# AI drug design must still clear the clinical-trial bottleneck

Markdown copy of https://realalpha.app/s/ai-drug-design-must-still-clear-the-clinical-trial/ on Real Alpha, the best minutes from long interviews with the people building AI. Cite and link the page, not this copy.

- Speaker: [Demis Hassabis](https://realalpha.app/builders/demis-hassabis/) · Demis Hassabis is co-founder and CEO of Google DeepMind.
- Host: [Harry Stebbings](https://realalpha.app/people/harry-stebbings/) · Founder, 20VC · Built a venture firm on his podcast; its third fund closed at $400M.
- Show: 20VC
- Episode: [Demis Hassabis: Why AGI is Bigger than the Industrial Revolution & Where Are The Bottlenecks in AI](https://realalpha.app/v/SSya123u9Yk/), Apr 7, 2026
- Moment: 12:18 to 15:22 of the episode (3 min), playing on https://realalpha.app/s/ai-drug-design-must-still-clear-the-clinical-trial/; the episode from there: https://realalpha.app/v/SSya123u9Yk/?t=738

## Quote

> “But I think the real revolution will come when a few, maybe **a dozen or so AI drugs** get through the whole process.”
> — [Demis Hassabis](https://realalpha.app/builders/demis-hassabis/) at [12:18](https://realalpha.app/v/SSya123u9Yk/?t=738) · 20VC

## Takeaways

1. Isomorphic Labs is working on the chemistry beyond protein folding.
2. AI could help model metabolism and match drugs to patients.
3. A first wave of successful AI drugs could support later regulatory changes.

## About this segment

Demis Hassabis sees AI accelerating drug design, then helping improve clinical trials through better modeling and patient selection. He expects regulators will need successful AI drugs as evidence before changing the process.

From “Demis Hassabis: Why AGI is Bigger than the Industrial Revolution & Where Are The Bottlenecks in AI”, Apr 7, 2026. [Watch the full episode](https://realalpha.app/v/SSya123u9Yk/)

## Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

[12:31](https://realalpha.app/v/SSya123u9Yk/?t=751) many people have different concerns if we just start generally what does that world look like well i think on the positive side and the things obviously i've i spent my whole career in life building towards agi is i think it will be the ultimate tool for science and medicine so in terms of advancing scientific discovery finding cures to diseases i think we need that kind of technology and so i hoping um in five years plus time we'll be sort of entering a new golden era our golden age of scientific discovery so my mother's got multiple cirrhosis so it's like something it's the thing that i'm always most excited about yeah the thing i worry about is actually kind of drug discovery the process of getting it through all the trials and knowing that it takes a decade before my mother will actually get any benefits from it how do we solve

[13:16](https://realalpha.app/v/SSya123u9Yk/?t=796) that yeah so i think we'll get to that point soon first of all what we're doing is you know after we did the alpha fold project to do protein folding then we spun out a company called isomorphic labs which is doing extremely well and that is supposed to you know the idea there is we're focusing on solving the rest of the drug discovery process which is a lot of chemistry designing the compounds checking it's not toxic and all the different properties you need for drugs to be safe i think we'll have that whole drug design engine ready in the next five plus five to 10 years, then you're right. The next problem is the clinical trials still take many, many years. But I think AI can help there in terms of simulating parts of the human metabolism, also stratifying patients to make sure that certain patients get exactly

[14:01](https://realalpha.app/v/SSya123u9Yk/?t=841) the right type of drug that's suitable for their genomic makeup. And so I think AI can help there too. But I think the real revolution will come when a few, maybe a dozen or so AI drugs get through the whole process. And then the government and the regulatory body see that and they have enough data to sort of back test the predictions of those models. And then maybe what we can do will be in the future where maybe 10 further years where we can really just trust the predictions that the models are making and actually then maybe skip out some steps, perhaps like the animal testing is not needed anymore. Maybe we can go up the dosage ladder quicker because you can rely on these models. So I think we've got to do it in two steps, solve the drug design problem first, and then look at the regulatory length of time it takes.

[15:09](https://realalpha.app/v/SSya123u9Yk/?t=909) Speaking of regulatory, AI safety is a big topic and a big concern.