Autoresearch Discovery of Interpretable Filter Rules for Antibody Binder Classification
Date:
Invited Talk at NVIDIA Research (Digital Biology / BioNeMo), Santa Clara, CA, USA (virtual)
Invited talk to NVIDIA’s Digital Biology / BioNeMo and Fundamental Generative AI Research teams on an “autoresearch” loop in which an LLM acts as an automated experimenter: it proposes interpretable filter rules over signals already available in a design pipeline (AlphaFold3 confidence, ProteinMPNN, antibody structural self-consistency), evaluates them under strict held-out protocols, logs each result, and iterates. Across several benchmark datasets the loop discovers compact, human-readable scoring rules that reach parity with supervised ML and state-of-the-art LLM prompting, with no model training and no LLM call at inference. The talk closes by framing the loop as symbolic regression with an LLM in the experimenter’s seat, connecting it to the interpretable-ML and equation-discovery line of work.