Chemical named entity recognition in the texts of scientific publications using the naïve Bayes classifier approach 

Motivation: Application of chemical named entity recognition (CNER) algorithms allows retrieval of information from texts about chemical compound identifiers and creates associations with physical-chemical properties and biological activities. Scientific texts represent low-formalized sources of information. Most methods aimed at CNER are based on machine learning approaches, including conditional random fields and deep neural networks. In general, most machine learning approaches require either vector or sparse word representation of texts, which is produced using a fixed-size vocabulary from a corpus of texts. Chemical named entities (CNEs) constitute only a small fraction of the whole text, and the datasets used for training are highly imbalanced. Methods: and results: We propose a new method for extracting CNEs from texts based on the naïve Bayes classifier. In contrast to the earlier developed CNER methods, our approach uses the representation of the data as a set of fragments of text (FoTs) with the subsequent preparation of a set of multi- n -grams (sequences from one to n symbols) for each FoT. Therefore, it does not require the usage of a predefined vocabulary. As a result, our approach may provide the recognition of novel CNEs. The average values of invariant accuracy (IA) range from 0.95 to 0.99 depending on the context window and the size values of n of the multi- n -grams. We applied the developed algorithm to the extracted CNEs of potential Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease (Mpro) inhibitors. A set of CNEs corresponding to the chemical substances evaluated in the biochemical assays used for the discovery of Mpro inhibitors was retrieved. Manual analysis of the appropriate texts showed that CNEs of potential SARS-CoV-2 Mpro inhibitors were successfully identified by our method. Conclusion: The obtained results show that the proposed method can be used for filtering out words that are not related to CNEs; therefore, it can be successfully applied to the extraction of CNEs for the purposes of cheminformatics and medicinal chemistry.