Towards knowledge representationimprovements in chemistry
Evan Bolton, Ph.D.
Mar. 15, 2016
National Center for Biotechnology Information
MOST CHEMISTRY KNOWLEDGE ISLOCKED UP IN TEXT.
Premise
Image credits:
https://play.google.com/store/apps/details?id=com.uc.addon.web2pdf
http://ideasuccessnetwork.com/idea-discovery-article-how-good-idea-mushroomed/
http://xk2.ahu.cn/
http://libraryschool.libguidescms.com/content.php?pid=682172
https://www.freshrelevance.com/blog/real-time-marketing-report-for-may-2014
Simplistic science workflow
Doscience
Publishpapers
Searchpapers
Readpapers
Image credits:
http://www.how-to-draw-funny-cartoons.com/cartoon-scientist.html
http://computertutorinc.net/computer-maintenance-safety-tips/
Computers help yet werely on humans whoabstract out keywords,article gist, data, etc
CAS, Medline,ChEMBL, etc.
Computer aided abstraction
Image credits:
http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/ner/
http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/PubTator/
http://www.intechopen.com/books/theory-and-applications-for-advanced-text-mining/biomedical-named-entity-recognition-a-survey-of-machine-learning-tools
http://www.slideshare.net/NextMoveSoftware/leadmine-a-grammar-and-dictionary-driven-approach-to-chemical-entity-recognition
BioCreative V (2015)
Name entity recognition (NER)now as good as a human to findchemical names, gene names,disease names in text corpus.
Natural LanguageProcessing (NLP)attempts to “read”text with a computerwith human-likeunderstanding …
is getting there
Effects of computer understandingyou can experience
HOW DO WE BRING IT ALL TOGETHER?
Knowledge representation helps to provide “information about the worldin a form that a computer system can utilize to solve complex tasks”…
https://en.wikipedia.org/wiki/Knowledge_representation_and_reasoning
Image credit: http://artint.info/html/ArtInt_8.html
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Chemical information is everywhere now
PubChem data complexity
•Many links between large record collections
–~210M Substances <-> ~85M Compounds
–~85M Compounds <-> ~85M Compounds
–~225M Bioactivities <-> ~3M Substances
–~225M Bioactivities <-> ~2M Compounds
–~225M Bioactivities <-> ~1M BioAssays
–~11M PMIDs <-> ~100K Compounds
–~3M Patents <-> ~30M Substances
–~3M Patents <-> ~15M Compounds
•Sparse and dense data and/or linking
•New types of data, links, and metadata on aregular basis
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With so many links, how does one ensure theyare accurate, relevant?
How does one improve the specificity of links?
•How do you describe a chemical substance?
–No standards, meta data associated with chemical representation (e.g., purity)
•How do you describe a chemical mixture?
–No standards (InChI?), often free-form text
•How do you describe a bioactivity?
–Emerging standards, not widely adopted
•Minimum Information About a Bioactive Entity (MIABE)
•How do you draw a chemical structure?
–IUPAC Graphical Representation Standards for Chemical Structure Diagrams
•Not widely adopted, large pre-existing corpus
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Chemical information is not easy
Image credit: http://www.ivyroses.com/Chemistry/GCSE/What-is-a-substance.php
•Most chemistry information isfor humans, not machines
•Chemists invent arbitraryconventions to communicatechemical information
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Chemical information is not designed for computers
•As a chemist, you canunderstand and recognize thatthis picture is the chemicalacetone
•You can put a chemical nameor registry identifier next to it
•Is this not good enough?
