Towards an Agentic AI Framework for Generating, Optimizing and Filtering Protein Binders

De novo protein binder design is increasingly limited by experimental false positives: many candidates appear plausible in silico but fail during expression, assay, folding or binding affinity characterization. We present an Agentic AI framework for protein binder generation, optimization, and filtering that explicitly treats candidate selection as a wet-lab risk-minimization problem. The framework combines (i) epitope-conditioned generation with PBind42, (ii) tri-model optimization using Boltz-2, ProteinMPNN, and a PBind42 prior, and (iii) deterministic multi-agent filtering system, DyRA, that evaluates structural confidence, assay compatibility, interface quality, and expression plausibility before synthesis. Across two oncology benchmark targets, BHRF1 and PD-L1, the workflow reduces 500 generated candidates per target to compact 24-design plates and recovers two confirmed binders for each target. The best BHRF1 binder reaches a mean KD of 176 nM, while the best PD-L1 binder reaches a mean KD of 217 nM, with a second PD-L1 binder at 704 nM. These results show that protein-language-model generation can be made experimentally actionable when coupled to manifold-preserving optimization and agentic pre-synthesis filtering, substantially reducing the number of designs that must be synthesized and assayed.