How Models Translate Targets into Candidates

We have been talking about computer-aided drug discovery for well more than a decade. It used to be the case that start-ups pitched their ability to use “machine learning” to hunt for new, promising therapies. Now we call machine learning “artificial intelligence” and have a new class of start-ups claiming big science breakthroughs.

One of these new wave start-ups is Chai Discovery and its founders Josh Meier and Jack Dent join the podcast this week. (The Core Memory podcast is available on all major platforms and on our YouTube pod channel.) The company was founded in 2024 and is backed by OpenAI, Anthropic, Thrive and others. (Chai is already a unicorn.) It published a number of notable accomplishments this last year, including using its own AI model to churn out promising antibody designs at an unprecedented clip.

The first couple iterations of machine learning-aided drug discovery companies came and went without tremendous success. Chai and Nabla Bio are two of the buzziest members of this new era of AI companies. Their models really do seem to be harnessing the advances in AI to hit on potential drug targets and designs in rather profound ways. Bio-tech, in fact, seems like the place where AI may make the most stunning scientific advances first.

In this episode, we get into Chai’s intellectual roots as a research project within Facebook/Meta and how the company has gone after building its models. We also try to provide a realistic picture of the current state of AI drug discovery.

The implications of the work done by Chai, Nabla and others are far reaching. If we’re able to come up with new drug designs at this accelerated rate, we will need major changes around how drugs are tested and put through trials. The current drug testing and FDA approval system is simply not set up to move as quickly as bio-tech appears to be going.

This will be our last episode for the year, and we’re taking a tiny break between posting the next one as the Core Memory crew has a little time off. Thank you so, so much to all of you who have listened to the show in our first year. We hope you’ve enjoyed it and learned some things along the way.

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