Thal-Kak: unifying biomolecular structure predictors reveals a sampling-selection gap

Authors

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.

Abstract

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.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 25 Aug 2026.

Stats

1-terrible, 9-excellent. How would you rate this preprint? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.