This interdisciplinary study explores a strategy by which readily available clinical data may be used along with structural features of drugs to identify associations with potential utility for both clinical decision-making and drug development.Ĭhemical functional groups and structural groups (SGs) of 261 drugs were manually classified in tiers, and their incidence of gastrointestinal (GI) and central nervous system (CNS) adverse drug reactions (ADRs) were obtained from a clinical database. Its role in analysis of safety and efficacy is relatively diminished after drugs are approved for clinical use. ChexMix discovered the allied species related to a keyword of interest and experimentally proved its usefulness for multi-species analysis.Ĭhemical structure is a vital consideration early in the drug development process. Herein, ChexMix was used to construct a taxonomic tree with allied species among Korean native plants and to extract the medical subject headings unique identifier of the bioentities, which co-occurred with the keywords in the same literature. ChexMix was designed to extract the unique identifiers of bioentities from query results.
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However, the manual re-organization and analysis of bioentities is a non-trivial and highly time-consuming task. A text-mining tool, PubTator, helps to automatically annotate bioentities, such as species, chemicals, genes, and diseases, from PubMed abstracts and full-text articles. The large volume of biomedical literature that is being published at an unprecedented rate hinders the discovery of relevant knowledge from keywords of interest to gather new insights and form hypotheses. īiomedical databases grow by more than a thousand new publications every day. Altogether, IMPPAT is the largest phytochemical atlas of Indian medicinal plants which is accessible at. Using cheminformatics, we have characterized the molecular complexity and molecular scaffold based structural diversity of the phytochemical space of Indian medicinal plants, and performed a comparative analysis with other chemical libraries. We also filtered a subset of 1335 drug-like phytochemicals of which majority have no similarity to existing approved drugs. The phytochemical library has been annotated with several useful properties to enable easier exploration of the chemical space.
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Notably, IMPPAT 2.0 compiles associations at the level of plant parts, and provides a FAIR compliant non-redundant in silico stereo-aware library of 17967 phytochemicals from Indian medicinal plants. To this end, we present IMPPAT 2.0, an enhanced and expanded database, compiling manually curated information on 4010 Indian medicinal plants, 17967 phytochemicals, 1095 therapeutic uses and 1133 traditional Indian medicinal formulations. Compilation, curation, digitization and exploration of the phytochemical space of Indian medicinal plants can expedite ongoing efforts toward natural product and traditional knowledge based drug discovery.