I571ChE531 2006 2D Chemical Database Applications

1
I571/ChE531 20062D Chemical Database Applications

• David Wild
• Assistant Professor, Life Science Informatics
• Indiana University School of Informatics
• djwild_at_indiana.edu

2
What well cover today

• Basic 2D chemical structure database theory
• Types of searching
• Substructure querying with SMARTS
• Using fingerprints for similarity searching
• Demonstration of substructure similarity
  searching
• Molinspiration
• Pubchem
• Available 2D database systems
• ISIS/Base, Oracle cartridges
• Demonstration of Cartridge searching
• Daycart

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Increasing size of chemical information

• Chemical Abstracts Service database (CAS) now
  holds information on 29 million compounds
• Pubchem contains 12 million compounds
• Pharmaceutical companies typically have over 1
  million proprietary compounds
• Other companies are producing large numbers of
  compounds through combinatorial chemistry
• Typically several hundred thousand compounds
  would be screened for a particular project

4
Combinatorial Chemistry

• By combining molecular building blocks, we can
  create very large numbers of different molecules
  very quickly.
• Usually involves a scaffold molecule, and sets
  of compounds which can be reacted with the
  scaffold to place different structures on
  attachment points.
• The set of molecules that is created (or which
  can be theoretically created) by a combinatorial
  experiment is called a combinatorial library.
• Feeds the capacity of High Throughput Screening

5
2D structure databases

• Special kinds of searching for 2D structure
  databases
• Specifying substructure queries with SMARTS
• Using fingerprints for similarity searching
• Commercially available databases

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Types of 2D structure searching

• Structure search
• Is this structure in the database?
• Substructure search
• Find me all of the structures that contain this
  substructure
• Similarity search
• Find me all of the structures that are similar
  to this one

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Structure search

• Looking for a particular structure in a database
• Searching proprietary databases or commercial
  databases
• e.g. is this structure in the database?
• Mathematically, the connection table can be
  considered a graph, and this is a graph
  isomorphism problem (solved)

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Substructure search

• Looking for all structures that contain one or
  more particular structural fragments
• e.g. which structures contain a nitro group?
• Mathematically, this is a subgraph isomorphism
  problem (Ullman Algorithm)
• Requires way of representing query fragment(s)

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Similarity search

• Looking for all the structures in a database that
  are highly similar to a given structure
• e.g. show me structures with a similarity greater
  than 0.7 to this molecule
• Requires a way of measuring similarity
• Solved using fingerprint representations and
  similarity coefficients

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www.molinspiration.com/cgi-bin/search
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Substructure search results
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Similarity search results
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Specifying a substructure query with SMARTS

• SMARTS is a superset of SMILES that is extended
  to allow partial structures (substructures) and
  optional parts of molecules to be represented
• Simple example
• C(O)O
• where the represents an attachment point (i.e.
  any number of any atoms)
• More information
• http//www.daylight.com/meetings/summerschool01/co
  urse/basics/smarts.html
• http//www.daylight.com/dayhtml/doc/theory/theory.
  smarts.html

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SMARTS special characters (examples)
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SMARTS examples
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Try out a SMARTS search

• DepictMatch
• http//www.daylight.com/cgi-bin/contrib/depictmatc
  h.cgi
• Enter a set of SMILES and a SMARTS, and any part
  of the SMILES that is found in the SMARTS is
  highlighted
• As an example, well use the sample dataset
  described on the following two slides, and use
  C(O)O (carboxyl group) as our SMARTS and
  RC(O)O (carboxyl attached to a ring)

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Sample dataset
Acetaminophen
Alprenolol
Amphetamine
Captopril
Chlorpromazine
Diclofenac
Gabapentin
Salicylate
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Sample Dataset SMILES file

• CC(O)Nc1ccc(O)cc1 Acetaminophen
• CC(C)NCC(O)COc1ccccc1CCC Alprenolol
• CC(N)Cc1ccccc1 Amphetamine
• CC(CS)C(O)N1CCCC1C(O)O Captopril
• CN(C)CCCN1c2ccccc2Sc3ccc(Cl)cc13 Chlorpromazine
• OC(O)Cc1ccccc1Nc2c(Cl)cccc2Cl Diclofenac
• NCC1(CC(O)O)CCCCC1 Gabapentin
• COC(O)c1ccccc1O Salicylate

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Measuring similarity between molecules

• Similar Property Principle Molecules with
  similar structure are likely to have similar
  biological activity
• Generally the Tanimoto Coefficient or Euclidean
  Distance between fingerprints is used

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Fingerprint Similarity Tanimoto

