Reality Check from Biotech Leaders on Using AI in Drug Discovery with BioXcel Therapeutics Gain Therapeutics iBio TRANSCRIPT

This roundtable on the role of AI in the biotech sector features Frank Yocca, Senior VP and Chief Scientific Officer at BioXcel Therapeutics, Joanne Taylor, Senior VP for Research at Gain Therapeutics, and Martin Brenner, CEO and Chief Scientific Officer at iBio. The conversation covers the historical adoption of AI in biotech, its current use in drug discovery, and future possibilities.  AI is not a new phenomenon in biotech and has evolved from data processing to sophisticated models that can screen vast amounts of data. There is a critical need for high-quality, structured data to train effective AI models, and these experts caution about the hype surrounding AI-generated discoveries and emphasize the need for real-world biological and human testing.

Frank explains, "We are all about AI right from the get-go. We sort of inherited that from the parent company, BioXcel, which is now BioXcel, LLC. The company started by deploying data science on big biomedical and other datasets. Much of the data was unstructured and required significant curation, which at first was largely manual. Later, we began deploying more natural language processing and knowledge graphs to predict whether drugs that initially failed but were safe could be repurposed for other indications. More recently, the latest evolution has really been to use large language models and more agentic workflows to generate hypotheses and insights."  

Joanne explains, "So Gain has had for many years, I think 10 years also, a virtual drug discovery platform where we've been able to screen millions of compounds virtually to discover allosteric binding molecules. But about three or so years ago, we made the change from screening millions of compounds to screening, now we're up to the capability of screening trillions of compounds."

"We can screen in days, whereas it would take you months and maybe a year to do high-throughput screening. But in terms of having introduced AI into this system, it means that we can do things better because obviously, if you can screen trillions of compounds, you're screening more of the possibilities, you are going to be making better drugs. At least that's the hypothesis than if you are screening fewer compounds. So it's the fact that this is a fast tool set that makes you able to do things that you wouldn't have been otherwise able to do, but it doesn't necessarily make the process itself that much faster because you are doing much more."

Martin elaborates, "So we had the good fortune to start from scratch. We're a very small company. We have made from the get-go the decision that our scientists would be bilingual. They're not only data and AI scientists, but they're also biologists. That makes it a lot easier to translate between the two disciplines. We literally started, or Rubrik Therapeutics started, on the hypothesis that would be a model of structure prediction for proteins. So the company was clearly ahead of its time, and we started by making molecules that set up better than existing ones. And that's, I think, a very low hurdle that a lot of people are doing right now. And you hear sometimes this overreaching argument: we make AI drugs. First of all, tomorrow medicines take 10,000 steps, and enabling three of them is not making an AI drug, but making better molecules. This was the first important step."

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