Biologically-Grounded Multi-Encoder Architectures as Developability Oracles for Antibody Design

ArXi:2604.09369v1 Announce Type: cross Generative models can now propose thousands of \emph{de novo} antibody sequences, yet translating these designs into viable therapeutics remains constrained by the cost of biophysical characterization. Here we present CrossAbSense, a framework of property-specific neural oracles that combine frozen protein language model encoders with configurable attention decoders, identified through a systematic hyperparameter campaign totaling over 200 runs per property.