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.
[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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