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 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.
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).
Provides a repository of profiles diseases and genes associated with related drugs, biological phenomena and anatomy described with MeSH vocabulary. Gendoo includes more than 1700000 associations. The database allows users to visualize associations between OMIM entries and relevant MeSH terms and compare different features. Searches can be made by OMIM IDs, OMIM titles, Entrez Gene IDs, gene names or MeSH terms.
Delivers information of pediatric diseases from published medical literature and clinical data. PedAM holds annotated enriched clinical and molecular data for most pediatric diseases. It standardizes and classifies data via different patterns and approaches. This database is a useful support for conduction of studies and practice in pediatrics. It simplifies understanding of underlying mechanisms for complex pediatric diseases.