Automated Extraction of Chemical Information from the Scientific Literature

ChemDataExtractor and other tools for chemical text mining.

Transcript

1. Automated Extraction of
   Chemical Information from
   the Scientific Literature
   Ma# Swain
   Cavendish Laboratory, University of Cambridge
   Vernalis (R&D) Ltd
   

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2. Cambridge CheminformaAcs MeeAng 07/12/2016
   Ma# Swain
   Overview
   • ChemDataExtractor: Open source toolkit
   • Natural language processing
   • HolisAc informaAon extracAon
   • Text mining at Vernalis
   2
   

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3. Cambridge CheminformaAcs MeeAng 07/12/2016
   Ma# Swain
   The Goal
   3
   name 2-[2-[4-(dimethylamino)phenyl]diazenyl]-benzoic acid
   label 3a
   uv-vis
   - solvent acetonitrile
   - wavelength 448 units nm
   - extinction 29000 units M-1cm-1
   - apparatus Agilent8453 diode array spectrophotometer
   Figure 2: UV-vis absorption spectra of 3a in acetonitrile.
   UV-vis spectra were recorded using an Agilent8453 diode array spectrophotometer.
   λ
   max
   /nm (ε/M−1 cm−1)
   3b
   448 (29,000)
   Dye
   3a
   415 (48,000)
   The dye 2-[2-[4-(dimethylamino)phenyl]diazenyl]-benzoic acid (3a) was added…
   

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4. Cambridge CheminformaAcs MeeAng 07/12/2016
   Ma# Swain
   System Overview
   4
   Tables
   Abstract
   PDF
   HTML
   XML
   Full Text
   Database
   CapAons
   Document
   Readers
   Table
   Processor
   Natural
   Language
   Processor
   Interdependency
   Resolver
   

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5. Cambridge CheminformaAcs MeeAng 07/12/2016
   Ma# Swain
   Document Model
   • Process all input formats into a consistent internal document model
   • Publisher-specific (ACS, RSC, USPTO) and generic format readers
   • Result: Simplified linear stream of document elements
   5
   PDF
   HTML
   XML
   Title
   Abstract
   Heading
   Paragraph
   Table
   …
   Document
   Document
   Readers
   

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6. Cambridge CheminformaAcs MeeAng 07/12/2016
   Ma# Swain
   Natural Language Processing
   6
   • All text-based elements make use of the same natural
   language processing pipeline:
   • Variety of techniques: Rules, DicAonaries, Machine
   Learning
   Sentence
   Spli`ng
   Word
   TokenizaAon
   Part-of-speech
   Tagging
   EnAty
   RecogniAon
   Phrase
   Parsing
   

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7. Cambridge CheminformaAcs MeeAng 07/12/2016
   Ma# Swain
   Tokenization
   7
   • Split sentences using Punkt method
   • Custom rule-based word tokenizer
   • NormalizaAon performed on the content of each token
   Fig. 3 m.p. 185 °C M. Swain et al. equiv.
   1.8 mg
   ~
   ~1.8mg
   (+/−)-chiraline
   (+/−)-chiraline
   buffer (pH7.4) ( pH
   buffer 7.4 )
   

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8. Cambridge CheminformaAcs MeeAng 07/12/2016
   Ma# Swain
   Part-Of-Speech Tagging
   8
   • Assign a category to each token - 47 tags in total
   • CRF trained on newspaper arAcles (WSJ) and
   biomedical abstracts (GENIA)
   • Used as a feature in machine learning enAty
   recogniAon
   • Used to construct parsing rules
   NN Noun
   NNS Plural Noun
   JJ Adjec/ve
   CD Cardinal Number
   CC Conjunc/on
   DT Determiner
   VB Verb
   FW Foreign Word
   >>> cpt = ChemCrfPosTagger()
   >>> cpt.tag(['NMR', 'spectra', 'were', 'recorded', 'on', 'a', '300', 'MHz', 'BRUKER', ‘spectrometer'])
   [('NMR', 'NN'), ('spectra', 'NNS'), ('were', 'VBD'), ('recorded', 'VBN'), ('on', 'IN'),
   ('a', 'DT'), ('300', 'CD'), ('MHz', 'NNP'), ('BRUKER', 'NNP'), ('spectrometer', ‘NN')]
   

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9. Cambridge CheminformaAcs MeeAng 07/12/2016
   Ma# Swain
   Entity Recognition
   9
   • Tagging chemical names using “IOB” scheme:
   • Combined dicAonary, machine learning and regular
   expression methods
   • Lowe & Sayle (2015) J. Cheminf. - DicAonary creaAon techniques
   • AbbreviaAon DetecAon
   with dimethyl sulfoxide as solvent
   O B-CM I-CM O O
   Tetrahydrofuran ( THF )
   B-CM O B-CM O
   

