We present a promising in silico paradigm called literature-based discovery (LBD) and describe its potential to identify novel pharmacologic approaches to treating diseases. The goal of LBD is to generate novel hypotheses by analyzing the vast biomedical literature. Additional knowledge resources, such as ontologies and specialized databases, are often used to supplement the published literature. MEDLINE, the largest and most important biomedical bibliographic database, is the most common source for exploiting LBD. There are two variants of LBD, open discovery and closed discovery. With open discovery we can, for example, try to find a novel therapeutic approach for a given disease, or find new therapeutic applications for an existing drug. With closed discovery we can find an explanation for a relationship between two concepts. For example, if we already have a hypothesis that a particular drug is useful for a particular disease, with closed discovery we can identify the mechanisms through which the drug could have a therapeutic effect on the disease. We briefly describe the methodology behind LBD and then discuss in more detail currently available LBD tools; we also mention in passing some of those no longer available. Next we present several examples in which LBD has been exploited for identifying novel therapeutic approaches. In conclusion, LBD is a powerful paradigm with considerable potential to complement more traditional drug discovery methods, especially for drug target discovery and for existing drug relabeling.
Keywords: Automatic summarization, computer applications, drug repurposing, information management, information retrieval, literature-based discovery, natural language processing, PubMed/MEDLINE, semantic processing, text mining, Unified Medical Language System