Track: Structure Prediction · LDDT-PLI: 0.5725 · BiSyRMSD: 3.41 · Coverage: 1.00
We generate a large ensemble of candidate poses with ESMFold2 and select one pose per fragment by a physics-based interaction energy rather than by model confidence.
1. Ensemble generation. ESMFold2, 85 independent random seeds with 5 diffusion samples each (about 425 poses per fragment). All fragments use the same PXR protein (293 aa) and a single precomputed MSA. No templates, no fragment-specific finetuning.
2. Pose scoring. Each candidate pose is protonated (protein hydrogens with PDBFixer
at pH 7.4, ligand hydrogens with RDKit from the CIF bond orders) and the hydrogens are
relaxed against frozen heavy atoms, so the predicted heavy-atom pose is preserved. A
single-point evaluation with the OrbMol-v2 machine-learned interatomic potential then
gives an interaction energy, E(complex) − E(protein) − E(ligand), and per-atom
forces.
3. Selection. For each fragment we discard poses whose maximum atomic force exceeds 20 eV/Å (broken geometry or clashes), then keep the surviving pose with the lowest interaction energy. If every pose fails the force gate, the least-strained pose is kept, so every fragment is covered.
Model confidence (ipTM) is recorded but not used to filter or rank.
- ESMFold2 (structure prediction)
- OrbMol-v2 (interaction-energy scoring)
- PDBFixer + OpenMM/AMBER14 (protein hydrogens), RDKit + MMFF (ligand hydrogens)