digitizing documents to provide a public spectroscopy database

Digitizing documents to provide a public spectroscopy database

Antony Williams, Colin Batchelor, William Brouwer and Valery Tkachenko

ACS Indianapolis

How can we digitize documents?

• As a publisher we would LOVE to bring data out of our historical archive

• What could we do?• Find chemical names and generate structures• Find chemical images and generate structures• Find reactions – and make a database!• Find data (MP, BP, LogP) and deposit• Find figures and database them• Find spectra (and link to structures)

DERA

• Data enabling the RSC Archive

• Data extraction from the RSC Archive

• Difficult enhancements of the RSC Archive!!!

Text Mining

The N-(β-hydroxyethyl)-N-methyl-N'-(2-trifluoromethyl-1,3,4-thiadiazol-5-yl)urea prepared in Example 6 , thionyl chloride ( 5 ml ) and benzene ( 50 ml ) were charged into a glass reaction vessel equipped with a mechanical stirrer , thermometer and reflux condenser .

The reaction mixture was heated at reflux with stirring , for a period of about one-half hour .

After this time the benzene and unreacted thionyl chloride were stripped from the reaction mixture under reduced pressure to yield the desired product N-(β-chloroethyl)-N-methyl-N'-(2-trifluoromethyl-1,3,4-thiaidazol-5-yl)urea as a solid residue

Text Mining

The N-(β-hydroxyethyl)-N-methyl-N'-(2-trifluoromethyl-1,3,4-thiadiazol-5-yl)urea prepared in Example 6 , thionyl chloride ( 5 ml ) and benzene ( 50 ml ) were charged into a glass reaction vessel equipped with a mechanical stirrer , thermometer and reflux condenser .

The reaction mixture was heated at reflux with stirring , for a period of about one-half hour .

After this time the benzene and unreacted thionyl chloride were stripped from the reaction mixture under reduced pressure to yield the desired product N-(β-chloroethyl)-N-methyl-N'-(2-trifluoromethyl-1,3,4-thiaidazol-5-yl)urea as a solid residue

Text-Mining

How is DERA going?

• We are working on 21st articles first

• Mostly marked up with XML, more structured, easier to handle

• 8.2Gbytes of data, >100k articles from 2000-2013

• Markup will be published onto the HTML forms of the articles

• We will iterate based on dictionaries, markup, OSCAR extraction

ChemSpider Reactions

ChemSpider Reactions

Structure Extraction from Images

• Structure extraction from images is old technology. It’s difficult!• Commercial and Open Source tools

• CLiDE• OSRA• Imago• Lots of others

Detailed analysis and test sets

• Detailed analysis from GGA : http://ggasoftware.com/imago/report/report.html

ESI – Text Spectra

Lots of “Textual Spectra”

Do we want to search text spectra?

What do we get when we search:

13C NMR (CDCl3, 100 MHz): δ = 14.12 (CH3), 30.11 (CH, benzylic methane), 30.77 (CH, benzylic methane), 66.12 (CH2), 68.49 (CH2), 117.72, 118.19, 120.29, 122.67, 123.37, 125.69, 125.84, 129.03, 130.00, 130.53 (ArCH), 99.42, 123.60, 134.69, 139.23, 147.21, 147.61, 149.41, 152.62, 154.88 (ArC)

1 Hit. Yay!

Reality

• No one will ever have perform a “spectral search” based on text searching!

• From sample to sample, solvents, concentration, temperature will change peak positions. The chance of even the same peak list is tiny.

• Reality need is a “spectral database” where search algorithms deal with peak positions, intensities, multiplicity when appropriate

Text and Images Spectra into “Real Spectra”?

• We can turn text into structures

• We can turn images into structures

• So is it possible to turn text into spectra?

