Approaches for extraction and digital chromatography of chemical data
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Approaches for extraction and digital chromatography of chemical data



The traditional perception of the publishing process has been that it culminates in a print article. The Royal Society of Chemistry (RSC) has for many years been acutely aware that there is a wealth ...

The traditional perception of the publishing process has been that it culminates in a print article. The Royal Society of Chemistry (RSC) has for many years been acutely aware that there is a wealth of information contained in scientific communications that we publish and that its true value can only be unlocked by enabling the discovery of the data within them. This is challenging due to the variety of ways that scientists provide data, textually, graphically, and increasingly in supplementary information. This talk will outline how the RSC has applied innovative approaches, developed both internally and externally, to identifying important chemical data within the literature and provides tools to anyone using chemical data to analyse and improve its quality. Examples will include: Project Prospect, the Experimental Data Checker, our CIF data importer, ChemSpider and our structure validation and standardization service.

This presentation was given by David Sharpe at the ACS Fall Meeting 2012 in Philadelphia



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Approaches for extraction and digital chromatography of chemical data Approaches for extraction and digital chromatography of chemical data Presentation Transcript

  • Approaches for extraction and “digitalchromatography” of chemical data:A perspective from the RSC
  • Overview• Introduction – What data can we consider? – What are the challenges – What data and sources does the RSC have? – Experimental Data Checker• Case Studies: – Project Prospect – Chair forms of Sugars/cyclohexanes
  • Traditional ChromatographyImages taken from:
  • Why Digital Chromatography?• Useable information is mixed in with description and analysis – Makes it difficult to find• Despite our best efforts – still lots of ambiguous or plain wrong/unusable chemical information• Why? – Human error – Processing errors – Incorrect usage of data generation/extraction – Style over meaning – Data not generated with reuse in mind – Data generated for humans
  • Style/Layout Vs Meaning• Structures drawn to illustrate more than just the identity• Data not generated with reuse in mind• Author practices• Mixed 2D and perspective representations• Unintentional definition of stereochemistry
  • Data generated for humans• Separated/Orphaned information inc. Markush structures, information passed by reference
  • What chemical data can we consider?• Chemistry is an especially challenging - wide range of types of data – Numeric data – Names – Structures – Terminology• Over a hugely different set of topics: Org, Inorg, Physical – Meanings/interpretations are not perfectly aligned• Application of standards can be challenging• Drawing conventions – are documented but not used
  • What chemical data and sources does the RSC have?
  • A beginning: helping chemists review their own workAmphidinoketide ITo a solution of…….…. Amphidinoketide I was isolated as a ……..[α]D25 −17.6 (c 0.085, CH2Cl2); Rf = 0.61 (1:1 hexane:ethyl acetate); νmax(CHCl3)/cm−1 1707.2 (CO), 1686.9 (CO), 1632.4 (CO), 1618.9 (CC), 1458.1; 1H NMR (CD2Cl2, 500 MHz) δH 6.08 (1H, t, J = 1.3 Hz, 3-CHC), 5.82 (1H, ddt, J = 16.9, 10.2, 6.7 Hz, 19-CHCH2), 4.99 (1H, m (17.1 Hz), 20-CHA), 4.92 (1H, m (10.2 Hz), 20-CHB), 3.05 (1H, dd, J = 17.9, 9.3 Hz, 8-CHA), 3.00–2.90 (3H, m, 9-CHCH3, 11-CHA, 12-CHCH3), 2.72–2.64 (2H, m, 5-CHA, 6-CHA), 2.62–2.55 (2H, m, 5-CHB, 6-CHB), 2.51–2.45 (3H, m, 8-CHB, 11-CHB, 14-CHA), 2.33 (1H, dd, J = 16.9, 7.4 Hz, 14-CHB), 2.09 (3H, s, 21-CH3), 2.05–1.99 (2H, m, 18-CH2), 1.99–1.96 (1H, m, 15-CHCH3), 1.88 (3H, s, 1-CH3), 1.39–1.25 (3H, 17-CH2, 16-CHA), 1.14–1.10 (1H, m, 16-CHB), 1.07 (3H, d, J = 7.0 Hz, 22-CH3), 1.05 (3H, d, J = 7.2 Hz, 23-CH3), 0.87 (3H, d, J = 6.7 Hz, 24-CH3); 13C NMR (CD2Cl2, 125 MHz) δC 213.15 (13-CO), 212.08 (10-CO), 208.40 (7-CO), 198.76 (4- CO), 155.40 (2-CCH), 138.41 (19-CHCH2), 123.54 (3-CHC), 114.19 (20-CH2C), 48.81 (14-CH2), 45.93 (11-CH2), 44.50 (8-CH2), 41.43 (9-CHCH3), 41.01 (12-CHCH3), 37.74 (5-CH2), 36.55 (16-CH2), 36.27 (6- CH2), 34.18 (18-CH2), 28.74 (15-CHCH3), 27.57 (1-CH3), 26.60 (17-CH2), 20.63 (21-CH3), 19.77 (24- CH3), 16.65 (22 or 23-CH3), 16.62 (22 or 23-CH3); HRMS (ESI) Calculated for C24H38O4 413.2668, found 413.26600 (MNa+). (9R, 12R, 15S)-1 had [α]D25 +11 (c 0.245, CH2Cl2).•
  • Case study 1: Project Prospect
  • What is Prospect?Visible output Enhanced Prospect InChI–name pairs Better Output layer RSS HTML database (in ChemSpider) ontologies Information layer Enhanced RSC XML Tool layer OSCAR InChI–Name pairs Author Input layer Ontologies RSC XML (from ChemSpider) CDX files 12
  • People and machines People MachinesCan understand narratives. Can’t understand narratives.Can interpret pictures. Can’t interpret pictures.Can reason about three- Not able to infer 3D structure dimensional objects. from 2D without cues.Can do a high-quality job. Can do a lower-quality, but still useful job.
  • Case study 2: The chair representation issue InChI=1S/C6H12O6/c7-1-2-3(8)4(9)5(10)6(11)12-2/h2-11H,1H2 WQZGKKKJIJFFOK-UHFFFAOYSA-N• 5 stereocentres = 2^5 isomers =32 structures
  • Case study 2: Chair forms ofhexacycles what could go wrong?
  • How do we “fix” chair-representationsHow we normalize them:1. Identify 6-membered rings (Indigo)2. Identify what sort of ring it is3. Map atoms onto a standard structure (eg. beta-D-glucopyranose)4. Tidy
  • The future: “The digester”• Ability to: – Reconnect R-groups – Expand abbreviations – Expand brackets – Link structures with reference IDs
  • Other examples that we didn’t mention in case studies• CIF data importer• Structure Validation and Standardisation – (Thurs Aug 23, 9:15 am, Marriott Downtown, Franklin Hall 6)• Work on creation of ontologies, RXNO, CMO – Also collaborating on: ChEBI ontology, GO, SO• Collaboration with Utopia to enable Prospect mark-up of PDFs
  • Summary• Many data sharing practices are based on: – Traditional print articles – Consumption of data by humans only• This poses issues for publishers and users alike• The RSC is developing innovative solutions to address some of these problems – Chemical structures are challenging – Limitations to what a machine methods can achieve – Need to educate authors to think differently
  • Acknowledgements• Colin Batchelor - Development and Technical work• Jeff White & Aileen Day• Richard Kidd, Graham McCann and Will Russell• RSC ICT staff
  • Thank youEmail: chemspider@rsc.orgTwitter: @ChemSpider