Thal-Kak: unifying biomolecular structure predictors reveals a sampling-selection gap
Bae, J.; Jo, S.; Kim, Y.; Kim, D.; Kim, K.; Park, S.; Park, S.; Myung, S.; Shin, H.; Kim, M. H.; Kang, M.; Baek, M.
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Complementary all-atom structure predictors sample different solutions, but how to allocate a fixed sampling budget across them and select the best output remains unclear. Thal-Kak unifies five released predictors under shared upstream inputs and a common schema. Across FoldBench and CASP16, model mixing improves oracle sampling over single-model runs, but selection remains a bottleneck because confidence scores do not transfer across models and existing quality-assessment methods cannot resolve this gap.
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