{"version":"1.0","type":"rich","provider_name":"Acast","provider_url":"https://acast.com","height":250,"width":700,"html":"<iframe src=\"https://embed.acast.com/$/5f2442bc6de29f32c4d05451/6a26f9e36642088a1052cada?\" frameBorder=\"0\" width=\"700\" height=\"250\"></iframe>","title":"AI & Antibodies mini-series | Balancing binding affinity and therapeutic practicality","thumbnail_width":200,"thumbnail_height":200,"thumbnail_url":"https://open-images.acast.com/shows/5f2442bc6de29f32c4d05451/1780938882678-4d378ff2-8e18-4a34-9769-5495be942b14.jpeg?height=200","description":"<p>In episode two of our AI &amp; Antibodies mini-series, we speak to Ryan Emerson, Senior Vice President of Data Science at A-Alpha Bio, to discuss AlphaBind, A-Alpha Bio’s antibody-antigen binding-affinity prediction model.</p><p>We discuss how this model was trained, how it operates and how it has enabled researchers to test mutations designed to optimize an antibody candidate for critical quality attributes computationally, assessing their likely impact on binding affinity, before returning to the wet lab. The conversation also explores the future of AI in antibody engineering and the critical role of high-quality data in advancing the field.</p><p><br></p><h2>Contents</h2><p>[02:20] Current challenges in antibody sequence design </p><p>[04:20] Presenting AlphaBind</p><p>[08:40] Demonstrating AlphaBind’s effectiveness</p><p>[11:40] Benefits of AlphaBind and it’s applications</p><p>[16:05] How to make the most of AlphaBind</p><p>[19:25] Current use of AlphaBind </p><p>[23:00] Predictions for the impact of AI in antibody engineering</p><p>[25:40] A brief detour into the uses of AI in drug design (<a href=\"https://www.the-scientist.com/chatgpt-and-alphafold-help-design-personalized-vaccine-for-dog-with-cancer-74227\" rel=\"noopener noreferrer\" target=\"_blank\">See this story for more detail</a>)</p><p>[26:45] What wish could be granted to improve AI in antibody design?&nbsp;</p>","author_name":"BioTechniques"}