AI-powered databases boost the Alzheimer’s drug discovery process

Subscribe to enjoy similar stories. Scientists at the United Kingdom’s Oxford Drug Discovery Institute can speed up the work of digging through journals and databases by nearly ten times.

Researchers studying Alzheimer’s disease are using artificial intelligence-powered databases to accelerate the drug discovery process by making it easier to sift through vast amounts of biomedical data. By using those technologies, scientists at the United Kingdom’s Oxford Drug Discovery Institute can speed up the work of digging through journals and databases by nearly ten times—helping to more quickly prioritize which genes or proteins should be selected for further work to generate potential Alzheimer’s drugs, it said.

Biologists at the Oxford Drug Discovery Institute had selected 54 genes from a genome-wide association study that were related to the immune system, all of which are likely targets for lab testing, said Emma Mead, its chief scientific officer. Those targets can include biological structures like genes or proteins, which potential drugs aim to affect.

Picking Alzheimer’s targets can be particularly tricky because there are so many genes that can increase the risk of developing disease, and because the disease has so many confounding environmental and socioeconomic risk factors, Mead said. But it took leveraging a knowledge graph, a database technology popularized by Google over a decade ago for its search engine, for staff to more quickly decipher those targets’ properties across a large number of sources—from the U.S.

National Library of Medicine’s PubMed to various scientific journals and its own datasets. Knowledge graphs—which are like databases that represent information similar to maps— can show relationships