A document classifier for medicinal chemistry publications trained on the ChEMBL corpus

Abstract

Background: The large increase in the number of scientific publications has fuelled a need for semi- and fully automated text mining approaches in order to assist in the triage process, both for individual scientists and also for larger-scale data extraction and curation into public databases. Here, we introduce a document classifier, which is able to successfully distinguish between publications that are 'ChEMBL-like' (i.e. related to small molecule drug discovery and likely to contain quantitative bioactivity data) and those that are not. The unprecedented size of the medicinal chemistry literature collection, coupled with the advantage of manual curation and mapping to chemistry and biology make the ChEMBL corpus a unique resource for text mining. Results: The method has been implemented as a data protocol/workflow for both Pipeline Pilot (version 8.5) and KNIME (version 2.9) respectively. Both workflows and models are freely available at:. These can be readily modified to include additional keyword constraints to further focus searches. Conclusions: Large-scale machine learning document classification was shown to be very robust and flexible for this particular application, as illustrated in four distinct text-mining-based use cases. The models are readily available on two data workflow platforms, which we believe will allow the majority of the scientific community to apply them to their own data. . Graphical Abstract Multidimensional scaling analysis applied to document vectors derived from titles and abstracts in different corpora. Notably, there is large overlap between the documents in the different ChEMBL versions and BindingDB, while the background MEDLINE set is largely divergent.