•Many names for structure
•The computer ‘sees’ a binaryimage not a structure
Acetone
67-64-1
Almost all chemical information isgeared towards human understandingin the form of text and images
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Computer understanding of chemical information
•Give a computer a chemicalstructure a (normal) humancannot understand it
•A computer can make the imagefrom the structure
•A computer can associateinformation to the structure
•A computer can generate otherkey information from structure
CC(=O)C
Acetone
67-64-1
propan-2-one
58.07914 g/mol
Computer understanding can helpprovide human understanding
If the computer understands, wecan leverage it for search, analysis,and more
•Chemical information is a bit of a mess and can be rather nuanced
–Names, names, and more names (+200M in PubChem)
•Some standard names are not open and cannot be used/verified without $$$
–Name/structure associations vary by use case (many overlapping)
•Acetic acid vs. Acetic acid tri-hydrate
•Formaldehyde: (gas) vs. Formalin (liquid, 40% formaldehyde w/ water)
•Sulfuric acid: SO3 (gas) vs. H2SO4 (liquid)
•Glucose: L/D, ring open/closed (f/p), alpha/beta/both vs. Glucose monohydrate
•Large corpus in the ‘wild’ .. data source dependent nuances
•Verify with primary source(s) prior to information use
–i.e., is this the form of the chemical I care about?
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Chemical structure is not enough
Go below 32%formaldehyde in water andit is non-flammable
Same chemical structurecan represent manythings, most of which areuse case dependent
Users are not happy tosee overlapping data ondifferent forms of samechemical
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Many data sources of relevant information
SDS Search and Product Safety Center
http://www.sigmaaldrich.com/
SIRI MSDS Index
Biological Safety Data Sheets
CHEMINDEX FREE on the WEB!
International Chemical Safety Cards (ICSC)
Right to Know Hazardous Substance Fact Sheets
Pathogen Safety Data Sheets and Risk Assessment
NIOSH Pocket Guide to Chemical Hazards
TOXLINE: A TOXNET DATABASE
SDS and Chemical Information from Manufacturers
ChemIDplus: A TOXNET DATABASE
https://www.dol.gov/
http://www.csb.gov/
Does each organization (or scientist)use their own favorite data source(s)?
Do these various data sources provideconsistent information (gaps, errors)?
How do their decisions change withdifferent information (or lack of it)?
•Many answers to “How do describe my substance andits data?”
•Information is geared towards humans
•Need computer understanding of information
•Chemical structure representation is insufficient
•Publically available chemical information is heavilyfragmented
•Lots of data links (use case dependent, relevancy, etc.)
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Chemical data issues are fundamental
WHAT ARE WE DOING ABOUT IT?
Community drive towards knowledge representation
Dear Colleagues,
We would like to thank you for taking the time to participate in our first meeting to address chemicalontologies (CO). Below please find some notes / minutes from the meeting held in Basel, Switzerland, October2, 2015.
a chemical ontology to be used for classification purposes as well as for input fordownstream operations such as knowledge graphs, data mining, chemical text mining and cognitive computingexperimentsThe purpose was to explore using computers to ingest machine-readable forms of molecules and to generatemolecular attributes (descriptors). For example, ingesting a SMILES string and producing a set of triples thatdescribe the molecule [ molecule X “is a “ ketone ; “ is a “ amino acid ; “is a “ steroid etc. ]. The output of whichwould provide the basis of a chemical ontology to be used for classification purposes as well as for input fordownstream operations such as knowledge graphs, data mining, chemical text mining and cognitive computingexperiments. Historically these operations were performed manually or semi-automated; however, it isdesirable to have a computer process for large scale processing to meet current day demands resulting fromcomputer curation of the scientific literature. To date, two programs have been developed to accomplish thisobjective: one at OntoChem, a German informatics company, and another (ClassyFire) at the University ofAlberta. While both programs produce reasonable output, there are differences that could lead to non-conformity in the resulting ontologies. One motivation for the workshop was anticipation that all parties willbenefit from common standards for a computer-derived chemical ontology. Overall we believe that aChemical Ontology can make contributions when it comes to answering scientific relevant questions.
Workshop hosted by Fatma Oezdemir-Zaech, Novartis Pharma AG
Things connect thru ontologies
A common vocabulary for researchers who need to share information in a domain.
Including machine-interpretable definitions of basic concepts in the domain
and relations among them.
Slide courtesy of Stephen Boyer, IBM
A Goal for Building Chemical Ontologies :
Step 1 ) Establish - Molecular Attributes
Step 2 ) Establish - Functional Attributes
Step 3 ) Explore - Integration & Predication
Slide courtesy of Stephen Boyer, IBM
Physical Attributes :
Examples : Molecular Weight , Melting point , Boiling Point …etc
•Molecular Attributes :
Examples: Steroid, Prostaglandin, Amino Acid, Alkene, Imidazole, .
