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May 02, 2022
Absci Senior Director, AI-assisted Drug Discovery Roberto Spreafico will be presenting, “Novel Deep Learning Models Enable Lead Antibody Optimization by Predicting Affinity and Naturalness of Sequence Variants” on Thursday, May 5th at 2:20 PM ET as part of the Machine Learning Approaches for Protein Engineering track. For more information please visit the conference home page or find the full agenda here.
Novel Deep Learning Models Enable Lead Antibody Optimization by Predicting Affinity and Naturalness of Sequence Variants
Therapeutic antibodies require optimization of binding affinity and other properties. Traditional engineering approaches are time-consuming and explore only a subset of the solution sequence space. To address these challenges, we assist antibody development with AI. Models trained with affinity measurements of sequence variants of trastuzumab could quantitatively predict the binding strength of unseen variants. Models can also score antibody sequences for naturalness by comparison with human antibody repertoires, mitigating downstream developability issues.
Presenter: Roberto Spreafico, Senior Director, AI-assisted Drug Discovery, Absci