ICIC 2013 New Product Introductions ChemAxon

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2 likes•1,157 views

Dr. Haxel Consult

Technology

Who and why ChemAxon:
Industry leader for enterprise & web based structure content
management & delivery

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Platform independent & web ready
Industry leading performance and support
Covers the functionality of most platform competitors
Widest / deepest file format support
Various ways to work with functionality
(API/apps/services…)
Deployed widely throughout STM publishing
Major inertia and adoption in pharma and biotech
Still called JChem and we are still called ChemAxon
Friendly commercial terms, pragmatic approach

Who are we working with…
Commercial
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Aureus Pharma
Cengage
Council of Europe
CCDC
Elsevier
EPO
GVK Bio
IBM
Informa/T&F/CRC Press
Pearson
RSC
Nature / SureChem
Thomson Reuters
Wiley

Public/free
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BARD
Binding DB
EPA (ACToR, +)
EBI
IUCr
KAIST
Ligand Depot
IMI
NIH NLM & NIAID
OECD
RCSB PDB
U. New Mex. (Wombat)
U. Alberta (DrugBank)
ZINC
…

What’s hot for this room
• Markush search and enumeration evaluation with TR
content now available
• Marvin JavaScript in the wild
• Biomolecule Registration toolkit –available (beta)
• Enterprise structure extraction from docs and archives
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Document > DB (Documentum) – beta launched

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SharePoint (Doc>Structure)

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Chinese language support added

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Ongoing improvement of recall and accuracy

…and of course

Enterprise structure extraction

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Chemical information
Document metadata
Searchable
Customizable
Document annotation
GUI’s available

• Supported throughout
CXN API and
applications

Not to be forgotten
• We are interested in the topic (no exit strategy)
• Major and increasing adoption of back end platform #1 global cheminformatics company?
• Many products come from custom work - we have
interest, tech & developers - what do you want to do?
• Free
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for academic teaching and research
for free websites
eval - incl. Markush search against full TR content

• What are the users doing with us:
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UGM archives (8 years) all slides available, many with
videos
Public forum (see how we support real users)

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WIPO started work in the area of patent analytics in 2010 with a Development Agenda project on “Developing Tools for Access to Patent Information” which resulted in the production of a series of Patent Landscape Reports (WIPO’s patent landscape reports can be found here). These reports, prepared in cooperation with various UN Agencies, non-governmental organizations, research institutes and national IP Offices, analyze patent activity in various topics in the areas of public health, food and agriculture, environment and energy, and disabilities. The key findings are often summarized in an infographic. In 2013 WIPO started working also on awareness raising and capacity building in the area of patent analytics. Apart from various workshops organized on this topic, WIPO published in September 2015 the “Guidelines for Preparing Patent Landscape Reports”. The Guidelines describe the objectives and motivations for preparing Patent Landscape Reports (PLR) and other types of patent analysis, the tasks associated with patent analytics, as well as the stages in the preparation of PLRs, providing also some insights from WIPO’s experience in the area. Since 2015 WIPO is exploring open source tools for patent analytics purposes in the framework of the preparation of a Manual on Open Source Tools for Patent Analytics. Open source tools are typically used by other disciplines, usually business/data analysts, statisticians, IT professionals and scientists, rather than with regard to patent data. Nevertheless, in recent years they started emerging as an alternative and/or a complement to ready-to-use tools, providing flexibility and adaptability in different analysis types. In view of the necessary programming related to this type of tools, WIPO developed step-by-step instructions in the Manual with example datasets, and will provide capacity building activities with training on patent analytics for Technology and Innovation Technology Support Centers (TISCs) around the world (for more information on the TISC program please visit www.wipo.int/tisc) .

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Life science companies increasingly rely on text mining to gain important insights from vast amounts of published information. But researchers struggle to get access to full-text articles for text mining. When they do get the full text they must contend with multiple formats and inconsistent license terms – all of which inhibit text mining efforts. In this presentation, we will describe the value in mining full-text scientific literature and outline the issues researchers face in accessing and licensing this content for commercial purposes. We will provide a walkthrough of Copyright Clearance Center’s (CCC) RightFind™ XML for Mining solution and contrast this with other approaches to solving these time-consuming content and licensing challenges. CCC is the parent organization of RightsDirect.

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Henning Schönenberger (SpringerNature, Germany) Springer Nature SciGraph, the new Linked Open Data platform aggregating data sources from Springer Nature and key partners from the scholarly domain. The Linked Open Data platform will initially collate information from across the research landscape, such as funders, research projects, conferences, affiliations and publications. Additional data, such as citations, patents, clinical trials and usage numbers will follow over time. This high quality data from trusted and reliable sources provides a rich semantic description of how information is related, as well as enabling innovative visualizations of the scholarly domain.

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This presentation will give some guidelines on how to create a meaningful Patent Landscapes. Generating patent landscaping reports seems simple, but it isn’t. For making patent landscapes you have to take several different issues into consideration. It’s important at the start to already have in mind what kind of landscape report you are going to prepare, and choose a topic of interest, but preferable not one that is too broad. It’s also extremely important to have a clean (80-90% relevance) dataset that the landscape is based on; otherwise the outcome will be rubbish. And of course, do not use landscapes for questions that require a legal opinion (like Freedom-to-operate conclusions!!). Patent landscapes are not aimed to be as precise as other patent searches. Some more important issues has to be taken in account and are presented.

