AdaProteina: An Adapter Is All You Need for Developable Antibody Generation

Modern flow-matching generators for antibody design sample from an unconditional prior and offer no control over the physicochemical properties (binding, humanness, aggregation) that determine therapeutic viability. Retraining the 160M-parameter denoiser under a conditional objective is expensive and must be repeated per property specification. Classifier-free guidance trades sample diversity for conditioning strength and doubles per-step inference cost. We propose AdaProteina, a lightweight noise-space adapter that leaves the denoiser and VAE decoder of La-Proteina entirely frozen and instead learns a refiner $g_{\varphi }$ mapping base Gaussian noise and a class label to a developability-aware initial latent, with $|\varphi |<<|\theta |$ learnable parameters and no change in per-sample inference cost. Training combines cross-entropy against a frozen developability classifier with a per-class MMD loss that anchors refined latents to the data manifold and prevents the mode collapse, e.g., tyrosine/tryptophan-dominated sequences induced by naïve property optimization. To compose multiple developability properties without training a combinatorial number of joint classifiers, we introduce a Kernel Perron–Frobenius transfer operator that maps between per-property noise spaces in closed form, enabling training-free generation of, e.g., human-like low non-specific binders. AdaProteina improves substantially over La-Proteina on sequence diversity, developability separability, and every structural-validity metric evaluated and augmenting downstream training. To the best of our knowledge, this is the first large scale exploration of noise-space refinement for developability-aware antibody generation.