Drug repurposing - leveraging extensive knwoledge graphs

Drug development is a very resource intensive process. The development phase as well as the thorough testing, takes time and can be costly. On the other hand, it has been shown that many drugs have beneficial side effects. E.g. Aspirin was originally developed as a painkiller, but it is now repurposed to treat cardiovascular diseases and colorectal and breast cancer. Drug repurposing has the advantage that the drug already exists, the production process is in place and that the regulatory tract can be shortened. By using an extensive knowledge graph as a basis, and the addition of extra datasets, we were able to predict a shortlist of drugs that would be worth testing for the blocking of HIV. Different algorithms were used, based on the drug - gene - disease links, over new link predictions, but also molecular structure.Some of drugs in the short list were confirmed to have a HIV blocking activity in the literature, without this knowledge being in the original dataset.Biological processes are intrinsically complex, and cover different processes, which very often lead to diverse datasets. Insights can be generated from these individual datasets, however the true power comes in combining different datasets, generating more in depth insights on the processes.