Future of Drug Discovery4 Apr 2023

Machine Learning-Based Design of Antibodies Against Difficult Targets - Ben Holland - #9

In this captivating podcast series, we dive deep into the world of computational antibody discovery with Ben Holland, our Co-Founder and CTO at Antiverse⁠. Join us as we uncover the immense potential of merging computational and experimental techniques in the pursuit of revolutionary antibodies, as Ben sheds light on the strong benefits and formidable challenges encountered when targeting complex molecules such as GPCRs. With an emphasis on precision and accuracy in predicting antibody-antigen interactions, Ben shares remarkable insights and breakthroughs achieved by computational discovery techniques. Tune in and join us at the forefront of antibody discovery!

Things mentioned:

⁠⁠Ben Holland, Co-Founder and CEO of Antiverse.

⁠⁠⁠⁠Antiverse⁠, an AI-first biologics discovery company that designs antibodies against difficult -to-drug targets.

Watch the full presentation, with slides! https://youtu.be/v8-ZrR7L83s

Watch the full Computational Antibody Discovery Symposium, hosted by The Antibody Society, NaturalAntibody and AstraZeneca! https://youtube.com/playlist?list=PLWG3bWQmCPkPPfDhqiRqKWmqHYumJXlHq

Additional Materials:

⁠⁠⁠Contact us about your discovery projects by emailing ⁠partnerships@antiverse.io⁠.

About Antiverse:

Antiverse was co-founded in 2017 with the goal of engineering the future of drug discovery to change people's lives. Based in Cardiff, Antiverse combines machine learning and phage display techniques to model antibody-antigen interactions. The current version of the platform uses next-generation sequencing and AI to design diverse antibody candidates for any given target. The technology is being developed to enable the discovery of biologics for difficult-to-drug targets associated with cancer, heart, and lung diseases.

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