CHIMERA-Bench: A Benchmark Dataset for Epitope-Specific Antibody Design

Mansoor Ahmed, Nadeem Taj, Imdad Ullah Khan, Hemanth Venkateswara, Murray Patterson

Keywords: Antibody design, protein design, generative models, deep learning, benchmark, graph neural networks, sequence-structure co-design

TL;DR: CHIMERA-Bench is a unified benchmark dataset of 2,922 curated complexes for epitope-conditioned antibody CDR sequence-structure co-design.

Abstract: Computational antibody design has seen rapid methodological progress, with dozens of deep generative methods proposed in the past three years, yet the field lacks a standardized benchmark for fair comparison and model development. These methods are evaluated on different SAbDab snapshots, non-overlapping test sets, and incompatible metrics, and the literature fragments the design problem into numerous sub-tasks with no common definition. We introduce CHIMERA-Bench (CDR Modeling with Epitope-guided Redesign), a unified benchmark built around a single canonical task: epitope-conditioned CDR sequence-structure co-design. CHIMERA-Bench provides (1) a curated, deduplicated dataset of 2,922 antibody-antigen complexes with epitope and paratope annotations; (2) three biologically motivated splits testing generalization to unseen epitopes, unseen antigen folds, and prospective temporal targets; and (3) a comprehensive evaluation protocol with seven metric groups including novel epitope-specificity measures. We benchmark representative methods spanning different generative paradigms and report results across all splits. CHIMERA-Bench is the largest dataset of its kind for the antibody design problem, allowing the community to develop and test novel methods and evaluate their generalizability.

Submission Number: 96