Chemical entity extraction using the chemicalize org technology
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Chemical Entity extraction using the chemicalize.org-technology. Josef Scheiber Novartis Pharma AG – NITAS/TMS. Where the story of this project started . A day in October 2008 Some time around 7:45 in the morning . Novartis Campus. Dreirosenbrücke.

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Chemical entity extraction using the chemicalize org technology

Chemical Entity extraction using the chemicalize.org-technology

Josef Scheiber

Novartis Pharma AG – NITAS/TMS


Where the story of this project started

Where the story of this project started ...

A day in October 2008

Some time around 7:45

in the morning ...

Novartis Campus

Dreirosenbrücke


Vision for textmining integration chemical biological knowledge

Vision for textminingIntegration chemical, biological knowledge


Mining for chemical knowledge rationale

Mining for Chemical Knowledge - Rationale

  • Make text corpora searchable for chemistry

  • Generate chemistry databases for use in research based on Scientific Papers or Patents

  • Link Chemical Information with further annotation in an automated way for e.g. Chemogenomics applications

  • Patent analyis for MedChem projects

Connection table


Mining for chemical knowledge rationale1

Mining for chemical Knowledge - Rationale

Information on compounds targeting GPCRs

HELP

Information explosion

Source: Banville, Debra L. “Mining chemical structural information from the drug literature.” Drug Discovery Today, Number 1/2 Jan. 2006, p.35-42


Example project prospect royal society of chemistry

Example:Project Prospect – Royal Society of Chemistry

  • Enhancing Journal Articles with Chemical Features

This helps you identifying other articles talking about the same molecule


Mining for chemical knowledge focus for today

Mining for Chemical Knowledge – Focus for today

  • Make text corpora searchable for chemistry

  • Generate chemistry databasesfor use in research based onScientific Papers or Patents

  • Link Chemical Information with further annotation in an automated way for e.g. Chemogenomics applications

  • Patent analyis for MedChem projects

Connection table


A use case for successful patent mining molecules you sometimes find in your inbox

A use case for successful patent mining(molecules you sometimes find in your inbox ;-) )

Vardenafil

(2003, Bayer) – € 1.24 billion (USD 1.6 billion)

Sildenafil (1998, Pfizer) – € 11.7 billion (USD 15.1 billion)

Slide inspired by an example from Steve Boyer/IBM; Sales data from Prous Integrity datase


Conventional database building

Conventional Database Building


Facts current standard

Facts – current standard

... (ACS) owes most of its wealth to its two 'information services' divisions — the publications arm and the Chemical Abstracts Service (CAS), a rich database of chemical information and literature. Together, in 2004, these divisions made about $340 million — 82% of the society's revenue — and accounted for $300 million (74%) of its expenditure. Over the past five years, the society has seen its revenue and expenditure grow steadily ...

Source: ACS homepage


Facts

Facts

Established application

Straighforward use

De-facto Gold standard

Unique data source

Very costly

No structure export for reasonable price

Very limited in large-scale follow-up analysis

Most recent patents not available


Chemical entity extraction using the chemicalize technology

Not data (search), but integration, analysis and insight, leading to decisionsanddiscovery


Now what would be the perfect solution

Now – What would be the perfect solution?

All patent offices require to provide all claimed structures as machine-readable version available for one-click-download 


Text extraction

Text extraction

Definition: Extract all molecules that are mentioned in a patent text of interest, convert them to structures and make them available in machine-readable format


Mining for chemical knowledge technologies from providers

Mining for Chemical KnowledgeTechnologies from providers


The objective

The objective

To provide a tool that provides sophisticated text analysis methods for NIBR scientists and thereby leverages the methods of TMS


Mining for chemical knowledge novartis tools the chemicalize technology is working under the hood

Mining for Chemical Knowledge – Novartis Tools – the chemicalize-technology is working under the hood!

Clipboard Analysis

Identified structures

Patent text

View structure onMouseOver

Export to other applications


Mining for knowledge novartis tools input example j med chem paper

Mining for Knowledge – Novartis ToolsInput example: J Med Chem Paper


Mining for chemical knowledge use case

Mining for Chemical Knowledge – Use Case

Medicinal Chemist wants to synthesize competitor compound as tool compound for own project

This enables the identification of compounds most representative for a competitor patent

Identification of core scaffold

Analysis of substitution patterns


Example a text based patent

Example – A text-based patent

A patent example

Automated Text extraction

452 compounds

Reference

636 compounds

71%


Example an image base patent

Example – An image-base patent

  • Text extraction not suitable for this case, it does find only a meager 40 molecules, 1129 in reference – Why?

An entirely image-based patent example


Language issues e g japanese patents

Language issues – e.g. Japanese patents


Encountered problems

Encountered problems

  • OCR (Optical Character Recognition)!!

  • USPTO and WIPO are now available full text in most cases

  • Typos!

  • Name2Struct problems (less an issue here)


Ibm initiative patent mining chemverse database steve boyer

IBM initiative Patent Mining / ChemVerse database (Steve Boyer)

  • The objective is to automatically extract all molecules from all patents available and make them searchable in a database

  • They leverage cloud computing and have access to all full-text patents

  • This is going absolutely the right direction

  • They annotate the molecules with information from freely available databases


Future ideas patent analysis

Future ideas: Patent Analysis

  • Markush translation, Image+Target

  • Ranking capabilities of outcome for User

  • „blurred“ dicos for translating stuff like aryl, cycloalkyl etc.

  • Select  annotate as entity  on the fly error-correction

  • Result goes in a database  Crowdsourcing efforts to improve and store results

  • Suggest functionality


To enable true patinformatics analyses

To enable true Patinformatics analyses ...

Definition by Tony Trippe:


Acknowledgements

Acknowledgements

NITAS/TMS

  • Therese Vachon

  • Daniel Cronenberger

  • Pierre Parisot

  • Martin Romacker

  • Nicolas Grandjean

  • Clayton Springer

  • Naeem Yusuff

  • Bharat Lagu

  • Alex Fromm

  • Katia Vella

  • Olivier Kreim

And many other people in different divisions of NIBR for their support


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