Recent years have seen a dramatic increase in the amount and availability of data in the diverse areas of medicinal
chemistry, making it possible to achieve significant advances in fields such as the design, synthesis and biological
evaluation of compounds. However, with this data explosion, the storage, management and analysis of available data to
extract relevant information has become even a more complex task that offers challenging research issues to Artificial Intelligence
(AI) scientists. Ontologies have emerged in AI as a key tool to formally represent and semantically organize aspects
of the real world. Beyond glossaries or thesauri, ontologies facilitate communication between experts and allow the
application of computational techniques to extract useful information from available data. In medicinal chemistry, multiple
ontologies have been developed during the last years which contain knowledge about chemical compounds and processes
of synthesis of pharmaceutical products. This article reviews the principal standards and ontologies in medicinal
chemistry, analyzes their main applications and suggests future directions.
Keywords: Drug design, Drug discovery, Medicinal chemistry, Ontologies, Semantic Web.
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