  We introduced a methodology to efficiently exploit natural-language expressed
biomedical knowledge for repurposing existing drugs towards diseases for which
they were not initially intended. Leveraging on developments in Computational
Linguistics and Graph Theory, a methodology is defined to build a graph
representation of knowledge, which is automatically analysed to discover hidden
relations between any drug and any disease: these relations are specific paths
among the biomedical entities of the graph, representing possible Modes of
Action for any given pharmacological compound. These paths are ranked according
to their relevance, exploiting a measure induced by a stochastic process
defined on the graph. Here we show, providing real-world examples, how the
method successfully retrieves known pathophysiological Mode of Actions and
finds new ones by meaningfully selecting and aggregating contributions from
known bio-molecular interactions. Applications of this methodology are
presented, and prove the efficacy of the method for selecting drugs as
treatment options for rare diseases.
