BIOTECHNOLOGY 51
Since its establishment in 2012,
Exscientia has achieved several collaborations, including more recently with Sanofi and GSK, which have helped to fuel expansion. Over time, the company has enhanced and refi ned AI-driven approaches, testing them in partnership with pharmaceutical partners on real projects, which also helped fund the company’s advancement without the need for external investment. In these early partnerships, Hopkins
reveals that Exscientia has ‘successfully and reproducibly completed projects to drug candidate stage within its target of 500 compounds and 12 months.’ For one partner, a candidate molecule was delivered within 12 months of project initiation. In Hopkins’ view, AI, if used correctly, most certainly has the potential to ‘transform the productivity of drug discovery and deliver superior candidates into the clinic.’ “T e drug discovery phase is the most
expensive phase of the drug discovery and development process – the goal is to generate better quality candidates quicker and more cost eff ectively. T is will change the way discovery is done,” he says. “To make it happen, Big Pharma needs to make the investment. Large companies
need to look critically at their incumbent processes and the associated metrics and ask whether those are the right processes to take them towards the future of drug discovery. We are already working with (and have worked with) several pharma fi rms to apply our AI approaches and the results so far are encouraging,” he adds. Ultimately, Hopkins argues that there will need to be a ‘change in big pharma mentality to such a radical rethinking’ – and he describes drug discovery as the ‘last artisan industry.’ “One of the key opportunities is also it’s challenge; the amount of data now available is beyond any human’s individual capability to hold in mind at one time. For AI focused towards drug design, where Exscientia concentrates its expertise, this provides unprecedented opportunity,” he says. “Other companies are looking at patient stratifi cation and personalis ed medicine. For example, in patient stratifi cation for clinical trials, AI may be used to fi t the patient to the treatment being tested better, enabling quicker recruitment and increasing the likelihood of getting the required clinical response for regulatory approval,” he adds.
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