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The goal is a sequence-only predictor whose accuracy holds on proteins it has never seen.","posed_since":"1998","precise":"For single-domain two-state proteins of 40 to 150 residues in water near $298\\,\\mathrm{K}$, predict $\\ln k_f$ ($k_f = \\text{folding rate in } \\mathrm{s}^{-1}$) from sequence alone; measured rates span approximately 8 decades, topology measures such as relative contact order correlate with $\\ln k_f$ at $r \\sim 0.8$ when the native structure is given, and sequence-only fits reach $r \\sim 0.8$ in-sample (Ivankov and Finkelstein, 2004). An answer is a method with a stated root-mean-square error, target below 1 unit of $\\ln k_f$, on a blind set of proteins with measured rates that are absent from its training data.","problem_ref":"034d04f3cc771a438d8414e0346e24e203583fc394f50573fde94b13fe643248","references":"","settled_by":"A blind test on newly measured folding rates of proteins absent from any training data, scored by root-mean-square error in $\\ln k_f$.","status_note":"Structure-prediction networks (AlphaFold 2 in 2021 and successors) give native structures, but no blind sequence-to-rate benchmark at this accuracy is known to this survey as of 2026.","title":"Predict a protein's folding rate from its sequence 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