drug-mmpbsa-gbsa (MM-GBSA / MM-PBSA)

Goal

To estimate relative binding free energies from MD trajectories using the single-trajectory MM-GBSA / MM-PBSA approach. For each trajectory frame, the method strips explicit solvent, evaluates potential energies of the complex, receptor, and ligand subsystems in implicit solvent, and computes:

dG = E_complex - E_receptor - E_ligand

The entropy term (-TdS) is omitted, which is standard practice when the goal is relative ranking rather than absolute binding affinity. MM-GBSA / MM-PBSA is most useful for re-ranking docked poses after MD refinement, providing an orthogonal signal to docking scores and geometric stability metrics.

Choosing a backend

Path	Script	When to use	Extras
OpenMM GBn2 (fast)	`compute_mmgbsa.py`	Throughput rescoring of HTVS hits; everything stays inside OpenMM with the same force field as the MD	No extra dependencies; ~1-5 minutes per compound on CPU
AmberTools MMPBSA.py	`compute_mmpbsa.py`	When you need PB (not just GB), per-method decomposition (ELE, VDW, EGB / EPB, ESURF), or a setup that matches what reviewers expect from the MM-PBSA literature	Adds `MMPBSA.py`, `cpptraj`, and `parmed` to the dependency surface (already in `drugmd-agent`); ~1-3 minutes for GB, ~5-30 minutes for PB depending on system size and frame count

Both paths give comparable GB rankings for typical drug-protein systems, but the absolute dG numbers will differ across backends because they use different GB models, radius sets, and surface-area treatments. Don't compare numbers across the two scripts.