Enhanced taxonomy annotation of antiviral activity data from ChEMBL

Abstract

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.

Examination of molecular space and feasible structures of bioactive components of humic substances by FTICR MS data mining in ChEMBL database

“…About 71 K structures and 787 K bioactivity data points were available for 6128 formulae found in ChEMBL. Antiviral activity data points (21,559 entries) linked to 7958 structures were extracted from thoroughly curated subset of antiviral activity data (ViralChEMBL) 44 to reveal that they were tested against viruses belonging to 25 distinct families. Among the most studied viruses were HIV-1, HCV, and Influenza virus A, from Retroviridae , Flaviviridae , and Orthomyxoviridae families, respectively (Supplementary Fig.…”

(Q)SAR Models of HIV-1 Protein Inhibition by Drug-Like Compounds

“…Such models can be built using publicly and commercially available large-scale sources of information about antiretroviral agents including the NIAID HIV/OI/TB Therapeutics [17], EBI ChEMBL [18], and Clarivate Analytics Integrity [19] databases. It is well-known that in any particular drug discovery project, careful curation of the information from the databases is needed [20][21][22]. Thus, our study consisted of the preparation of cleaned versions of the training sets on HIV-1 integrase (IN), protease (PR), and reverse transcriptase (RT) inhibitors; creation and validation of (Q)SAR models using the PASS [23] and GUSAR [24] software; investigation of the possibilities and limitations of practical application of the developed models; and creation of a freely available web service for prediction of anti-HIV activity using the integrated training set.…”

Getting to Know the Neighbours with GTM: The Case of Antiviral Compounds

“…The subsets FlaviChEMBL and EnteroChEMBL (Supplementary File 1), containing compounds tested against members of Flavivirus and Enterovirus genera, respectively, were extracted from ViralChEMBL . The statistics of the FlaviChEMBL and EnteroChEMBL datasets content is summarized in Table .…”

“…The subset of ViralChEMBL dataset containing 7455 compounds tested against members of Flavivirus (FlaviChEMBL) and Enterovirus (EnteroChEMBL) genera (Supplementary File 1) was extracted from ViralChEMBL …”