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10. Cambridge CheminformaAcs MeeAng 07/12/2016
    Ma# Swain
    • Brown clustering: Hierarchical clustering of words
    • Bitstring prefixes used as features in CRF to improve POS
    tagging and enAty recogniAon performance
    Word Clusters
    10
    000000000: ethanol, methanol, acetone, glycerol, brine
    0000000010: THF, DMSO, CH2Cl2, toluene, DMF
    0000000011: H2O, MeOH, EtOAc, hexane, TFA
    0
    1
    1
    1111000001110: subs@tuted, func@onalized, terminated
    1111000001111: conjugated, interac@ng, cross-linked, fused
    10011011100: NPs, NCs, NRs, NWs, NTs, NSs, NBs
    0
    1
    10011011101: nanopar@cles, nanocrystals, nanowires
    0
    1
    0
    1
    0
    0
    1
    h#ps:/
    /github.com/percyliang/brown-cluster
    

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11. Cambridge CheminformaAcs MeeAng 07/12/2016
    Ma# Swain
    Rule-Based Parsing
    11
    • Parsers transform tagged tokens into a tree structure
    • Rules defined in Python code from building blocks:
    • W: Match word text
    • T: Match POS tag or enAty tag
    • R: Match regular expression
    • And, Or, ZeroOrMore, OneOrMore, OpAonal, …
    • Built in parsers: Chemical Names, MelAng Points, Quantum Yield,
    Fluorescence lifeAmes, NMR, UV-vis, IR spectra, etc.
    name = T('B-CM') + ZeroOrMore(T('I-CM'))
    label = R('[1-9][a-z]?') | R('[IVX]+')
    cem = name + W('(') + label + W(')')
    cem
    name label
    1,2-dihydroxybenzene 3a
    

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12. Cambridge CheminformaAcs MeeAng 07/12/2016
    Ma# Swain
    Table Processing
    • Process each table cell with a specialised natural language
    processing pipeline
    • Parse headings to determine column type and extract contextual
    informaAon
    • Parse cells in each row to extract values
    • Merge contextual informaAon from capAon, footnotes, and headings
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13. Cambridge CheminformaAcs MeeAng 07/12/2016
    Ma# Swain
    Data Schema
    13
    Compound
    name
    label
    role
    mel/ng points
    uvvis spectra
    Melting Point
    value
    units
    UV-vis Spectrum
    peaks
    solvent
    temperature
    apparatus
    Peak
    wavelength
    ex/nc/on
    shape
    

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14. Cambridge CheminformaAcs MeeAng 07/12/2016
    Ma# Swain
    Interdependency Resolution
    14
    • Assign orphan properAes to relevant compound
    • Merge equivalent chemical idenAfiers to a single record
    • Assign global contextual informaAon
    {"name": "4-(10-phenylanthracene-9-yl)pyridine", "role": "product"}
    {"melting_points": [{"value": "280.7", "units": "°C"}]}
    {"name": "4-(10-phenylanthracene-9-yl)pyridine", "label": "3a"}
    {"label": "3a", "melting_points": [{"value": "280.7", "units": "°C"}]}
    {"uv_vis": [{"solvent": "acetonitrile", "apparatus": "Agilent 8453 spectrometer"}]}
    

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15. chemdataextractor.org
    

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16. chemdataextractor.org
    

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17. chemdataextractor.org
    

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18. chemdataextractor.org
    

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19. Cambridge CheminformaAcs MeeAng 07/12/2016
    Ma# Swain
    • ExperAse
    • Fragments and structure-based drug discovery
    (Protein Science, Structural Biology, Chemistry)
    • TherapeuAc areas
    • Oncology, CNS, infecAous diseases
    • LocaAon
    • Based in Granta Park, just outside Cambridge
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20. Cambridge CheminformaAcs MeeAng 07/12/2016
    Ma# Swain
    Text Mining at Vernalis
    • Patents: USPTO, WIPO and EPO
    • NextMove Sotware: spelling correcAon, translaAon,
    reacAon extracAon
    • PatSearch: In-house patent searching and alerAng
    plauorm
    • PatExplore: Patent chemistry explorer
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21. Cambridge CheminformaAcs MeeAng 07/12/2016
    Ma# Swain
    PatSearch
    • Python Flask web app with Celery task queues for
    downloading, processing, indexing patents
    • ElasAcSearch index with ReactJS frontend
    • Extract enAAes using LeadMine and store in database
    • From publicaAon to a scienAst’s inbox in under 1 hour
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25. ChemDataExtractor Info
    • chemdataextractor.org - Try it out!
    • Python package:
    • JCIM ArAcle:
    $ pip install chemdataextractor
    $ conda install -c chemdataextractor chemdataextractor
    ChemDataExtractor: A Toolkit for Automated Extraction of Chemical
    Information from the Scientific Literature
    Matthew C. Swain and Jacqueline M. Cole*
    Cavendish Laboratory, University of Cambridge, J. J. Thomson Avenue, Cambridge, CB3 0HE, U.K.
    ABSTRACT: The emergence of “big data” initiatives has led
    to the need for tools that can automatically extract valuable
    chemical information from large volumes of unstructured data,
    such as the scientific literature. Since chemical information can
    be present in figures, tables, and textual paragraphs, successful
    information extraction often depends on the ability to interpret
    all of these domains simultaneously. We present a complete
    toolkit for the automated extraction of chemical entities and
    their associated properties, measurements, and relationships
    from scientific documents that can be used to populate
    Article
    pubs.acs.org/jcim
    

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