AI structure predictions improve filtering for antibody design tests
Revisiting Structure-Based Filtering for AI-Designed Antibodies: Opportunities, Limits, and What Matters
Computational Engineering, Finance, and Science
Summary
AI methods can create lots of new antibody designs, but it’s hard to decide which ones to test in the lab. The authors studied if predicted 3D structures of antibodies help pick the best candidates. They found that details about the antibody-antigen interface help when choosing among designs for different targets, while confidence in antibody regions outside key parts helps when comparing designs for the same target. Their work shows current prediction tools provide useful but limited clues for selecting promising antibodies.
What this means in practice
- •For biopharmaceutical developers: Improve selection of AI-designed antibodies for lab testing by using structure-based confidence metrics from prediction tools.
- •For protein engineering teams: Use non-CDR confidence scores in predicted antibody structures to better rank candidates targeting the same antigen.
Authors
Langzhang Liang, Junchen Qian, Xiaojie Ma, Mingxi Zou, Yinghui Xu, Zenglin Xu, Tianlei Ying
Abstract
AI antibody design can generate far more candidates than can be experimentally tested, making post-generation filtering a central bottleneck. We study whether structure-prediction outputs can help prioritize AI-designed antibodies for experimental validation. We evaluate this problem on 4,184 experimentally characterized antibody-antigen pairs, combining newly generated wet-lab measurements with curated affinity data across diverse antigens and assay settings. We introduce two complementary filtering signals: Regional Interaction Contrast (RIC), which captures support for the predicted antibody-antigen interface, and Non-CDR Antibody Confidence (NAC), which measures antibody confidence outside the CDRs. Across AlphaFold 3, Protenix-v2, and ESMFold2, we find a striking difference in what information matters for filtering. Interface evidence is strongest when comparing candidates designed for different antigens, whereas antibody-level reliability is substantially more informative for ranking candidates within the same antigen. In particular, non-CDR antibody confidence consistently outperforms whole-antibody confidence across predictors, revealing an unexpected source of filtering signal beyond the predicted interface. Finally, we examine the information encoded in the predictor outputs themselves. Predicted structures contain substantial but incomplete binding-related signal, revealing both the opportunities and limits of structure-based filtering: better metrics can exploit current predictors more effectively, but their performance ultimately remains constrained by what those predictors can distinguish.