Improving scoring functions for protein-protein docking with LambdaLoss
Modeling protein-protein interactions requires accurate scoring functions that can rank potential poses (conformations) of a protein-protein complex to differentiate near-native poses from incorrect ones. Here, we propose a general framework for improving protein-protein pose ranking and other biomolecular interaction models using the LambdaLoss loss function from the Learning-to-Rank field. We test this framework by fine-tuning the energy prediction head of DFMDock with the LambdaLoss on an augmented dataset of 2.9M decoy poses derived from the DIPS dataset. On targets from the CAPRI score set benchmark, our fine-tuned ranking model LambdaDockScore is better at identifying correct poses ...
| Authors | Richard Zhu, Darren Xu, Lee-Shin Chu et al. |
|---|---|
| Primary category | q-bio.QM |
| Published | 18 September 2026 |
| Last updated at source | 18 September 2026 |
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