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. Competing Interest Statement The authors have declared no competing interest. Footnotes Figure 1 has been revised to replace the previous schematic with an updated pipeline figure that more clearly summarizes the autoresearch loop for rule discovery. The manuscript text, results, conclusions, author list, and supplemental materials are otherwise unchanged.