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Poster
Antibody-Antigen Docking and Design via Hierarchical Structure Refinement
Wengong Jin · Regina Barzilay · Tommi Jaakkola

Thu Jul 21 03:00 PM -- 05:00 PM (PDT) @ Hall E #205
in Poster Session 3 »

Computational antibody design seeks to automatically create an antibody that binds to an antigen. The binding affinity is governed by the 3D binding interface where antibody residues (paratope) closely interact with antigen residues (epitope). Thus, the key question of antibody design is how to predict the 3D paratope-epitope complex (i.e., docking) for paratope generation. In this paper, we propose a new model called Hierarchical Structure Refinement Network (HSRN) for paratope docking and design. During docking, HSRN employs a hierarchical message passing network to predict atomic forces and use them to refine a binding complex in an iterative, equivariant manner. During generation, its autoregressive decoder progressively docks generated paratopes and builds a geometric representation of the binding interface to guide the next residue choice. Our results show that HSRN significantly outperforms prior state-of-the-art on paratope docking and design benchmarks.

Keywords: APP: Chemistry and Drug Discovery · Deep Learning · DL: Generative Models and Autoencoders

Author Information

Wengong Jin (MIT)
Regina Barzilay (MIT CSAIL)
Regina Barzilay

Regina Barzilay is an Israeli-American computer scientist. She is a professor at the Massachusetts Institute of Technology and a faculty lead for artificial intelligence at the MIT Jameel Clinic. Her research interests are in natural language processing and applications of deep learning to chemistry and oncology.

Tommi Jaakkola (MIT)

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