Welcome to Talking Techniques! In this Podcast BioTechniques Digital Editor Tristan Free, interviews researchers at the forefront of their fields about the latest breakthroughs, controversies and conversations in the life sciences. From CRISPR to COVID-19, organoids to the microbiome, this podcast will explore the latest developments in the lab and interesting applications of techniques, while trying to determine how we can drive science forward in progressive and inventive ways.
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AI & Antibodies miniseries | The series concludes: our review with Guest Advisor Pin-Kuang Lai
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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6 Jul 2026
AI & Antibodies miniseries | Nanobody thermostability prediction and the great data challenge
In this episode, the fifth in our miniseries covering the mAbs journal article collection on artificial intelligence and machine learning in antibody development, we speak to Aubin Ramon, Postdoctoral Research Associate in the Sormanni Lab at Imperial College London (UK), about his paper in the collection: Prediction of protein biophysical traits from limited data: a case study on nanobody thermostability through NanoMelt.
Aubin addresses one of the most persistent challenges in applying AI to specialized fields like antibody therapeutics: the scarcity of high-quality training data. Through the development of NanoMelt, he demonstrates how a combination of sophisticated modeling approaches, general protein stability prediction models, and robust validation pipelines can achieve meaningful predictions with training datasets of just 600-700 sequences.
Together, we explore why data quality often matters more than quantity, the counterintuitive finding that general protein models can outperform antibody-specific ones, and how NanoMelt is already being applied in synthetic library design and therapeutic development, with exciting improvements on the horizon in NanoMelt 2.
Contents
[01:40] The data availability problem in AI and a novel solution for it
[06:05] Achieving accuracy with small data sets
[09:05] Is this model accurate enough to be truly useful?
[12:50] General vs highly specific models for themostabilty prediction
[15:40] The limits of just generating more data
[19:20] Adapting the model for more complex/full length antibodies
[21:05] Advice for using NanoMelt for your own work
[24:15] Applications of NanoMelt in current research and announcing NanoMelt2
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22 Jun 2026
AI & Antibodies miniseries | Designing smart antibodies in the age of AI
In this episode, the fourth in our miniseries covering the mAbs journal article collection on artificial intelligence and machine learning in antibody development, we speak to Andrew Buchanan, Senior Vice President of Discovery at a biotech company currently in stealth mode, and former Principle Scientist at AstraZeneca, about his paper in the collection: How to think about designing smart antibodies in the age of GenAI: integrating biology, technology, and experience.
Andrew provides a holistic overview of how AI and machine learning are transforming the design of smart antibodies – the more complex evolution of monoclonal antibodies that can bind multiple receptors and utilize different mechanisms of action. Together, we explore the critical role of establishing robust candidate drug target profiles (CDTPs), the current capabilities and limitations of AI in structural antibody design, and how the simultaneous rise of multi-specificity and AI-driven approaches is reshaping the field.
Contents
[02:10] Exploring the simultaneous rise of AI and multi-specificity in therapeutic antibody design
[04:20] Establishing a candidate drug target profile with AI
[06:50] Limitations of AI in the development of a CDTP
[08:20] AI in practical therapeutic antibody design
[10:45] How industry and academia can work together to overcome current limitations in the use of AI in antibody therapeutic design
[13:45] Exciting recent applications of AI in antibody design
[16:32] Predictions for the next 5 years of AI in antibody design
[18:10] If I could grant you a wish to improve the abilities of AI in antibody development, what would it be?
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