Computational Polypharmacology with Text Mining and Ontologies

Conrad      Plake; Michael      Schroeder

Abstract

Huge volumes of data, produced by microarrays and next- generation sequencing, are now at the fingertips of scientists and allow to expand the scope beyond conventional drug design. New promiscuous drugs directed at multiple targets promise increased therapeutic efficacy for treatment of multi- factorial diseases. At the same time, more systematic tests for unwanted side effects are now possible. In this paper, we focus on the application of text mining and ontologies to support experimental drug discovery. Text mining is a high- throughput technique to extract information from millions of scientific documents and web pages. By exploiting the vast number of extracted facts as well as the indirect links between them, text mining and ontologies help to generate new hypotheses on drug target interactions. We review latest applications of text mining and ontologies suitable for target and drug-target interaction discovery in addition to conventional approaches. We conclude that mining the literature on drugs and proteins offers unique opportunities to support the laborious and expensive process of drug development.

Keywords: Ontologies, text mining, semantic similarity, target identification, lead identification, drug-target interaction

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