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New method extracts protein contacts from AI models in single pass

Researchers have developed a method to extract protein contact information from language models more efficiently. This new approach, which involves analyzing a small subset of attention heads, requires only a single forward pass compared to the multiple passes needed by previous methods. The technique demonstrates comparable or superior performance to existing methods on bidirectional models, suggesting that protein contact signals are already concentrated within specific attention heads. AI

IMPACT This research could lead to more efficient analysis of protein structures using AI, potentially accelerating biological research and drug discovery.

RANK_REASON The cluster contains an academic paper detailing a new method for analyzing protein contacts using AI models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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New method extracts protein contacts from AI models in single pass

COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Rome Thorstenson ·

    Protein contacts are already in the attention: a single-forward-pass alternative to the Categorical Jacobian

    arXiv:2606.21876v2 Announce Type: replace-cross Abstract: The Categorical Jacobian of Zhang et al. (2024) reads protein contacts from a language model by perturbing every residue with every alternative amino acid, about $19L$ forward passes. We show the signal it reconstructs is …