Identifying candidate drugs for repositioning by graph based modeling techniques based on drug side-effects

Drug development is a lengthy and highly costly endeavor, often with limited success and high risk. The objective of drug repositioning is to apply existing drugs to different diseases or medical conditions than the original target, and thus alleviate to a certain extent the time and cost expended. The area of drug repositioning is a suitable application area for computational intelligence because numerous online databases containing technical information on drug targets, protein interactions, side-effects and biological knowledge are freely available. Thus {\it in-silico} analysis can be used as a useful first stage to screen potential candidate drugs for possible redeployment. We take the position that drugs with side-effects are potential candidates for use elsewhere, it is a case of identifying potential diseases that may benefit from this re-deployment. The system we are refining uses graph based computational techniques to analyze drugs with known side-effects and compares the proteins involved in these side-effects with proteins known to be identified with other diseases. Our intention is to find potential candidates for treating Alzheimer's disease