ArticleJournal of cheminformatics2015
Managing expectations: assessment of chemistry databases generated by automated extraction of chemical structures from patents.
Article in Journal of cheminformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- PatCID: an open-access dataset of chemical structures in patent documents.Nature communications · 2024Article
- Exploring SureChEMBL from a drug discovery perspective.Scientific data · 2024Article
- Article
- ChemTables: a dataset for semantic classification on tables in chemical patents.Journal of cheminformatics · 2021Article
- From Tokenization to Self-Supervision: Building a High-Performance Information Extraction System for Chemical Reactions in Patents.Frontiers in research metrics and analytics · 2021Article
- ChEMU 2020: Natural Language Processing Methods Are Effective for Information Extraction From Chemical Patents.Frontiers in research metrics and analytics · 2021Article
- Automatic identification of relevant chemical compounds from patents.Database : the journal of biological databases and curation · 2019Article
- Assessment of the significance of patent-derived information for the early identification of compound-target interaction hypotheses.Journal of cheminformatics · 2017Article
- Patterns of database citation in articles and patents indicate long-term scientific and industry value of biological data resources.F1000Research · 2016Article
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Authors and funding
4 authors.
Funding
Abstract
backgroundFirst public disclosure of new chemical entities often takes place in patents, which makes them an important source of information. However, with an ever increasing number of patent applications, manual processing and curation on such a large scale becomes even more challenging. An alternative approach better suited for this large corpus of documents is the automated extraction of chemical structures. A number of patent chemistry databases generated by using the latter approach are now available but little is known that can help to manage expectations when using them. This study aims to address this by comparing two such freely available sources, SureChEMBL and IBM SIIP (IBM Strategic Intellectual Property Insight Platform), with manually curated commercial databases.
resultsWhen looking at the percentage of chemical structures successfully extracted from a set of patents, using SciFinder as our reference, 59 and 51 % were also found in our comparison in SureChEMBL and IBM SIIP, respectively. When performing this comparison with compounds as starting point, i.e. establishing if for a list of compounds the databases provide the links between chemical structures and patents they appear in, we obtained similar results. SureChEMBL and IBM SIIP found 62 and 59 %, respectively, of the compound-patent pairs obtained from Reaxys.
conclusionsIn our comparison of automatically generated vs. manually curated patent chemistry databases, the former successfully provided approximately 60 % of links between chemical structure and patents. It needs to be stressed that only a very limited number of patents and compound-patent pairs were used for our comparison. Nevertheless, our results will hopefully help to manage expectations of users of patent chemistry databases of this type and provide a useful framework for more studies like ours as well as guide future developments of the workflows used for the automated extraction of chemical structures from patents. The challenges we have encountered whilst performing this study highlight that more needs to be done to make such assessments easier. Above all, more adequate, preferably open access to relevant 'gold standards' is required.
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