AI-Powered Databases Boost the Alzheimer’s Drug Discovery Process

 

The Oxford Drug Discovery Institute is using artificial intelligence and ‘knowledge graphs’ to sift through vast amounts of biomedical data, potentially leading to faster treatments


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.


Among businesses, knowledge graphs are being used alongside a method called retrieval-augmented generation, or RAG, to help fine-tune the general-purpose AI models offered by companies like Anthropic or OpenAI. AI models can also be linked up with vector databases, which are a different format of storing data that represent data as “vectors.”

For instance, the method of connecting databases with AI helps businesses customize the AI chatbots they build for employees to reference company policies.


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