‐presentaLons/chemical‐enLty‐extracLon‐using‐the‐chemicalize‐org‐technology/
- 55. OPSIN
- 56. OPSIN • Can be used as a library or as a command line tool • Outputs CML by default, but this can easily be converted to other formats by CDK or OpenBabel • See here for some benchmarks of OPSIN versus Lexichem and ChemAxon
- 57. OPSIN Example package gov.nih.ncgc; import nu.xom.Element; import uk.ac.cam.ch.wwmm.opsin.NameToStructure; import uk.ac.cam.ch.wwmm.opsin.NameToStructureException; import java.io.BufferedReader; import java.io.File; import java.io.FileReader; import java.io.IOException; public class OpsinExample { String filename; public OpsinExample(String filename) throws NameToStructureException, IOException { this.filename = filename; } public void run() throws NameToStructureException, IOException { NameToStructure nameToStructure = NameToStructure.getInstance(); BufferedReader reader = new BufferedReader(new FileReader(new File(filename))); String line; while ((line = reader.readLine()) != null) { Element cmlElement = nameToStructure.parseToCML(line); System.out.println(cmlElement.toXML()); } } public static void main(String[] args) throws IOException, NameToStructureException { OpsinExample oe = new OpsinExample("/Users/guhar/Documents/Presentations/trec/sw/chemnames.txt"); oe.run(); } }
- 58. Lexichem Example from openeye.oechem import * from openeye.oeiupac import * mol = OEGraphMol() for line in open('chemnames.txt'): line = line.strip() mol.Clear() OEParseIUPACName(mol,line) print OECreateCanSmiString(mol)