AbDiffuser: Full-Atom Generation of In Vitro Functioning Antibodies

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• Karolis Martinkus & Andreas Loukas (Prescient Design, Genentech, Roche)
• Tuesday 24 October 2023, 13:00-14:00
• Zoom: https://cam-ac-uk.zoom.us/j/92041617729.

If you have a question about this talk, please contact mj201.

Notice that this talk is online only: https://cam-ac-uk.zoom.us/j/92041617729 Pietro Lio and Chaitanya Joshi will introduce the speakers

We will discuss AbDiffuser, our latest equivariant and physics-informed diffusion model for the joint generation of antibody 3D structures and sequences. AbDiffuser is built on top of a new representation of protein structure, and utilizes strong diffusion priors to improve the denoising process. Our approach improves antibody diffusion by taking advantage of domain knowledge and physics-based constraints; handles sequence-length changes; and reduces memory complexity by an order of magnitude enabling backbone and side chain generation. We have validated AbDiffuser in silico and in vitro. Numerical experiments showcase the ability of AbDiffuser to generate antibodies that closely track the sequence and structural properties of a reference set. Laboratory experiments confirm that all 16 HER2 antibodies discovered using AbDiffuser were purified and expressed at high levels and that 57.1% of selected designs were novel tight binders.

ArXiv: https://arxiv.org/abs/2308.05027 NeurIPS 2023 Spotlight

Speakers Bio:

Karolis Martinkus is a Machine Learning Scientist at the Prescient Design team within Genentech Research & Early Development (gRED) where he works on generative models for de-novo antibody design. He completed his PhD at ETH Zurich under the supervision of Prof. Roger Wattenhofer, where he focused on applying deep learning to structured domains (e.g. graphs), in particular generative models.

Andreas Loukas is a Senior Principal Scientist and Machine Learning Lead at Prescient Design within Genentech Research & Early Development (gRED). His work focuses on the foundations and applications of machine learning to structured problems. He aims to find ways to exploit (graph, constraint, group) information, with the ultimate goal of designing algorithms that can learn from fewer data. He is also focusing on the theoretical analysis of neural networks and in using them to solve hard bioengineering problems (especially protein design). Andreas obtained his Ph.D. in computer science from TU Delft in 2015 and pursued postdoctoral studies at TU Berlin and EPFL . He became an SNSF Ambizione fellow at EPFL in 2018 and an Assistant Professor at the Computer Science department of the University of Luxembourg in 2021. He joined Genentech/Roche in 2022.

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