Several research consortia and countess projects in pharmaceutical companies generate, organize, and analyze small molecule drug screening data.  Such consortia supported by the NIH Common Fund include the (now past) Molecular Libraries Program (MLP), and currently the Illuminating the Druggable Genome (IDG) and the Library of Integrated Network-based Cellular Signatures (LINCS) projects.  A large component of the MLP program was the development of chemical probes to study a wide variety of biological questions.  This program generated new assay technologies, huge amounts of chemical biology screening data and over 350 chemical probes.  The observation of an apparent strong bias of drug discovery research and development efforts towards targets that are already well studied, motivated the IDG program to prioritize novel drug targets and catalyze the development of chemical entities that target understudied proteins in these families.  The LINCS program has a systems biology focus.  The project creates a reference "library" of molecular signatures, such as changes in gene expression and other cellular phenotypes that occur when cells are exposed to a variety of perturbing agents, and computational tools for data integration, access, and analysis.  Dimensions of LINCS signatures include the biological model system (cell type), the perturbation (e.g. small molecules) and the assays that generate diverse of phenotypic profiles.<br/>Data integration is a common and critical challenge in these and other projects; and data integration requires common metadata standards and conventions for data representation and exchange.  Towards the goal of creating common data standards to represent data in these and other projects that produce data relevant for drug discovery, and to support software tools that we and others have been building as part of these projects, we have been developing ontologies including the BioAssay Ontology (BAO) and the Drug Target Ontology (DTO).  The goal of these ontologies is enable the knowledge-based classification of diverse datasets into categories that facilitates re-use and context-specific integration of these data, for example to develop predictive models or to quickly explore and correlate different datasets.<br/>BAO, DTO and other ontologies provide a robust framework to represent, integrate, model, and query diverse drug discovery data generated in different projects.

Ontologies for classifying and modeling drug discovery data

251st National Meeting (2016)

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