Movement disorders are a heterogeneous group of both common and rare neurological conditions characterized by abnormalities of motor functions and movement patterns. This work overviews recent successes and ongoing studies of repositioning relating to this disease group, which underscores the challenge of integrating the voluminous and heterogeneous findings required for making suitable drug repositioning decisions. In silico drug repositioning methods hold the promise of automated fusion of heterogeneous information sources, but the controllable, flexible and transparent incorporation of the expertise of medicinal chemists throughout the repositioning process remains an open challenge. In support of a more systematic approach toward repositioning, we summarize the application of a computational repurposing method based on statistically rooted knowledge fusion. To foster the spread of this technique, we provide a step-by-step guide to the complete workflow, together with a case study in Parkinson's disease.
Keywords: Movement disorders, Parkinson’s disease, in silico drug discovery, similarity search, data fusion, multiple kernel learning, multi-aspect drug repositioning.