arXiv:2606.27440v1 Announce Type: new Abstract: Foundation models for structural biology have achieved remarkable performance in predicting biomolecular structure and show promise for the design of proteins and small molecules. Yet understanding which internal features drive their outputs remains challenging. Standard sparse autoencoders (SAEs), effective on transformer-style sequence embeddings, do not transfer cleanly to pairformer-like architectures: naively operating on pairwise representations yields a quadratic blow-up of features and obscures concepts distributed jointly across sequence
This research addresses a critical challenge in structural biology foundation models, which are rapidly advancing but lack transparency regarding their internal mechanisms for protein design.
Understanding the internal workings of structural biology AI models is crucial for their reliable application in drug discovery, materials science, and synthetic biology, enabling more controlled and predictable outcomes.
The development of PairSAE offers a potential method for interpreting complex protein co-folding models, moving beyond the current 'black box' nature towards more explainable AI in structural biology.
- · Synthetic Biology Researchers
- · Pharmaceutical Companies
- · AI for Science Tool Developers
- · Biotech Sector
- · Researchers relying solely on black-box structural models
Improved interpretability of AI models for protein structure prediction and design.
Accelerated and more targeted development of new proteins, enzymes, and therapeutics.
Enhanced ability to engineer novel biological functions and materials with unprecedented precision.
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Read at arXiv cs.LG