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Understanding competitors’ patent portfolios and protecting their own intellectual properties are key questions for pharmaceutical companies. Extracting and analyzing the chemical space covered by these patents is an extremely complex and time consuming challenge and requires many communication rounds between IP experts and members of drug discovery teams. ChemAxon has been working with researchers in the industry to develop tools to help in this area by building and analyzing project specific databases based on high quality computer-assisted extraction of chemical information from patent documents. These databases can be useful across the full drug discovery process from idea generation to lead candidate selection, drug design and creation of new patents. This way we can eliminate these rounds of communication, because IP experts can precisely translate the content of patent documents to the language of chemistry which is more comprehensible to other actors. This presentation will discuss the results of this development and technologies developed or used, namely: English and Chinese Name to Structure, which dramatically speeds up the extraction process; Markush Editor that helps draw complex Markush structures more easily, Structure Checker and Markush Validation, which confirm the quality of extracted information. We will also introduce our search, enumeration and hit visualization and our latest improvements that allow overlap analysis between Markush structures.

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The Royal Society of Chemistry publishes many thousands of articles per year, the majority of these containing rich chemistry data that, in general, in limited in its value when isolated only to the HTML or PDF form of the articles commonly consumed by readers. RSC also has an archive of over 300,000 articles containing rich chemistry data especially in the form of chemicals, reactions, property data and analytical spectra. RSC is developing a platform integrating these various forms of chemistry data. The data will be aggregated both during the manuscript deposition process as well as the result of text-mining and extraction of data from across the RSC archive. This presentation will report on the development of the platform including our success in extracting compounds, reactions and spectral data from articles. We will also discuss our developing process for handling data at manuscript deposition and the integration and support of eLab Notebooks (ELNS) in terms of facilitating data deposition and sourcing data. Each of these processes is intended to ensure long-term access to research data with the intention of facilitating improved discovery.

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The Royal Society of Chemistry was pleased to contribute to the Open PHACTS project, a 3 year project funded by the Innovative Medicines Initiative fund from the European Union. For three years we developed our existing platforms, created new and innovative widgets and data platforms to handle chemistry data, extended existing chemistry ontologies and embraced the semantic web open standards. As a result RSC served as the centralized chemistry data hub for the project. With the conclusion of the Open PHACTS project we will report on our experiences resulting from our participation in the project and provide an overview of what tools, capabilities and data have been released into the community as a result of our participation and how this may influence future projects. This will include the Open PHACTS open chemistry data dump including the chemistry related data in chemistry and semantic web consumable formats as well as some of the resulting chemistry software released to the community. The Open PHACTS project resulted in significant contributions to the chemistry community as well as the supporting pharmaceutical companies and biomedical community.

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AI-SDV 2022: Machine learning based patent categorization: A success story in...

Dr. Haxel Consult

It is relatively easy for a human to read a document and quickly figure out which concepts are important. However, this task is a difficult challenge for a machine. During the past few decades, there have been two main approaches for concept identification: Natural Language Processing and Machine Learning. During the early part of this century, Machine Learning made great strides as new techniques came into wider use (SVM’s, Topic Modeling, etc..). Sensing the competition, Natural Language Processing responded with deployment of new emerging techniques (sematic networks, finite state automata, etc..). Neither approach has completely solved the WHAT problem. Advances in Artificial Intelligence have the potential to significantly improve the situation. Where AI is making the most impact is as an enhancement to make Machine Learning and Natural Language Processing work better and, more importantly, work together. This presentation looks at some of this history and what might happen in the future when we blend the interpretation of language with pattern prediction.

AI-SDV 2022: Finding the WHAT – Will AI help? Nils Newman (Search Technology,...

Dr. Haxel Consult

Trademarks serve as key leading indicators for innovation and economic growth. As the vanguards of new and expanding enterprises, trademarks can be used to study entrepreneurship and shifting market demands in response to varying economic factors. This responsiveness has been seen as recently as the COVID-19 pandemic, where trademark research revealed key insights about business reaction to the global upheaval. At CIPO, we have been delving more deeply than ever before into trademark analysis by leveraging cutting-edge natural language processing (NLP) tools to derive actionable business intelligence from trademark data. In this presentation, we present a survey of NLP in use at CIPO and the insights we have learned applying them. These insights include COVID-19 responses, line-of-business trends based on firm characteristics, and more. We also discuss ongoing and future trademark research projects at CIPO. These projects include emerging technology detection methods and high-resolution trademark classification systems. We conclude that artificial intelligence-enhanced tools like NLP are key components of future exploitation of trademark data for business and economic intelligence.

AI-SDV 2022: New Insights from Trademarks with Natural Language Processing Al...