•Functional Attributes :
Examples: Anti-Inflammatory, Explosive, Refrigerant, Pesticide
There are many types of : “ Attributes “
Slide courtesy of Stephen Boyer, IBM
Consider this molecule
Slide courtesy of Yannick Djoumbou & David Wishart / Drugbank team / U of Alberta
Carboxylic acid
Slide courtesy of Yannick Djoumbou & David Wishart / Drugbank team / U of Alberta
Carboxylic acid
Benzoic acid
Slide courtesy of Yannick Djoumbou & David Wishart / Drugbank team / U of Alberta
Carboxylic acid
Benzoic acid
Phenol
Slide courtesy of Yannick Djoumbou & David Wishart / Drugbank team / U of Alberta
Carboxylic acid
Benzoic acid
Phenol
Hydroxy group
Slide courtesy of Yannick Djoumbou & David Wishart / Drugbank team / U of Alberta
Carboxylic acid
Benzoic acid
Phenol
Hydroxy group
Azo
Slide courtesy of Yannick Djoumbou & David Wishart / Drugbank team / U of Alberta
Carboxylic acid
Benzoic acid
Phenol
Hydroxy group
Azo
Pyridine
Slide courtesy of Yannick Djoumbou & David Wishart / Drugbank team / U of Alberta
Carboxylic acid
Benzoic acid
Phenol
Hydroxy group
Azo
Pyridine
Sulfone
Slide courtesy of Yannick Djoumbou & David Wishart / Drugbank team / U of Alberta
Carboxylic acid
Benzoic acid
Phenol
Hydroxy group
Azo
Pyridine
Sulfone
Sulfonamide
Slide courtesy of Yannick Djoumbou & David Wishart / Drugbank team / U of Alberta
Carboxylic acid
Benzoic acid
Phenol
Hydroxy group
Azo
Pyridine
Sulfone
Sulfonamide
Azobenzene
Slide courtesy of Yannick Djoumbou & David Wishart / Drugbank team / U of Alberta
Carboxylic acid
Benzoic acid
Phenol
Hydroxy group
Azo
Pyridine
Sulfone
Sulfonamide
Azobenzene
Benzene
Slide courtesy of Yannick Djoumbou & David Wishart / Drugbank team / U of Alberta
Carboxylic acid
Benzoic acid
Phenol
Hydroxy group
Azo
Pyridine
Sulfone
Sulfonamide
Azobenzene
Benzene
Molecular Attributes (Labels)
Is a Benzoic acid
Is a Carboxylic acid
Is a Carbonyl cpd
Is a Phenol
Is aAxobenzene
Is a Azo compound
Is a sulfone
Is a Sulfonamide
Is aPyridine
Is aBenzene
Is a hydroxy
Slide courtesy of Yannick Djoumbou & David Wishart / Drugbank team / U of Alberta
Carboxylic acid
Benzoic acid
Phenol
Hydroxy group
Azo
Pyridine
Sulfone
Sulfonamide
Azobenzene
Benzene
Functional Attributes
Is used for the treatment of Crohn's disease
Is used for the treatment of rheumatoid arthritis
Is used for the treatment of ulcerative colitis
Molecular Attributes (Labels)
Is a Benzoic acid
Is a Carboxylic acid
Is a Carbonyl cpd
Is a Phenol
Is aAxobenzene
Is a Azo compound
Is a sulfone
Is a Sulfonamide
Is aPyridine
Is aBenzene
Is a hydroxy
Slide courtesy of Yannick Djoumbou & David Wishart / Drugbank team / U of Alberta
SMILES
Name
Molecular Attributes / Labels
Molecular Attributes / labels
Operation A
Operation B
Structure
Name
Slide courtesy of Stephen Boyer, IBM
SMILES
Name
Molecular
Labels
Molecular
Labels
Operation A
ClassyFire
Structure
Name
Molecular
Labels
OntoChem
Molecular
Labels
Other
Ex : Derwent Frag Codes, ChEBI, MeSH
Normalization Process – compatible with rules of chemical nomenclature
Slide courtesy of Stephen Boyer, IBM
Name
