Quality data and cutting-edge GenAI speed drug discovery

Every day around the globe, drug researchers are working on developing therapies and finding cures to our ailments. It is work that takes knowledge, skill and ingenuity – but maybe most of all, it takes time. And that time is costly – in financial terms, and more importantly in lost quality of life for the patients awaiting these discoveries.

That’s why industry insiders took notice of the announcement of Elsevier’s multiyear partnership with the innovative French AI company Iktos to deliver an AI-driven synthetic chemistry platform for small molecule development. In short, the world’s largest and highest-quality chemistry database, Elsevier’s Reaxys, is opening itself up to Iktos’s cutting-edge AI for drug discovery. It’s a combination that will undoubtedly accelerate pharmaceutical companies’ Design-Make-Test-Analyze chemistry research cycle.

Elsevier is bringing its data, broad market appeal, user-friendly interface and application programming interfaces (APIs) to the partnership. Meanwhile, Iktos offers its best-in-class retrosynthesis AI technology, which has already proven itself with the world’s most important pharmaceutical companies.

To find out more, we chatted with the two main people behind the partnership: Iktos CEO and co-founder Yann Gaston-Mathé and Reaxys Innovation Director Dr Abhinav Kumar.

How do you quickly describe your job to those outside the industry?

Yann: Essentially, Iktos is helping chemists, scientists and researchers find new drugs faster thanks to data and AI.

Abhinav: For a non-technical audience, I usually use the analogy of Google Maps — that we’re trying to design a Google Maps for drug discovery whereby you enter your destination: a certain new drug with certain characteristics.

That’s a great analogy. Because like Google Maps, you can also potentially take different routes to your destination: the fast one, the cheap one, the most sustainable one, etcetera.

Abhinav: It’s a tried and tested analogy. And it nicely bypasses those more complex ideas around neural networks and machine learning that many often struggle with.

What do you wish everyone knew that would make your job easier?

Abhinav: I’d say AI or machine learning or any such related tools are there to enhance your day-to-day work and increase your productivity. People should not be afraid of adopting these tools. They are not out to take your job.

Yann: I concur. It is strange how people are often suspicious and skeptical about AI while also having unrealistic expectations regarding AI. It would be great if more people knew that it’s more about helping make better decisions — and how this will lead to improvements over time. AI is rarely the magic bullet. But of course, we are making real progress step by step. We are getting closer to dramatically changing the world of drug discovery.

And this is another thing I wish more people knew: We are in a great era of drug discovery, especially since AI in drug discovery poses no ethical issue of any kind compared to other areas. We are fortunate here. We don’t even work with patient data, so we don’t have to worry about privacy and data misuse issues. We essentially only work with data from chemical testing. And our task is straightforward: to help you develop better drugs more quickly for the benefit of patients. We are improving a process known to be very difficult, expensive and time-consuming.

Yann, you’ve said, “AI is not a revolution.”

Yann: Certainly, it’s not in our field of drug discovery. It's more of an acceleration of a trend that started around the 1980s with what was called computer-assisted drug design. Now, we are witnessing an acceleration because AI is becoming more available. It’s also an attitude shift: you can no longer ignore the fact that technology can help you be more efficient, faster, and so forth.

Abhinav: I'm always suspicious of new people entering the field of AI and overselling what technology can deliver — that whole ‘revolution’ aspect. This leads to unreasonable expectations and disappointments — which is a shame since technology can now bring something to the table.

As we speak, you are gearing up to launch your first collaborative product — a significant milestone. How do you see the partnership evolving in the coming years?

Abhinav: Well, first things first. The upcoming product is still our focus: delivering it and enhancing it. The Iktos team has already done some additional great science, and we will work to incorporate that while making the overall enhancements required by any 1.0 version of a product. As for the medium and long term, there are other predictive models to build to accelerate the drug discovery process. We will explore other products we can build together in this area of de novo design and broader predictive chemistry.

Yann: I see it similarly. I hope in one year we can celebrate some very successful commercial achievements with several major pharmaceutical accounts that have been convinced of the value of our product. Early success is key to long-term success. Longer term, there are many opportunities to develop new technology by leveraging the fantastic Elsevier Reaxys data.

Where do you see the biggest challenge in maximizing this partnership’s potential?

Abhinav: Today, there’s potentially AI for everything. The challenge will be to work together to identify which of these many opportunities we’re willing to pursue. And I'm sure we will make a very well-informed decision together given the customer obsession and scientific expertise on both sides.

Enjoy the whole conversation with Yann and Abhinav by reading the full interview on Elsevier Connect.