Rescoring that actually ranks your hits.
BScorer is a quantum physics–based rescoring engine for protein–ligand binding affinity — fast enough to sit downstream of docking, accurate enough to drive synthesis decisions.
For covalent inhibitors, a QM/QM/MM engine with DMET embedding delivers quantum-accurate reaction paths and barrier heights; for everything else, quantum-enhanced entropy and optimal embedding lift R² and AUC at a fraction of the cost.
Affinity correlation on program-relevant series — sharper than MMGBSA and fast enough to triage thousands of poses per hour.
Where lead-optimisation accuracy is needed, hand the shortlist to BFEP for free-energy calculations on the same physics stack.
Quantum-inspired physics, packaged as a rescoring engine your pipeline can call.
Physics-based affinity
Fast and accurate quantum physics–based prediction of protein–ligand binding affinity — a step beyond MMGBSA without paying full FEP cost.
Covalent binding (QM/QM/MM)
High-quality embedding via DMET enables quantum-accurate reaction paths and barrier heights for covalent inhibitors.
DMET embedding
Density Matrix Embedding Theory partitions the active site so the chemistry that matters gets quantum treatment, the rest stays classical.
Quantum entropy with BW2
Quantum-enhanced entropy lifts R² and AUC by capturing ligand librations the classical models miss.
Optimal model embedding
Quantum information theory drives the embedding choice — same accuracy at a fraction of the scoring cost.
MMGBSA baseline & diff
Run alongside MMGBSA out of the box so you can compare against the workflow your team already trusts.
How a BScorer rescoring run goes.
- 01Ingest docked poses from BDocker or third-party engine
- 02Assign protonation states & explicit waters
- 03Apply quantum-corrected entropy & H-bond physics
- 04DMET embedding for covalent reaction paths (optional)
- 05Compare with MMGBSA baseline side-by-side
- 06Export ranked affinity report with per-pose diagnostics