• Also known as Jaccard Coefficient
• 1s in common / 1s not in common
• 0s are treated as not significant
• Similarity is between 0 (dissimilar) and 1 (same)
• Good cutoff for likely biologically similar
  molecules is 0.7 or 0.8

c 1s in common a 1s in fingerprint A b
1s in fingerprint B
c 4 a 6 b 6
A 101101011 B 011101101
Example
Tanimoto Similarity 4 / ( 6 6 4 ) 0.5
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Fingerprint similarity Euclidean

• Pythagorean distance
• For binary dimensions, equivalent to the square
  root of the Hamming distance (i.e. square root of
  the number of bits that are different)
• 0s are treated as significant
• Smaller values mean more similar
• Example
• 101101011
• 011101101
• Different? xx xx
• Euclidean distance sqrt(4) 2.0

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References on Similarity Searching

• Chemical Similarity Searching, P. Willett, J.M.
  Barnard, G.M.Downs, Journal of Chemical
  Information and Computer Sciences, 1998, 38,
  983-996 (available as a PDF on Oncourse)
• And check out the JCIM ASAP!

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Basic facilities required by Pharmaceutical
companies

• Structure Registration
• Being able to store information about new
  compounds that are created in-house or brought in
  from outside
• Structure and Data Searching
• Being able to search for particular compounds or
  groups of compounds, based on structure,
  biological data, test results, etc., and export
  this information

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Structure Registration

• Submission of new compounds made by a chemist to
  a proprietary database
• Chemist will generally draw in structure,
  specifying sterochemistry, etc.
• Other information provided Chemists name,
  notebook numbers, reaction protocol, safety
  information, amounts made, barcode , etc
• Will usually be inspected by a registrator, and
  assigned a unique identifier (e.g. PF123456 for
  Pfizer, A12345 for Abbott, etc)
• Batch registration (e.g. for combinatorial
  libraries) will also be provided
• May be interfaced with a LIMS system

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Structure Registration Systems

• new compounds need to be added regularly
• used to be done by chemical information
  specialists
• now frequently done directly by bench chemists
• registration system must
• check consistency of input data
• e.g. compare molecular formula with structure
• check that compound is really new
• different ways of handling tautomers, salts,
  stereoisomers etc.
• assign registry number
• add supplementary data (melting point etc.)
• make data immediately available for search

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Structure Searching System

• Structure, substructure and similarity searching
  based on 2D structure, or other text/numeric
  fields
• Exporting of lists and tables of structures with
  associated data (biological activities,

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MDL ISIS

• Currently used by most pharmas as chemical
  informatics backbone
• Provides server software (back-end) for
  maintaining databases of chemical structures and
  other data
• Provides client software (front-end) called
  ISIS/Base for registration, searching, etc
• ISIS/Base and ISIS/Draw can be installed on
  chemists desktop machines

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MDL ISIS

• Can maintain multiple views of the data, e.g.
  for specific projects
• Interface rather clumsy, and technology showing
  its age
• Maintaining two separate databases is complex
• Oracle with chemistry cartridges slowly replacing
  back end
• More information www.mdl.com

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MDL ISIS
ISIS/Base
ISIS/Base
ISIS/Base
ISIS/Host
ISIS Database for chemical structures
Oracle Database for other data
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MDL ISIS/Base
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Web / Oracle systems
Web Browser
Web Browser
Web Browser
Web applications server
Oracle database with chemistry cartridge
SQL
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Web / Oracle Systems

• Advantages
• Single database for structures and data
• No software to install on client machines (except
  maybe plug-ins like Chime)
• Not dependent on (expensive) contract with MDL
• Highly customizable
• Disadvantages
• Requires extensive web-based interface software
  to be written, for registration, searching, etc
• Company will have to maintain system internally
• Requires current ISIS system to be abandoned

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Other systems available

• Most of the chemical informatics companies do not
  provide a complete package, but have parts (e.g.
  database systems). Some exceptions are
• IDBS ActivityBase
• http//www.id-bs.com/products/abase/
• Accelrys DS Accord Enterprise Informatics
• http//www.accelrys.com/aei/index.html

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Chemical Searching using Oracle

• Oracle 8 and higher have the ability to add
  functionality using cartridges
• These cartridges can add new data types and new
  functions to SQL
• The last few years have seen the release of
  several chemistry cartridges for Oracle
• Informix has a similar DataBlade but this has
  not been as exploted by the chemical community

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Chemical Searching using Oracle / SQL

