CAB: A Confidence-Aware Model for Predicting Binding Affinity Changes in Antibodies

Background: Computational prediction of antibody-antigen (Ab-Ag) affinity is increasinglyimportant for designing tumor-targeting therapeutics, enabling rapid in-silico affinity maturation,and optimization of formats such as bispecifics and ADC. However, existing models are trainedalmost exclusively on positive binding cases with measurable affinity, creating a positive-casebias that limits their ability to recognize true loss-of-binding events and often yields unreliableaffinity predictions for Ab-Ag pairs that are experimentally non-binding. Method: To avoid challenges in predicting accurate structures for mutated proteins, we adopteda sequence-to-function framework and fine-tuned sequence-only protein language models (e.g.,ESM2, DPLM) for mutation-level affinity prediction. We incorporated the AbAgym dataset(∼335k measurements) to address the under-representation of weak and non-binding cases. Tobetter separate binding from non-binding regimes, we added a sequence-pair contrastivelearning stage using validated binders as positives and randomly sampled non-binders asnegatives. After contrastive pretraining, we jointly optimized an affinity-change regression headand an AlphaFold-inspired confidence-score head to capture prediction uncertainty and flaglikely non-binders. This combined contrastive and multi-task strategy improves mutation-levelsensitivity and strengthens detection of loss-of-binding events essential for anti-tumor antibodydiscovery. Result: CAB enabled high-throughput in-silico screening of antibody mutation libraries and wasevaluated on both viral and tumor-associated antigen systems. For SARS-CoV-2 RBD, CABrapidly prioritized thousands of CDR variants and identified high-confidence mutations predictedto yield >10-fold affinity improvements, successfully recovering redesigns known from deepmutational scanning. For tumor targets such as HER2 and Plexin-B2, CAB similarly selected tophigh-confidence mutations with enhanced predicted affinity, consistent with experimentallyengineered antibodies. Discussion: Unlike conventional models that output a single affinity estimate regardless ofreliability, CAB’s confidence head enables explicit identification of low-confidence, likely non-binding variants, addressing a critical limitation of current prediction frameworks. This improvesthe trustworthiness of virtual screening and reduces false-positive redesigns. CAB provides anefficient computational engine for identification of promising variants against tumor-associatedantigens in cancer immunotherapy. Citation Format: Yuanfei Sun, Bing Jiang, Huiping Liu. CAB: A confidence-aware sequence model enabling high-throughput prediction of mutation-driven binding affinity change in antibody-based cancer therapeutics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 1479.