48 BIOTECHNOLOGY
volumes of unstructured information in scientific papers, patents and clinical trial information, together with a large number of structured data sets.’ “It works to understand the information by employing an array of proprietary deep learning linguistic models and algorithms to analyse and understand context; then reasons, learns, explores, creates and translates what it has learnt to produce unique drug development hypotheses,” she says. In Hunter’s view, one obvious
Jackie Hunter, CEO at BenevolentBio
700,000 people in the UK are predicted to have late-stage AMD, with tens of millions more across the world living with the condition. As Jackie Hunter, CEO at
BenevolentBio, explains, the AI technology created by BenevolentAI ‘generates usable knowledge from vast
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advantage of this approach is the speed at which operations can be managed using AI – and she reveals that the company has ‘conservatively estimated’ that on a per-project basis it can cut the early stage drug discovery process by ‘up to four years against pharma industry averages.’ “Tat will have a big impact on the ability to bring drugs to market as we continue to evolve towards truly personalised medicine. Te biggest challenge is access to data – especially negative data. Much of the negative data is unpublished and therefore it is very hard to access it,” she says.
According to Dr Laura Ferraiuolo,
Lecturer in Translational Neurobiology in the Sheffield Institute of Translational Neuroscience (SITRAN) at the University of Sheffield – which continues to work closely with BenevolentBio as part of the project – it is ‘clear that the scientific community currently produces much more information and data than can be processed by a single individual, hence the need for AI to mine into large datasets and databases.’ In her perspective, AI will lead to a ‘massive acceleration’ in hypothesis generation and new findings, leading to faster drug discovery. “Processes that now take weeks, months
or years even, might take only a few hours. For what we can foresee, AI already can and will be able to perform a number of complex in silico tests, that will not only save a large amount of time, but also money, thus releasing funding for further scientific and clinical advancements. One of the current challenges is testing the hypotheses and feeding back into the system in a timely fashion, so that the machine learning process can continue and be perfected,” she says.
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