• Oracle 8 and higher have the ability to add
  functionality using cartridges
• These cartridges can add new data types and new
  functions to SQL
• Other databases like PostgreSQL can also use
  cartridges
• The last few years have seen the release of
  several chemistry cartridges for Oracle and other
  databases
• Informix has a similar DataBlade but this has
  not been as exploted by the chemical community

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Chemistry Cartridges

• Daylight DayCart
• http//www.daylight.com/products/daycart.html
• Tripos Auspyx
• http//www.tripos.com/sciTech/inSilicoDisc/chemInf
  o/auspyx.html
• Accelrys Accord for Oracle
• http//www.accelrys.com/accord/oracle.html
• MDL Direct
• http//www.mdl.com/products/framework/rel_chemistr
  y_server/index.jsp
• IDBS ActivityBase
• http//www.id-bs.com/products/abase/
• gNova CHORD
• http//www.gnova.com
• JChem Cartridge
• http//www.jchem.com

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Example - DayCart

• Store SMILES as string (VARCHAR2) in Oracle
  database
• Cartridge provides extra functions and extensions
  to functions for searching based on chemical
  structures
• Structure search implemented by EXACT function
• Substructure search implemented by MATCHES
  function
• Similarity search implemented by TANIMOTO and
  EUCLID functions

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Sample dataset
Acetaminophen
Alprenolol
Amphetamine
Captopril
Chlorpromazine
Diclofenac
Gabapentin
Salicylate
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Oracle table Test for sample dataset

• Smiles Name
  LogP
• ------ ----
  ----
• CC(O)Nc1ccc(O)cc1 Acetaminophen
  0.27
• CC(C)NCC(O)COc1ccccc1CCC Alprenolol
  2.81
• CC(N)Cc1ccccc1 Amphetamine
  1.76
• CC(CS)C(O)N1CCCC1C(O)O Captopril
  0.84
• CN(C)CCCN1c2ccccc2Sc3ccc(Cl)cc13
  Chlorpromazine 5.20
• OC(O)Cc1ccccc1Nc2c(Cl)cccc2Cl Diclofenac
  4.02
• NCC1(CC(O)O)CCCCC1 Gabapentin
  -1.37
• COC(O)c1ccccc1O Salicylate
  2.60

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DayCart structure search using SQL

• select from Test where
• exact(Smiles, CC(N)Cc1ccccc1) 1
• Smiles Name
  LogP
• ------ ----
  ----
• CC(N)Cc1ccccc1 Amphetamine
  1.76

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DayCart substructure search

• select from Test where
• matches(Smiles, C(O)O) 1
• Smiles Name
  LogP
• ------ ----
  ----
• CC(CS)C(O)N1CCCC1C(O)O Captopril
  0.84
• OC(O)Cc1ccccc1Nc2c(Cl)cccc2Cl Diclofenac
  4.02
• NCC1(CC(O)O)CCCCC1 Gabapentin
  -1.37
• COC(O)c1ccccc1O Salicylate
  2.60

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Substructure search for carboxylic acid
Acetaminophen
Alprenolol
Amphetamine
Captopril
Chlorpromazine
Diclofenac
Gabapentin
Salicylate
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DayCart substructure / value search

• select from Test where
• (matches(Smiles, C(O)O) 1)
• AND (LogP gt 1.0))
• Smiles Name
  LogP
• ------ ----
  ----
• OC(O)Cc1ccccc1Nc2c(Cl)cccc2Cl Diclofenac
  4.02
• COC(O)c1ccccc1O Salicylate
  2.60

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DayCart similarity search
Aspirin

• select from TEST where
• tanimoto(SMILES, CC(O)Oc1ccccc1C(O)O) gt
  0.6
• SMILES NAME
  LOGP
• ------ ----
  ----
• COC(O)c1ccccc1O Salicylate
  2.60
• CC(O)Nc1ccc(O)cc1 Acetaminophen
  0.27
• CC(N)Cc1ccccc1 Amphetamine
  1.76

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Similarity search for carboxylic acid
?
?
Acetaminophen
Alprenolol
Amphetamine
Captopril
?
Chlorpromazine
Diclofenac
Gabapentin
Salicylate
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More examples of DayCart

• http//www.daylight.com/meetings/summerschool02/co
  urse/admin/daycart_hints.html

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Follow-up

• Try a substructure and similiarity search
  yourself at www.molinspiration.com/cgi-bin/search.
  Note any structures you find particularly
  surprising on the similarity search. If possible,
  capture a screenshot of the results (CTRLPRNT
  SCRN on PCs to copy). Email notes and/or
  screenshots to djwild_at_indiana.edu (5 of grade)