Image: Stef van Grieken, co-founder and CEO of Cradle (courtesy of Cradle)
AI is all the rage. Even starting an article with the sentence “AI is all the rage” has become its own kind of cliche. But beneath the hype are critical, underpinning technologies that could usher in the next generation of therapeutics and bring more effective solutions to patients more quickly. Even as large language models (LLMs) such as ChatGPT augment our daily lives, another kind of revolution is taking place across human health.
I sat down with Stef van Grieken, co-founder and CEO of Cradle, a company that has been leading machine learning in human health long before LLMs were part of the cultural zeitgeist. The generosity of his insights astounded me, from the current most significant challenges in AI and ML, hallucinating algorithms, and the surprising revelation that European big pharmas are edging out American ones
on AI.
Fiona Mischel: What is the promise of machine learning—and AI more broadly—for human health?
Stef van Grieken: “Machine learning is able to accelerate many parts of the drug development process, from early research to clinical validation. Ultimately, these techniques are tools that can either speed up the process, reduce cost, or lead to better, more efficacious, safer, and easier-to-manufacture drugs going into the clinic.
I’m excited about many companies that apply machine learning to human health, ranging from products like Causaly, helping scientists better understand their disease targets by getting accurate summaries of literature and data, Opentrons making it easier to program a protocol in your lab, understanding cell behavior with Spring, selecting reagents with BenchSci, or discovering and designing protein-based therapeutics with our company Cradle.”
FM: Where are we today in achieving such significant improvements in therapeutics with AI? What roadblocks are we facing?
SvG: “We are still in the early stages of applying these techniques to drug development. We recently saw the first machine learning-generated small molecule drug entering clinical trials, but the jury is still out. We are still discovering in what areas machine learning can have the most impact.
There are several issues that cut across the different application areas, though. The first is the lack of high-quality and volume of data needed to train and validate models. [This could be] experimental data on therapeutic proteins, clinical trial protocols, and results, or high-quality academic literature. Most of these datasets are locked up inside companies or publishers and cannot be used to develop better models.”
Read the full insights here →
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