Dr. Haxel Consult

In our customer projects involving automated document processing, we often encounter document types providing crucial data in the form of tables. While established text analytics algorithms are usually optimized to operate on running text, they tend to produce rather poor results on tables as they do not capture the non-sequential relations inside them (e.g. interpret the content of a table cell relative to its column title, interpret line breaks inside a cell differently from line breaks between cells or rows). While there are elaborate information extraction products in the market for a few highly specific types of tabular documents, there is no general approach out there. The main cause for this is the fact that table structures can be encoded by a heterogenous range of layout means (e.g. column boundaries can be signaled by lines vs. aligned text vs. white space). In this talk, we will illustrate several solutions that we have developed for a range of challenges occurring in this context, both for scanned and digitally generated documents.

AI-SDV 2022: Extracting information from tables in documents Holger Keibel (K...

Dr. Haxel Consult

Most scientific journals request, that the complete set of research data is published simultaneously with the peer-reviewed paper. The publication of the research data usually is carried out as so-called "Supplementary Material", attached to the original paper, or on a "Research Data Repository". Both forms have in common, that the data is published usually unstructured and not in an uniform machine processable format. This makes its further use in electronic tools for AI or data mining unnecessarily difficult or even impossible. A concept is presented, in which the data is digitally recorded, following the principle of FAIR data, as part of the publication process. This digital capture makes the data available to the scientific community for easy use in data mining and AI tools. The data in the repository contains links to the publication to document its origin. The concept is applicable for preprints, peer-review papers, diploma and doctoral theses and is particularly suitable for open access publications. Moreover, the presentation highlights correspondent activities, which were released in scientific publications recently.

AI-SDV 2022: Scientific publishing in the age of data mining and artificial i...

Dr. Haxel Consult

AI-SDV 2022: AI developments and usability Linus Wretblad (IPscreener / Uppdr...

Dr. Haxel Consult

How do you find video when you only have sparse data? While you can wander the stacks (if you can still find open stacks) for inspiration, video either physical or digital, is difficult to discover. Wandering the virtual stacks is, well, virtually impossible. Discovery platforms on the whole have not replicated the inspirational experience of wandering the stacks. More companies are using archivable video for internal communication of the various research projects, product developments, test results, and more that are being considered, in progress, or completed. Showing how an experiment was conducted can convey considerably more information that is very difficult to communicate via text. How do you find a company video that might be helpful for your project? A case study is presented of the problems and the solutions that were implemented by a large, multinational chemical company. A suite of content discovery technologies was used including a video to text to tagging system connected to their documents database and automatically indexed using several chemical as well as conceptual systems (rule-based, NLP, inference engine). To build the system and support the manuscript and video submission there is a metadata extraction program which pulls and inserts the metadata into the submission forms so the author can move quickly through that process.

AI-SDV 2022: Where’s the one about…? Looney Tunes® Revisited Jay Ven Eman (CE...

Dr. Haxel Consult

Copyright Clearance Center A pioneer in voluntary collective licensing, CCC (Copyright Clearance Center) helps organizations integrate, access, and share information through licensing, content, software, and professional services. With expertise in copyright and information management, CCC and its subsidiary RightsDirect collaborate with stakeholders to design and deliver innovative information solutions that power decision-making by helping people integrate and navigate data sources and content assets. CCC recently acquired the assets and technology of Deep SEARCH 9 (DS9), a knowledge management platform that leverages machine learning to help customers perform semantic search, tag content, and discover new insights.

AI-SDV 2022: Copyright Clearance Center

Dr. Haxel Consult

Lighthouse IP is the world’s leading provider of intellectual property content. The core business of Lighthouse IP is sourcing and creating content from the world’s most challenging authorities. Specialized in IP data, Lighthouse IP provides over 160 countries coverage for patents, over 200 authorities for trademarks and over 90 authorities for designs. Lighthouse IP data is available via several partners. The company is headquartered in Schiphol-Rijk in the Netherlands and has offices in the United States, China, Thailand, Vietnam, Egypt, Indonesia and Belarus. Globally a team of 150 experts works on the creation of this unique data collection.

AI-SDV 2022: Lighthouse IP

Dr. Haxel Consult

CENTREDOC was created in 1964 as the technical information center of the swiss watchmaking industry. Building on a strong team of engineers, CENTREDOC now offers a complete range of services and solutions for the monitoring of strategic, technological and competitive information. CENTREDOC is also a leader in the research of patent, technical and business intelligence, and offers consulting expertise in the implementation of monitoring solutions.

AI-SDV 2022: New Product Introductions: CENTREDOC

Dr. Haxel Consult

AI-SDV 2022: Possibilities and limitations of AI-boosted multi-categorization...

Dr. Haxel Consult

What if there was a platform where literature, conference abstracts, patents, clinical trials, news, grants and other sources were fully integrated? What if the data would be harmonized, enriched with standardized concepts and ready for analysis? After building our patent analytics platform we didn’t stop dreaming and built our big data analytics platform by semantically integrating text-rich, scientific sources. In my presentation I will talk about what we built and why we built it. And, of course, I will also address the challenges and hurdles along the way. Was it worth it and what comes next? Let’s talk about it!

AI-SDV 2022: Big data analytics platform at Bayer – Turning bits into insight...

Dr. Haxel Consult

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