Tristan Free

speaker

289 appearances 6 recordings 1 series first heard Jun 2026 last heard 23 Jul

Tristan Free’s voice in public audio — every appearance, attributed to the second.

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recordings per month · last 12 months

Recordings per month over the last 12 months — 6 in all, peaking in Jun 2026 with 4.

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Talking Techniques · AI & Antibodies mini-series | Balancing binding affinity and therapeutic practicality · 9 Jun 2026 podcast

Ryan's contribution to the collection, a paper titled Alpha Bind, a domain-specific model to predict and optimize antibody antigen binding affinity, presents a model that can optimize antibody sequences to strengthen the binding affinity of novel antibody candidates.

And we're delighted to have him with us today to take us through the key concepts that run through that paper and that underlie this model.

Ryan, it's lovely to have you on the show.

So first off, Ryan, can you just tell us a bit more about yourself um and what it is you work on at A Alpha Bio?

Fantastic.

Uh and so your paper in this collection addresses the challenges in the design of novel antibody sequences.

So to to sort of outline that process, can you give us a uh an understanding or a brief outline of the process behind um sorry, the current process behind the design of novel antibody sequences and the challenges that are associated with that field?

Fantastic.

And and so can you present the model that's presented in your paper?

Um firstly maybe talking about how you trained it and where the data's come from.

Fantastic.

Um I have to say it still takes me uh a moment every time someone says and then that goes into the bin to realise that that's a good thing and it's been selected for use rather than it's it's getting thrown away.

And um yeah, so obviously that's uh yeah, thank you for that uh explanation of how it works and the and the kind of training process behind it.

Um so how did you then in this paper demonstrate the effectiveness of this model?

Fantastic.

And and so just to um put that into context, that a hundred f hundredfold increase in affinity, um, how big a difference is that to other methods of trying to optimize antibody binding and and um

Uh, what can that mean for the kind of l later success of the drug?

So it's really that that time saving then that you're getting from being able to to go back to a um a sequence that's on a computer rather than in front of you and having to manipulate it physically, um, to to kind of come to conclusions about about how you can adapt that um for for better use um or s for more applicable use later on down the down the um product pipeline.

Fantastic.

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