Autonomous agent designs target-specific MSAs to improve AlphaFold 3 complex prediction
SJTU · hf · 2026-11-04
A team from SJTU, Fudan, BUPT and Shanghai AI Lab released a paper introducing an autonomous research agent that adaptively organizes multiple sequence alignments (MSAs) for AlphaFold 3.
- The paper argues current pipelines use uniform MSA recipes (maximizing depth and species-based pairing), but the value of depth and cross-chain pairing is strongly target-dependent — no single recipe is optimal across interfaces.
- The agent treats a frozen AF3 as a black-box experiment platform, reasoning over biological hypotheses and running self-directed experiments to design target-specific MSAs, improving protein-protein interface prediction with fewer sequences.
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