Drug repositioning identifies new indications for known drugs. Here we report 
repositioning of the malaria drug amodiaquine as a potential anti-cancer agent. 
While most repositioning efforts emerge through serendipity, we have devised a 
computational approach, which exploits interaction patterns shared between 
compounds. As a test case, we took the anti-viral drug brivudine (BVDU), which 
also has anti-cancer activity, and defined ten interaction patterns using our 
tool PLIP. These patterns characterise BVDU's interaction with its target s. 
Using PLIP we performed an in silico screen of all structural data currently 
available and identified the FDA approved malaria drug amodiaquine as a 
promising repositioning candidate. We validated our prediction by showing that 
amodiaquine suppresses chemoresistance in a multiple myeloma cancer cell line by 
inhibiting the chaperone function of the cancer target Hsp27. This work proves 
that PLIP interaction patterns are viable tools for computational repositioning 
and can provide search query information from a given drug and its target to 
identify structurally unrelated candidates, including drugs approved by the FDA, 
with a known safety and pharmacology profile. This approach has the potential to 
reduce costs and risks in drug development by predicting novel indications for 
known drugs and drug candidates.