Provides a standardized ontology for drug targets that aims to facilitate the integration of diverse drug discovery information from various resources. DTO is organized around the class hierarchies of the four Illuminating the Druggable Genome (IDG) protein families, G protein–coupled receptors (GPCRs), kinases, ion channels, and nuclear hormone receptors. This terminology is composed of protein classes related to tissue and disease according to different levels of confidence.
Provides a comprehensive ontology for drug−drug interactions (DDI). DINTO aims to organize all DDI related knowledge and furnishes a wide range of DDI mechanisms as well as different subtypes of PD and PK mechanism types. The ontology was build according to the Neon methodology for ontology development and the OBO Foundry.
Consists in a Web Ontology Language representation of drug products and their ingredients, mechanisms of action, strengths, and dose forms. DrOn aims to support analyses of large, drug-related datasets such as pharmacy claims and electronic health record (EHR) data. A version without Pittsburgh National Drug Codes (NDCs) is also available.
Aims to develop a comprehensive ontology and annotated database for the nanosafety domain to address the challenge of supporting the unified annotation of nanomaterials. eNanoMapper ontology was created by pan-European computational infrastructure for toxicological data management for engineered nanomaterials (ENMs).
Aims to facilitate knowledge-based inference of potential adverse outcomes using chemical screening and prioritization data from high content and high throughput assays. AOPOntology is a functional ontology that models Adverse Outcome Pathways (AOPs) as a parent class that contains classes for several different child AOP groupings. It builds on or imports several exiting ontologies, such as ChEBI, human phenotype ontology, or BioAssay Ontology and it also includes linkages to the Uniprot database.
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