Annotated chemical patent corpus: A gold standard for text mining

Exploring the chemical and biological space covered by patent applications is crucial in early-stage medicinal chemistry activities. Patent analysis can provide understanding of compound prior art, novelty checking, validation of biological assays, and identification of new starting points for chemical exploration. Extracting chemical and biological entities from patents through manual extraction by expert curators can take substantial amount of time and resources. Text mining methods can help to ease this process. To validate the performance of such methods, a manually annotated patent corpus is essential. In this study we have produced a large gold standard chemical patent corpus. We developed annotation guidelines and selected 200 full patents from the World Intellectual Property Organization, United States Patent and Trademark Office, and European Patent Office. The patents were pre-annotated automatically and made available to four independent annotator groups each consisting of two to ten annotators. The annotators marked chemicals in different subclasses, diseases, t

Analysis of in vitro bioactivity data extracted from drug discovery literature and patents: Ranking 1654 human protein targets by assayed compounds and molecular scaffolds

<p>Abstract</p> <p>Background</p> <p>Since the classic Hopkins and Groom druggable genome review in 2002, there have been a number of publications updating both the hypothetical and successful human drug target statistics. However, listings of research targets that define the area between these two extremes are sparse because of the challenges of collating published information at the necessary scale. We have addressed this by interrogating databases, populated by expert curation, of bioactivity data extracted from patents and journal papers over the last 30 years.</p> <p>Results</p> <p>From a subset of just over 27,000 documents we have extracted a set of compound-to-target relationships for biochemical <it>in vitro </it>binding-type assay data for 1,736 human proteins and 1,654 gene identifiers. These are linked to 1,671,951 compound records derived from 823,179 unique chemical structures. The distribution showed a compounds-per-target average of 964 with a maximum of 42,869 (Factor Xa). The list includes non-targets, failed targets and cross-screening targets. The top-278 most actively pursued targets cover 90% of the compounds. We further investigated target ranking by determining the number of molecular frameworks and scaffolds. These were compared to the compound counts as alternative measures of chemical diversity on a per-target basis.</p> <p>Conclusions</p> <p>The compounds-per-protein listing generated in this work (provided as a supplementary file) represents the major proportion of the human drug target landscape defined by published data. We supplemented the simple ranking by the number of compounds assayed with additional rankings by molecular topology. These showed significant differences and provide complementary assessments of chemical tractability.</p

Annotated Chemical Patent Corpus: A Gold Standard for Text Mining - Figure 1

<p>Example patent text with pre-annotations as shown by the Brat annotation tool.</p

Number of annotated terms and unique terms in the harmonized set and in the full patent set of the gold standard corpus after disambiguation.

<p>Number of annotated terms and unique terms in the harmonized set and in the full patent set of the gold standard corpus after disambiguation.</p

Inter-annotator agreement (F-score) without ambiguity resolution.

<p>Inter-annotator agreement (F-score) without ambiguity resolution.</p

Target class distribution of the 8,066 patents from which the final set was drawn.

<p>Target class distribution of the 8,066 patents from which the final set was drawn.</p

Inter-annotator agreement after ambiguity resolution.

<p>The lower left triangle presents the inter-annotator agreement scores (F-score). The upper right triangle shows the improvement gained through disambiguation.</p><p>Inter-annotator agreement after ambiguity resolution.</p

The effect of the disambiguation process on the annotations.

<p>The effect of the disambiguation process on the annotations.</p

Number of annotated terms and unique terms within the harmonized set prior to disambiguation.

<p>Number of annotated terms and unique terms within the harmonized set prior to disambiguation.</p