arXiv:2605.21610v1 Announce Type: new Abstract: Antibody design methods condition on antigen structure to generate complementarity-determining regions (CDR), yet a systematic evaluation of baseline methods reveals that they largely ignore the antigen input. We identify three failure modes that explain this behavior. Antigen blindness arises because models derive predictions from antibody framework context rather than antigen information, producing nearly identical CDRs regardless of the target. Vocabulary collapse reduces predicted amino acids to three to five per position, far below the groun
The proliferation of AI in drug discovery and biotechnology is leading to more sophisticated application and evaluation of generative models for complex biological problems like antibody design, pushing the boundaries of what is possible.
Advanced and reliable generative antibody design directly accelerates therapeutic development, potentially creating novel treatments faster and more affordably, impacting healthcare and biodefense.
The ability to accurately condition generative models on antigen structure significantly improves the efficacy and specificity of designed antibodies, reducing trial-and-error in drug development.
- · Biopharmaceutical companies
- · AI-driven drug discovery platforms
- · Patients with complex diseases
- · Immunology researchers
- · Traditional antibody discovery methods
- · Companies reliant on high-throughput screening alone
More efficient and targeted antibody therapeutics will enter clinical trials faster.
The cost of developing new biological drugs could decrease, expanding access and market opportunities.
Enhanced programmable biology capabilities could lead to new avenues for disease prevention, synthetic immunity, and even biomanufacturing of other complex proteins.
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Read at arXiv cs.LG