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Title
Japanese: 
English:Active learning for energy-based antibody optimization and enhanced screening 
Author
Japanese: 古井 海里, 大上 雅史.  
English: Kairi Furui, Masahito Ohue.  
Language English 
Journal/Book name
Japanese: 
English: 
Volume, Number, Page        
Published date 2024 
Publisher
Japanese: 
English:arXiv preprint 
Conference name
Japanese: 
English: 
Conference site
Japanese: 
English: 
Official URL https://arxiv.org/abs/2409.10964
 
DOI https://doi.org/10.48550/arXiv,2409.10964
Abstract Accurate prediction and optimization of protein-protein binding affinity is crucial for therapeutic antibody development. Although machine learning-based prediction methods ΔΔG are suitable for large-scale mutant screening, they struggle to predict the effects of multiple mutations for targets without existing binders. Energy function-based methods, though more accurate, are time consuming and not ideal for large-scale screening. To address this, we propose an active learning workflow that efficiently trains a deep learning model to learn energy functions for specific targets, combining the advantages of both approaches. Our method integrates the RDE-Network deep learning model with Rosetta's energy function-based Flex ddG to efficiently explore mutants. In a case study targeting HER2-binding Trastuzumab mutants, our approach significantly improved the screening performance over random selection and demonstrated the ability to identify mutants with better binding properties without experimental ΔΔG data. This workflow advances computational antibody design by combining machine learning, physics-based computations, and active learning to achieve more efficient antibody development.

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