Home PublicationsData Innovators 5 Q’s for Garry Pairaudeau, Chief Technology Officer of Exscientia

5 Q’s for Garry Pairaudeau, Chief Technology Officer of Exscientia

by Christophe Carugati
Garry Pairaudeau

The Center for Data Innovation spoke with Garry Pairaudeau, chief technology officer of Exscientia, a UK-based firm using AI for drug discovery. Pairaudeau discussed how the company uses AI to shorten the pre-clinical drug discovery stage to bring safe, effective drugs to market faster.

Christophe Carugati: Exscientia’s goal is to use AI to discover new drugs. How does AI help solve this problem?

Garry Pairaudeau: There are a multitude of ways our teams at Exscientia are using AI to optimize drug discovery and bring new, high-quality investigational medicines from the lab to the clinic more efficiently.

Discovering new drugs is precision-engineered technology. You’re creating compounds which interact with the body exactly as intended—efficacious when targeting disease, but also safe and well-tolerated by patients.

In drug discovery, there are billions of potential design options that we need to consider when making a drug. Using AI, we turn data into knowledge and create digital models of the desired drug properties. We then use a branch of AI called Generative Methods to precision engineer potent and exquisitely designed drug molecules. On top of that, by leveraging AI, we’re able to move projects faster, make better decisions, and carry out significantly fewer experiments in order to get to best-in-class, as well as first-in-class, medicines.

Carugati: Developing new drugs takes time. How can AI accelerate this timeframe to get products to market faster?

Pairaudeau: Our driving motivation is to change the drug design paradigm so that innovative and important medicines get to patients faster. By combining the analytical power of AI with the creativity and expertise of our world-class scientists, we have demonstrated that it is possible to reduce the time it takes to create a drug candidate to around 12 months. Our platform delivers a significantly more rapid and cost-effective route to discovering drug candidates than the industry benchmark.

For example, our drug candidate for obsessive-compulsive disorder, DSP-1181, developed in partnership with Sumitomo Dainippon Pharma and using Exscientia’s proprietary AI platform Centaur Chemist, was the result of a project which ran five times faster than typical drug discovery methods. The entire project took 12 months as opposed to the usual 4 to 5 years, with the candidate compound found within the first 350 synthesized compounds versus the typical 2,500 compounds. DSP-1181 was the world’s first drug designed by AI to be brought to the clinic, and we expect further successes in the near term.

Carugati: What type of data do you need for your work?

Pairaudeau: AI is usually associated with “big data” but in drug discovery particularly at the start of a project there is often very little known about the target. We have developed systems that make the most of these sparse data situations and enable us to learn our way forward using the fewest number of experiments.

Carugati: How would access to more or better-quality data improve the ability of companies like yours to use AI in healthcare?

Pairaudeau: Access to more and better-quality data is of critical importance as it allows our platform to deliver a more rapid and cost-effective route to discovering drug candidates. Higher volume quality data allows us to build even better models of critical properties like toxicity or metabolism and prioritize molecules more effectively. This enables us to shorten the pre-clinical drug discovery stage, and ultimately bring medicines to patients faster.

Carugati: How do you expect AI to shape drug development in the future?

Pairaudeau: The role of AI will be nothing short of transformational. In fact, we expect that by the end of the decade it will be commonplace for drugs entering into human clinical trials to have been designed with AI.

At Exscientia, we’re driving change in the drug discovery paradigm. Much like the COVID-19 pandemic demanded urgency in development of new vaccines and antibody treatments, we’re looking to inject that same urgency into the entirety of the drug research and development system.

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