Autoresearch Discovery of Interpretable Filter Rules for Antibody Binder Classification

Abstract

Antibody design campaigns increasingly generate many candidates before only a small subset can be tested experimentally, making candidate filtering a central bottleneck. We study whether an autoresearch loop can discover better training-free filters for antibody binder classification by iteratively proposing rule variants, evaluating them under a fixed Leave-One-System-Out protocol, recording each experiment in version control, and using the results to guide the next iteration. Across 75 unique logged filter variants on seven antibody-antigen systems, the loop improves average ROC-AUC from 0.6371 for the initial baseline to 0.8060 for a compact final rule that we call the RMSD-Tuned Triad rule, an absolute gain of 0.1689 and a relative improvement of 26.5%. The discovered filter is competitive with supervised machine learning baselines and prompted LLM baselines evaluated on the same systems: it exceeds logistic regression (0.7144), feature-selected balanced logistic regression (0.7536), and GPT-4o tabular few-shot prompting (0.7640), and it comes within 0.0044 ROC-AUC of the strongest GPT-5 tabular few-shot result (0.8104). Unlike the LLM baseline, the final rule requires no prompted examples and no LLM inference once the numeric structure-derived features are available. These results show that systematic autoresearch can turn simple structural-confidence signals into compact, interpretable filters that are useful when target-specific training data are scarce.