HyperBind2: Multi-Shot Learning Enables Progressive Improvement in Computational Antibody Discovery

Abstract

Antibody discovery remains constrained by resource-intensive experimental screening approaches that offer limited control over critical properties. Here we present HyperBind2, a machine learning platform that progressively improves antibody-antigen interaction predictions through experimental feedback cycles. Unlike static or zero-shot computational approaches, HyperBind2 employs multi-shot learning, which adapts to target-specific patterns using minimal experimental data (10-20 validated binders/non-binders). The platform requires only the targets primary sequences as input (no experimental structures are required), embedding antibody and antigen sequences into a shared representation space where binding affinity is modeled as a learned geometric relationship. HyperBind2 was validated at multiple independent academic and commercial labs. One such experiment validated HyperBind2 on a challenging multi-pass membrane receptor target through three iterative design-test cycles. Starting from an initial screening of 100 million candidates completed within 48 hours, model accuracy improved from 65% to 85% across three rounds of lab-to-AI feedback. By round 3, HyperBind2 achieved a 21% experimental success rate, with 20 of 96 tested candidates demonstrating KD [≤] 100 nM, including 3 with sub-10 nM affinities. HyperBind2 spans multiple therapeutic formats including scFvs, VHHs, and full-length IgGs, with preliminary research extending to CAR-T, BiTE, and bispecific formats. HyperBind2 establishes an efficient digital-experimental workflow that reduces laboratory resources and screening time while maintaining high hit rates. By combining massive computational pre-screening with targeted experimental validation and continuous model refinement, HyperBind2 significantly reduces experimental burden while accelerating the identification of therapeutic quality antibody candidates. HyperBind2 is available via open-source for academic research or a commercial platform (abtique.com), which provides lab-ready antibody sequences with no coding required. DisclaimerDue to the sensitive nature of intellectual property and confidentiality agreements, target identities have been anonymized where research is ongoing or proprietary. While we provide detailed descriptions of experimental methods, all details cannot be disclosed. The results have been independently validated by third parties, enhancing their reliability.