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. 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 combined with specially developed filters. 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 preparati`on of a set of multi-n-grams (sequences from one to n symbols) for each FoT. Our approach may provide the recognition of novel CNEs. For CHEMDNER corpus, the values of the sensitivity (recall) was 0.95, precision was 0.74, specificity was 0.88, and balanced accuracy was 0.92 based on five-fold cross validation. 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.

COVID-19: Analysis of Drug Repositioning Practice.

An outbreak of a new coronavirus disease (COVID-19) in China in December 2019 became the epicenter for the spread of a global pandemic. The SARS-CoV-2 coronavirus causes a cascade of respiratory diseases similar to severe acute respiratory syndrome (SARS). Currently, there is no effective, specific, and safe treatment for COVID-19 to suppress the virus in the human body. The present study searched for pharmacological substances with antiviral activity for possible drug repositioning based on experimental and theoretical information in a series of publications on in vitro assays of agents against SARS-CoV-2. An analysis identified 46 well-known pharmaceutical substances that could be used for drug repositioning to create a therapy for COVID-19.

Computational Approaches to Identify a Hidden Pharmacological Potential in Large Chemical Libraries

To improve the discovery of more effective and less toxic pharmaceutical agents, large virtual repositories of synthesizable molecules have been generated to increase the explored chemicalpharmacological space diversity. Such libraries include billions of structural formulae of druglike molecules associated with data on synthetic schemes, required building blocks, estimated physical-chemical parameters, etc. Clearly, such repositories are “Big Data”. Thus, to identify the most promising compounds with the required pharmacological properties (hits) among billions of available opportunities, special computational methods are necessary. We have proposed using a combined computational approach, which combines structural similarity assessment, machine learning, and molecular modeling. Our approach has been validated in a project aimed at finding new pharmaceutical agents against HIV/AIDS and associated comorbidities from the Synthetically Accessible Virtual Inventory (SAVI), a 1.75 billion compound database. Potential inhibitors of HIV-1 protease and reverse transcriptase and agonists of toll-like receptors and STING, affecting innate immunity, were computationally identified. The activity of the three synthesized compounds has been confirmed in a cell-based assay. These compounds belong to the chemical classes, in which the agonistic effect on TLR 7/8 had not been previously shown. Synthesis and biological testing of several dozens of compounds with predicted antiretroviral activity are currently taking place at the NCI/NIH. We also carried out virtual screening among one billion substances to find compounds potentially possessing anti-SARS-CoV-2 activity. The selected hits’ information has been accepted by the European Initiative “JEDI Grand Challenge against COVID-19” for synthesis and further biological evaluation. The possibilities and limitations of the approach are discussed © The Authors 2020. This paper is published with open access at SuperFri.org

COVID-19: Analysis of Drug Repositioning Practice

New coronavirus disease flashing in December 2019 (COVID-19) has become starting point for the spread of a global pandemic. SARS-CoV-2 coronavirus causes a number of severe respiratory disorders similar to severe acute respiratory syndrome (SARS). Currently there is no effective, specific and safe treatment for COVID-19 to suppress the virus in human body. Based on the experimental and theoretical information with regard to <i>in vitro</i> coronavirus assays in a series of publications, this study has been undertaken to reveal pharmacological substances with antiviral activity in the field of drug repositioning. Our analysis identified 46 well-known pharmaceutical substances which could be important candidates for drug repositioning that could help overcome the pandemic situation with COVID-19. В декабре 2019 г. вспышка новой коронавирусной болезни (COVID-19) в Китае стала отправной точкой развития общемировой пандемии. Вирус SARS-CoV-2 вызывает каскад респираторных заболеваний, сходных с тяжелым острым респираторным синдромом. В настоящий момент не существует эффективной, специфической и безопасной терапии для подавления взаимодействия этого вируса с организмом человека. При поиске фармакологических субстанций с противовирусной активностью ведутся исследования в области репозиционирования лекарственных средств на основе экспериментальной и теоретической информации, представленной в ряде публикаций, посвященных тестированию <i>in vitro</i> средств в отношении SARS-CoV-2. Анализ научных публикаций позволил идентифицировать 46 фармацевтических субстанций, которые могут быть использованы для репозиционирования лекарств при создании терапии против COVID-19.