The discovery of antiviral drugs is a rapidly developing area of medicinal chemistry research. The emergence of resistant variants and outbreaks of poorly studied viral diseases make this area constantly developing. The amount of antiviral activity data available in ChEMBL consistently grows, but virus taxonomy annotation of these data is not sufficient for thorough studies of antiviral chemical space. We developed a procedure for semi-automatic extraction of antiviral activity data from ChEMBL and mapped them to the virus taxonomy developed by the International Committee for Taxonomy of Viruses (ICTV). The procedure is based on the lists of virus-related values of ChEMBL annotation fields and a dictionary of virus names and acronyms mapped to ICTV taxa. Application of this data extraction procedure allows retrieving from ChEMBL 1.6 times more assays linked to 2.5 times more compounds and data points than ChEMBL web interface allows. Mapping of these data to ICTV taxa allows analyzing all the compounds tested against each viral species. Activity values and structures of the compounds were standardized, and the antiviral activity profile was created for each standard structure. Data set compiled using this algorithm was called ViralChEMBL. As case studies, we compared descriptor and scaffold distributions for the full ChEMBL and its `viral’ and `non-viral’ subsets, identified the most studied compounds and created a self-organizing map for ViralChEMBL. Our approach to data annotation appeared to be a very efficient tool for the study of antiviral chemical space.

Recent outbreaks of dangerous viral infections, such as Ebola virus disease, Zika fever, etc., are forcing the search for new antiviral compounds. Preferably, such compounds should possess broad‐spectrum antiviral activity, as the development of drugs for the treatment of dozens of viral infections lacking specific treatment would require significant resources. Antiviral activity data present in public resources are very sparse and further investigation of structure‐activity relationships is necessary. One of the strategies could be the investigation of chemical space around known active compounds and assessment of activity against closely related viruses in order to fill in the antiviral activity matrix. Here we present an investigation of antiviral activity using universal maps built with generative topographic mapping (GTM) algorithm. The GTM‐based maps were used to find commercially available compounds in close proximity to already known compounds with anti‐flaviviral and anti‐enteroviral activities. Selected compounds were then assessed in cell‐based assays against tick‐borne encephalitis virus (TBEV) and a panel of enteroviruses. This approach allowed us to identify 23 new compounds showing anti‐TBEV activity with EC50 values in micromolar and submicromolar range.

The article relevance. Modern concepts of education development, recognition of the uniqueness and self-worth of human individuality have led to the search for ways to socialize individuals with disabilities, to the development of new pedagogical strategies aimed at developing ideas of independent life for this category of the population. The research purpose is to study the features and identify ways to improve the quality of inclusive education. Research methods: as a research method, we used a questionnaire survey as a method of collecting primary information, which allows us to identify various aspects related to the problems of formation and perception of inclusive education by participants in the educational process. Research results: the article describes the peculiarity of the current stage of inclusive education development, analyzes the social mechanisms for improving the quality of inclusive education.

Recommender systems (RSs), which underwent rapid development and had an enormous impact on e-commerce, have the potential to become useful tools for drug discovery. In this paper, we applied RS methods for the prediction of the antiviral activity class (active/inactive) for compounds extracted from ChEMBL. Two main RS approaches were applied: collaborative filtering (Surprise implementation) and content-based filtering (sparse-group inductive matrix completion (SGIMC) method). The effectiveness of RS approaches was investigated for prediction of antiviral activity classes (“interactions”) for compounds and viruses, for which some of their interactions with other viruses or compounds are known, and for prediction of interaction profiles for new compounds. Both approaches achieved relatively good prediction quality for binary classification of individual interactions and compound profiles, as quantified by cross-validation and external validation receiver operating characteristic (ROC) score >0.9. Thus, even simple recommender systems may serve as an effective tool in antiviral drug discovery.