# AI could shrink drug discovery from years to weeks

Markdown copy of https://realalpha.app/s/ai-could-shrink-drug-discovery-from-years-to-weeks/ 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 cofounder and CEO of Google DeepMind.
- Host: Konstantine Buhler · Partner, Sequoia
- Show: Training Data
- Episode: [Demis Hassabis: We're Three Quarters of the Way to AGI](https://realalpha.app/v/AFpeWo1GTeg/), Apr 29, 2026
- Moment: 9:01 to 12:47 of the episode (4 min), playing on https://realalpha.app/s/ai-could-shrink-drug-discovery-from-years-to-weeks/; the episode from there: https://realalpha.app/v/AFpeWo1GTeg/?t=541

## Quote

> “we could reduce drug discovery times instead of that for taking like, you know, an average of **10 years down to months**, maybe even weeks”
> — [Demis Hassabis](https://realalpha.app/builders/demis-hassabis/) at [12:09](https://realalpha.app/v/AFpeWo1GTeg/?t=729) · Training Data

## Takeaways

1. DeepMind began formal AI-for-science work after AlphaGo.
2. AlphaFold addresses one important part of drug discovery.
3. Isomorphic aims to design compounds computationally before lab validation.

## About this segment

Demis Hassabis says DeepMind built AI for science into its mission, then describes how AlphaFold and Isomorphic could automate much of drug discovery. If wet-lab work becomes validation, the process could shrink dramatically.

From “Demis Hassabis: We're Three Quarters of the Way to AGI”, Apr 29, 2026. [Watch the full episode](https://realalpha.app/v/AFpeWo1GTeg/)

## Transcript

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

[9:05](https://realalpha.app/v/AFpeWo1GTeg/?t=545) forefront of AI for science? Well, that was the ultimate goal, at least for me, my personal passion. There's one, there's my own drive to build AI, which was to advance science and medicine and our understanding of the world. It's my expression of that mission was to sort of do it in a meta way right build the build the the ultimate tool and then come back when that was ready and use it to make breakthroughs in science things like alpha folder that we've done and i think many more things so we've always had that at the heart of what we've been trying to do at deep mind so actually we've had an ai for science group division uh led by push me coley that um has existed for nearly a decade now actually pretty much the day after we got back from Seoul and the AlphaGo match, which is sort of 10 years to the month

[9:52](https://realalpha.app/v/AFpeWo1GTeg/?t=592) now, is when we started, formally started the AI for Science efforts, because I was waiting for the algorithms to be powerful. And the ideas to be general enough, and for me, you know, Cracking Go was that point, that time, that we thought, okay, now we're ready to really apply these ideas to important real world problems, starting with these big scientific challenges. So we've always had that in mind as the most beneficial use of AI, like what could be better than using it to cure diseases and give us healthier lifespans and to help with medicine, followed obviously by other really important areas like material science and the environment and energy and these kinds of topics, which I think AI is also going to have a huge part to play in the next few years.

[10:37](https://realalpha.app/v/AFpeWo1GTeg/?t=637) And how does AI break through in biology? You're deeply involved with isomorphic. This is an area of deep passion. You have been a purist on the potential of AI to cure diseases from the very beginning. When do we have the type of moment that we've had in language and coding, but in biology? Yeah, well, I mean, I'd argue we've already had one of those moments with alpha folds. So, you know, it's a 50-year grand challenge, protein folding, and the 3D structure of proteins is an incredibly important thing to know about if you want to design medicines or if you want to understand biology.

[11:12](https://realalpha.app/v/AFpeWo1GTeg/?t=672) of course it's only one part of the drug discovery process it's an important part but it's only one part so isomorphic labs which is our um you know later spin out uh having a lot of fun uh running that as well is is is to build adjacent technologies in more biochemistry and chemistry space that can actually design the compounds uh automatically to kind of fit and bind to the right part of the protein so we now know the protein the shape of the protein we know that what's on the on the surface of the protein and what we have to target um but now we've got to build the right compound that of course binds strongly to where you want it to bind on the target of interest but doesn't bind to anything else ideally because that would be a toxic side effect so um the dream is to do almost all the exploration uh which is 99 of the of the work

[12:00](https://realalpha.app/v/AFpeWo1GTeg/?t=720) and the time in silico and then save the the the wet lab step just for the validation step right So that would be, you know, I think if we can do that, and I think we can get there in the next few years, I think we could reduce drug discovery times instead of that for taking like, you know, an average of 10 years down to months, maybe even weeks, and perhaps even days one day. And then I think then all disease could be in reach.

[12:26](https://realalpha.app/v/AFpeWo1GTeg/?t=746) And I think things like personalized medicine will become possible. personalized variations off of base medicines. So I think the whole medical area, drug discovery areas, is going to be revolutionized in the next few years. Brilliant. You talked a lot about AI for science.