Artificial Intelligence (AI) is being used for a myriad of applications and healthcare is one such prominent field where it’s applied. While there are numerous companies that employ AI to solve varying problems, the rapidly growing UK-based startup BenevolentAI is one step ahead of the lot.
The company was founded in 2013 with the aim of digging deep into data to find insights and solutions, and to apply them to “to transform the way medicines are discovered, developed, tested and brought to market.”
BenevolentAI has now made a long-term collaboration with AstraZeneca, a US based global pharmaceutical company. The two organisations will employ machine learning and AI for discovery and development of potential new drugs, for chronic kidney disease (CKD) and idiopathic pulmonary fibrosis (IPF). Here’s how this new partnership aims to accelerate drug discovery and development.
Amalgamating AI with human experience
With this partnership, scientists from BenevolentAI and AstraZeneca will work together. They will use BenevolentAI target identification platform and biomedical knowledge graph and combine it with AstraZeneca’s genomics, chemistry and clinical data. This will enable the machine learning system to analyse data and find insights to understand connections between facts, which were not unknown before.
Additionally, AI-based reasoning is employed to extrapolate previously undiscovered connections. The companies will then together analyse and interpret the results to “understand the underlying mechanisms of these complex diseases and more quickly identify new potential drug targets.”
Joanna Shields, CEO of BenevolentAI, says,“Millions of people today suffer from diseases that have no effective treatment. The future of drug discovery and development lies in bridging the gap between AI, data, and biology. We are thrilled to be joining forces with AstraZeneca to develop new insights and identify promising new treatments for chronic kidney disease and idiopathic pulmonary fibrosis.”
CKD and IPF are complicated diseases in which the underlying disease biology is said to be poorly understood. Therefore, this disease complexity is said to require the interrogation of vast, rich datasets. This partnership thus will help discover more about the diseases and hopefully, improve the chances of developing treatments for them.
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