Andrew Hopkins,



"Our opinion is that the interface between AI, machine learning and pharma coming together is potentially revolutionary and similar to what we saw when molecular biology and pharma came together to feed biotech 40 years ago."

How has Exscientia evolved from its founding to where it is today?

Exscientia was founded in 2012 and we were the first company to use AI in drug design. We started off as a spin out company from the University of Dundee where I was a professor and we started doing deals almost from day one. The reason for this is that we really wanted to test our technology in the real world and on live drug discovery projects. Normally, these projects can cost tens of millions of dollars. Rather than spend equity on VC money to validate the system, we decided to work with partners right from the start to drive our work forward. By the time we took our series A investment in September 2017, it was with one of our partners, Evotech. Then in December 2018 we announced our Series B, and in May 2020 our Series C funding.

In January 2020, we were very excited to announce a huge milestone in AI drug discovery. In partnership with Sumitomo Dainippon Pharma (DSP), we announced that a Phase I clinical study of DSP-1181, that was created using Artificial Intelligence, has been initiated in Japan for the treatment of obsessive-compulsive disorder as an initial indication. This was a huge milestone for not only us, but for the industry, as the first time a drug designed using AI has entered into testing on humans.

How do you try to balance risk?

The difference between Exscientia and a conventional biotech company is that a conventional biotech company is not usually revenue generating and typically spends all of its capital to advance its pipeline. We feel we are getting that balance right between revenue generation, short term, and long-term value generation by building and developing our own drugs. That allows us to then de-risk the company.

How well can your technology move between different disease indications?

A better way to think about it is systemisation, which is actually automation. If we systematise not just individual tasks, but the entire process, then we can have an impact not just on efficiency but also in terms of achieving more effective outcomes. It is an opportunity to make more molecules and better molecules that have a higher chance of success in the clinic. This approach has the power to transform the pharma industry. Our opinion is that the interface between AI, machine learning and pharma coming together is potentially revolutionary and similar to what we saw when molecular biology and pharma came together to feed biotech 40 years ago. How we learn to apply these technologies is going to be as equally important as the technologies themselves.

How do you envision the future for drug hunters using AI?

We hypothesize that pharma’s productivity has fallen because cognitive bandwidth has been the bottleneck as we add massive amounts of new data to projects. For the past 20 to 30 years, there has been an abundance of incredible new technologies in the pharmaceutical industry. Our knowledge of disease is so much greater, and yet somehow, if you look at productivity output metrics on the pharma industry over the past 10 years, it has declined from 10% IRR a decade ago to around 1% today. So, there is this paradox between the increase in knowledge and advancement in technology and yet a decrease in return on investment. What did not change was human decision making. You are creating more information and more data to the human drug designer to try to integrate and assimilate. Therefore, whilst any one technology could give us great new insights, what had not changed is how people running these projects understand how to integrate this information. That is where AI and machine learning make a fundamental difference. AI and machine learning allows us to expand that cognitive bottleneck.

We have not yet seen notable acquisitions in the space today. Is it just a matter of big pharma needing to see proof before paying up for companies?

I think what you are likely to see is some vertical integration starting to take place between AI in drug discovery and AI in clinical starting to come together all the way to market. Furthermore, I think you will start to see some potential major acquisitions once companies start to validate and prove themselves.