Active learning for energy-based antibody optimization and enhanced screening
<|reference_start|>Active learning for energy-based antibody optimization and enhanced screening: Accurate prediction and optimization of protein-protein binding affinity is crucial for therapeutic antibody development. Although machine learning-based prediction methods $\Delta\Delta G$ 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 $\Delta\Delta G$ data. This workflow advances computational antibody design by combining machine learning, physics-based computations, and active learning to achieve more efficient antibody development.<|reference_end|>
@article{furui2024active,
    title={Active learning for energy-based antibody optimization and enhanced
  screening},
    author={Kairi Furui, Masahito Ohue},
    journal={arXiv preprint arXiv:2409.10964},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.10964},
    primaryClass={q-bio.BM cs.AI cs.LG q-bio.QM}
}