A document classifier for medicinal chemistry publications trained on the ChEMBL corpus by George Papadatos, et al. (Journal of Cheminformatics 2014, 6:40)
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.
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: ftp://ftp.ebi.ac.uk/pub/databases/chembl/text-mining webcite. These can be readily modified to include additional keyword constraints to further focus searches.
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.
While the abstract mentions “the triage process,” it fails to capture the main goal of this paper:
…the main goal of our project diverges from the goal of the tools mentioned. We aim to meet the following criteria: ranking and prioritising the relevant literature using a fast and high performance algorithm, with a generic methodology applicable to other domains and not necessarily related to chemistry and drug discovery. In this regard, we present a method that builds upon the manually collated and curated ChEMBL document corpus, in order to train a Bag-of-Words (BoW) document classifier.
In more detail, we have employed two established classification methods, namely Naïve Bayesian (NB) and Random Forest (RF) approaches -. The resulting classification score, henceforth referred to as ‘ChEMBL-likeness’, is used to prioritise relevant documents for data extraction and curation during the triage process.
In other words, the focus of this paper is a classifier to help prioritize curation of papers. I take that as being different from classifiers used at other stages or for other purposes in the curation process.
I first saw this in a tweet by ChemConnector.