Molecular Descriptors / Attributes / Labels
2-[(1S,2S,4R,8S,9S,11S,12R,13S,19S)-12,19-difluoro-11-hydroxy-6,6,9,13-tetramethyl-16-oxo-5,7-dioxapentacyclo[10.8.0.0^{2,9}.0^{4,8}.0^{13,18}]icosa-14,17-dien-8-yl]-2-oxoethyl acetate
2-[(1S,2S,4R,8S,9S,11S,12R,13S,19S)-12,19-difluoro-11-hydroxy-6,6,9,13-tetramethyl-16-oxo-5,7-dioxapentacyclo[10.8.0.0^{2,9}.0^{4,8}.0^{13,18}]icosa-14,17-dien-8-yl]-2-oxoethyl acetate
Slide courtesy of Stephen Boyer, IBM
[H][C@@]12C[C@@]3([H])[C@]4([H])C[C@]([H])(F)C5=CC(=O)C=C[C@]5(C)[C@@]4(F)[C@@H](O)C[C@]3(C)[C@@]1(OC(C)(C)O2)C(=O)COC(C)=O
SMILES String
ClassyFire
OntoChem
ClassyFire: Halogenated steroids (6); Fluorohydrins (7); Halohydrins (7); 1,3-dioxolanes (9);11-beta-hydroxysteroids (9); Dioxolanes (9); 3-oxo delta-1,4-steroids (10); Alpha-acyloxyketones (10); Delat-1,4-steroids (10); 11-hydroxysteroids (12); Gluco/mineralcorticoids,progestogins and derivatives (13); Pregnane steroids (13); 20-oxosteroids (15); Acetatesalts (22); 3-oxosteroids (26); Oxosteroids (27); Carboxylic acid salts (30); Hydroxysteroids(32); Cyclic ketones (45); Alpha amino acid amides (73); Pyrrolidines (80); D-alpha-aminoacids(85); Cyclic ketones (45); Acetals (50); Steroids and steroid derivatives (51); Alkylfluorides (53); Alkyl halides (67); Cyclic alcohols and derivatives (86); Ketones (101);Organofluorides (128); Carboxylic acid esters (139); Secondary alcohols (187); Oxacycliccompounds (192); Lipids and lipid-like molecules (209); Organohalogen compounds (272);Ethers (393); Alcohols and polyols (395); Carboxylic acid derivatives (423); Carboxylic acidsand derivatives (548); Carbonyl compounds (598); Organic acids and derivatives (633);Organoheterocyclic compounds (651); Organooxygen compounds (856); Organiccompounds (978); Chemical entities (989); Hydrocarbon derivatives (995);
OntoChem: 17-deoxy-prednisolones (6); halohydrins (6);prednisolones (6); ethanoic acid esters (20); methyl esters(20); acetals (37); alkyl fluorides (56); cyclic ketones (61);natural product derivatives (92); fluorine compounds(126); alkene derivatives (172); polycyclic compounds(184); oxacyclic compounds (190); secondary alcohols(202); carboxylic acids (249); formic acid derivatives (559);lipophilic molecules (642); lipinski molecules (785);bioavailable molecules (867); oxygen compounds (891);small molecules (949); carbon compounds (974); heterocompounds (978);
Slide courtesy of Stephen Boyer, IBM
Green means likely synonym between ontologies
Its’ an olefin
Its’ an alkene
Slide courtesy of Yannick Djoumbou & David Wishart / Drugbank team / U of Alberta
•Enables drill down to chemical lists with aclassified feature of interest
•Allows interesting cross comparison betweenclassification systems
–For example, compare MeSH with ChEBI
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Classifications in PubChem
Of the 38 benzaldehydesin ChEBI, how many arewithin the set of 128benzaldehyes in MeSH?
Only 20 between the two are in common
Only 19 of 20 are considered aldehydes
Only 17 of 19 are considered benzaldehydes!