MestreLabs Mnova NMR Beta

1H NMR (CDCl3, 400 MHz): δ = 2.57 (m, 4H, Me, C(5a)H), 4.24 (d, 1H, J = 4.8 Hz, C(11b)H), 4.35 (t, 1H, Jb = 10.8 Hz, C(6)H), 4.47 (m, 2H, C(5)H), 4.57 (dd, 1H, J = 2.8 Hz, C(6)H), 6.95 (d, 1H, J = 8.4 Hz, ArH), 7.18–7.94 (m, 11H, ArH)

13C NMR (CDCl3, 100 MHz): δ = 14.12 (CH3), 30.11 (CH, benzylic methane), 30.77 (CH, benzylic methane), 66.12 (CH2), 68.49 (CH2), 117.72, 118.19, 120.29, 122.67, 123.37, 125.69, 125.84, 129.03, 130.00, 130.53 (ArCH), 99.42, 123.60, 134.69, 139.23, 147.21, 147.61, 149.41, 152.62, 154.88 (ArC)

Text Conversion Approaches

• Work in progress but early observations• Converted spectra are NOT what would be

seen in the data• They are commonly GOOD approximations

of C13 spectra (except intensity)• They are average BUT useful approximations

of H1 spectra – couplings are tough, dispersion of spectra, overlaps etc.

• We need to figure out workflows, structure associations, storage in ChemSpider

It’s exactly the WRONG WAY!

• We should NOT be mining data out of future publications

• Structures should be submitted “correctly”

• Spectra should be digital spectral formats, not images

• ESI should be RICH and interactive

ESI – Text and Image Spectra

ESI – Text and Image Spectra

Extracted JCAMP Spectrum

Turn “Figures” Into Data

Plot2Txt (p2t)

Plot2txt.com (p2t) proprietary cloud based service for fast large scale document content extraction

Figures in technical documents are recognized and converted into text, CSV and other formats eg., JCAMP without human intervention.

Extracted data suitable for storage/indexing, further reuse

What’s the process?

Input : PDF document collection, split into pages, handed to p2t instances and processed

Output : Spectra in JCAMP/CSV, molecules in BMP images

pdf

page

page

p2t

p2t

Test Experiments

Input : 74 supplementary data documents/ 3444 pages Output : p2t extracted content in 1069 page instances

578 molecules ~ 10% false positives eg., classifies Bruker logo as

chemical object ~ 20% false negatives eg., missing some symbols

from structure

1151 spectra > 80% of peaks extracted to within 1-2 decimal

places (ppm)

Performance

Plot2Txt output: processed on average 1.4 M pixels / second / CPU core

(Intel i7, O3 optimization in compilation) 2 hours for 1069 pages, in serial

0

0.5

1

1.5

2

2.5

0 200 400 600 800 1000

M p

ixe

ls / s

eco

nd

page number

Analysis Process

• Manual examination….viewing spectra, one at a time, and comparing extracted JCAMP versus image (TIME!)

• Generally excellent results for high S/N – small/close peaks can be lost

• Spectrum is “representative enough” and way more useful than just images for indexing and searching

• Structure association MUST be checked but name-structure association can be used

Prepare CONSISTENT JCAMP

Data onto ChemSpider

Summary

Plot2txt does recognize and extract content Rapid and increasingly accurate process Fails in low resolution cases, some fine

structure in spectra is lost

Structure recognition is NEW needs some work in order to lower false negatives

Future data checking opportunity

• How will we check data consistency?

• How do we know the structure and the spectra match? Comparing image to spectrum is NOT enough!!!

• Predict spectra, use spectral verification, use algorithmic checking.

• Flag “dodgy data” and use crowdsourcing for data checking – If 10,000 spectra online are 5% in error are they useful???

Future Work

• We can EASILY find text spectra in articles but have work to do regarding:• Pipelining of work and structure association• Non-truncation from wordwrapping

• We can quite easily find spectra based on Figure Legends and have work regarding• Pipelining of work and structure association

• Validation of structure-spectrum association• Data curation

Grand Target

• I want ALL 21st century spectra converted and in ChemSpider in one year

• I REALLY want scientists to get the value of real data over image data in terms of ESI

• I want authors to have data validation via our web services

• We will support IR, Raman, UV-Vis, 1D NMR and 2D…yet to come!

Acknowledgments

• Bill Brouwer – Plot2Txt.com live in 2 weeks

• Carlos Cobas and Santi Dominguez

• Colin Batchelor and Peter Corbett – OSCAR, text mining, dictionaries, markup

• Valery Tkachenko, Alexey Pshenichnov and Richard Gay – ChemSpider Reactions

• Daniel Lowe – ChemSpider Reactions data

• ACD/Labs – Provider of spectroscopy tools