Learning to design protein-protein interactions with enhanced generalization

arXiv:2310.18515v3 Announce Type: replace
Abstract: Discovering mutations enhancing protein-protein interactions (PPIs) is critical for advancing biomedical research and developing improved therapeutics. While machine learning approaches have substantially advanced the field, they often struggle to generalize beyond training data in practical scenarios. The contributions of this work are three-fold. First, we construct PPIRef, the largest and non-redundant dataset of 3D protein-protein interactions, enabling effective large-scale learning. Second, we leverage the PPIRef dataset to pre-train PPIformer, a new SE(3)-equivariant model generalizing across …

abstract advanced arxiv beyond biomedical construct cs.lg data design interactions machine machine learning practical protein research struggle therapeutics training training data type work