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Computational vs. Manual Classification
•Humans make mistakes
–This includes programming mistakes,classification mistakes, encodingmistakes
•Manual classifications only handle asmall number of chemical structures
–Automated classification can help toextend classifiers to all knownchemicals
•Harmonization of terminology?
–Can they speak a common language?
Image credit: https://commons.wikimedia.org/wiki/File:Brueghel-tower-of-babel.jpg
Benzene boiling point case study
Benzene
Coal tar
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Gasoline
•ILO 1400 is for gasoline. There are threenames [Gasoline, Benzin, 86290-81-5].The corresponding MeSH concept isM0008998 with single name ‘Gasoline’.
•The ILO term ‘86290-81-5’ is not in MeSH.The other ILO term ‘Benzin’ is located inanother concept M0045647 with names‘naphtha, O3L624621X, 8030-30-6,benzin, benzine, petroleum ether’.
•OSHA 706 is for naptha. There are several names[Naptha, Naptha (coal tar), high solvent naptha,crude solvent coal tar naptha, 8030-30-6]. This mapsto MeSH concept M0045647 , with names [naphtha,O3L624621X, 8030-30-6, benzin, benzine, petroleumether].
•There is a second record for naptha from OSHA, 707,which is a narrower concept to OSHA 706 above.OSHA 707 has several names [naptha, petroleumether, petroleum spirit, painters naphtha, refinedsolvent naptha, 8032-32-4, ligroin]. These namesmap to the MeSH concept M0045647 via ‘petroleumether’ but also M0060581 with names [ligroin, 8032-32-4].
Computational harmonization of chemical concepts[comparison of MeSH, WHO ILO ICSC, OSHA concepts]
Naptha
Harmonization suggestions to improve MeSH
1.Reg# ‘86290-81-5’ to be added to the gasoline concept M0008998.
2.M0060581 to be a related concept (narrower) to M0045647.
3.name ‘petroleum ether’ be moved from M0045647 to M0060581.
4.names [petroleum spirit, painters naphtha, refined solvent naptha]to be added to M0060581
5.name [phenyl hydride] to be added to M0002332.
6.M0008998 to be a related concept (narrower) to M0045647.
•Knowledge representation in chemistry enables computerunderstanding of the domain
•Community efforts are underway to help build and harmonizechemical ontologies
•There are many opportunities to improve the quality,quantity, variety, relevancy, and integration of (open) chemicaldata and chemical knowledge
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Summary
PubChem Crew …
Evan Bolton
Jie Chen
Tiejun Cheng
Gang Fu
Renata Geer
Asta Gindulyte
Lianyi Han
Jane He
Siqian He
Sunghwan Kim
Ben Shoemaker
Paul Thiessen
Jiyao Wang
Yanli Wang
Bo Yu
Leonid Zaslavsky
Jian Zhang
Special thanks to the NCBI Help Desk, especially RanaMorris, and past PubChem group members.
Steve Bryant (PI)
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•Chemical Ontology Collaborators (including Stephen Boyer, Yannick Djoumbou, Lutz Weber)
•PubChemRDF Collaborators
–Especially:
•Janna Hastings (European Bioinformatics Institute), Michel Dumontier (Stanford University), Colin Batchelor (RoyalSociety of Chemistry), Egon Willighagen (Maastricht University)
•Stephan Schurer, Uma Vempati, and Hande Küçük (U. of Miami)
–NLM Linked Data Infrastructure Working Group (MeSH RDF)
•Chemical Health and Safety collaborators
–Especially: Leah McEwen (Cornell U.), Ralph Stuart (Keene State College), Ye Li (U. of Michigan)
•Software collaborators
–NextMove Software (Roger Sayle, Daniel Lowe, Noel O’Boyle, John May)
–Xemistry GmbH (Wolf D. Ihlenfeldt)
–OpenEye Scientific Software
•All PubChem Contributors and Collaborators
•This research was supported [in part] by the Intramural Research Program of the NIH, NationalLibrary of Medicine.
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Special thanks
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Have anyquestions?
