GearBind — это геометрическая графовая нейросеть, созданная для оптимизации антител и дизайна белков. ИИ использует контрастивное обучение на огромных массивах данных, чтобы с высокой точностью предсказывать и улучшать связывающую способность терапевтических молекул.
Increasing the binding affinity of an antibody to its target antigen is a crucial task in antibody therapeutics development. This paper presents a pretrainable geometric graph neural network, GearBind, and explores its potential in in silico affinity maturation. Leveraging multi-relational graph construction, multi-level geometric message passing and contrastive pretraining on massscale, unlabeled protein structural data, GearBind outperforms previous stateof-the-art approaches on SKEMPI and an independent test set. A powerful ensemble model based on GearBind is then derived and used to successfully enhance the binding of two antibodies with distinct formats and target antigens. ELISA EC50 values of the designed antibody mutants are decreased by up to 17 fold, and KD values by up to 6.1 fold. These promising results underscore the utility of geometric deep learning and effective pretraining in macromolecule interaction modeling tasks.