{"version":"1.0","type":"rich","provider_name":"Acast","provider_url":"https://acast.com","height":250,"width":700,"html":"","title":"AI & Antibodies miniseries | The series concludes: our review with Guest Advisor Pin-Kuang Lai","thumbnail_width":200,"thumbnail_height":200,"thumbnail_url":"https://open-images.acast.com/shows/5f2442bc6de29f32c4d05451/1784630954446-26fa9a35-56dd-4d0d-85ce-d99c9010b237.jpeg?height=200","description":"

In this final episode of our miniseries covering the mAbs journal article collection on artificial intelligence and machine learning in antibody development, we speak to Pin-Kuang Lai, Assistant Professor at Stevens Institute of Technology and co-Guest Advisor for the collection.

Pin-Kuang reflects on the remarkable success of this collection, considering why it has proven so popular, detailing the key themes that emerged throughout it – from the rapid adoption of deep learning and foundation models to the growing emphasis on developability prediction and data integration – and highlighting how the field has changed even over the course of the collection.  

Catch this short episode to hear more about Pin-Kuang's vision of AI use in the near future, where multi-modal AI frameworks, physics-informed models, and AI as an experimental planning partner become a mainstay in the development of safer, more effective antibody therapeutics.


Contents

[00:00] Introductions

[01:40] Exploring the landscape of AI in antibody discovery and the aims of the article collection

[03:15] Achieving the aims of the article collection

[04:40] Highlights from the collection

[06:00] Key themes from the article collection

[07:10] Areas to explore further

[08:10] What is next for the field of AI in antibody therapeutic development?



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