MM-GBSA rescores docked poses with physics-based energetics rather than a fitted docking score. It's the final step in the docking arc (pocket → pose → rescore): run it on the candidate poses you generated with DiffDock. See what you can achieve for the full arc.

⚠︎ This implementation returns no binding free energy and no binding-strength verdict. It performs a single-point OpenMM evaluation of the minimized complex, so delta_g_bind is always null. Read how MM-GBSA results compare to the bench before using any number from it.

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What MM-GBSA does

MM-GBSA (Molecular Mechanics / Generalized Born Surface Area) is a post-docking rescoring method that estimates protein-ligand binding free energies by combining molecular mechanics force field energies with implicit solvation. The canonical form computes ΔG_bind = E_complex − E_receptor − E_ligand.

Citations

Massova I, Kollman PA. Combined molecular mechanical and continuum solvent approach (MM-PBSA/GBSA) to predict ligand binding. Perspectives in Drug Discovery and Design. 2000;18:113-135. doi: 10.1023/A:1008763014207

Eastman P, et al. OpenMM 7: Rapid development of high performance algorithms for molecular dynamics. PLoS Comput Biol. 2017;13(7):e1005659. doi: 10.1371/journal.pcbi.1005659

Key achievements

Physics-based rescoring: energies grounded in molecular mechanics force fields rather than a fitted scoring function. MM/GBSA rescoring has been shown to improve a docking program's screening and ranking power on tyrosine kinases (citation); it does not make the result an affinity measurement
Energy decomposition: Breaks down binding energy into van der Waals, electrostatic, polar solvation, and non-polar solvation components for mechanistic insight
Per-residue analysis: Identifies individual amino acid residues contributing most to binding, enabling rational mutagenesis and optimization
GPU acceleration: OpenMM-based implementation delivers results in minutes rather than hours

Core capabilities

Binding free energy estimation: Calculates DeltaG_bind from the thermodynamic cycle (complex - receptor - ligand)
Energy decomposition: Reports van der Waals (DeltaG_vdw), electrostatic (DeltaG_elec), polar solvation (DeltaG_polar), and non-polar solvation (DeltaG_nonpolar) terms
Per-residue decomposition: Identifies binding hotspot residues by decomposing the total energy into per-residue contributions
Binding classification — removed. No binding-strength category and no estimated Kd is emitted — the classifier had been fed the complex's raw potential energy and returned "very strong" for everything. See why a ΔG → Kd conversion is not defensible
Multi-pose rescoring: Scores multiple docked poses and ranks them by binding free energy
Trajectory analysis: Optionally runs short MD equilibration before energy evaluation for conformational sampling

Technical approach

MM-GBSA follows a three-step thermodynamic cycle:

Complex energy: Calculate the total energy of the protein-ligand complex using the selected force field and implicit solvent model
Receptor energy: Calculate the energy of the protein alone (with the ligand removed) in the same force field
Ligand energy: Calculate the energy of the ligand alone in the same force field
Binding energy: DeltaG_bind = G_complex - G_receptor - G_ligand

Each energy term is decomposed into:

DeltaG_vdw: Van der Waals interactions (Lennard-Jones potential)
DeltaG_elec: Electrostatic interactions (Coulombic potential)
DeltaG_polar: Polar solvation energy (Generalized Born model)
DeltaG_nonpolar: Non-polar solvation energy (proportional to solvent-accessible surface area)

The Generalized Born (GB) implicit solvent replaces explicit water molecules with a continuum dielectric, providing solvation energies at a fraction of the computational cost of explicit solvent simulations.

When to use MM-GBSA

Primary use cases

Post-docking rescoring

Pose ranking: Re-rank docked poses from DiffDock or other docking tools using physics-based energetics
Hit prioritization: Distinguish true binders from false positives in virtual screening campaigns
Consensus scoring: Combine MM-GBSA scores with docking scores for improved hit rates

Binding mechanism analysis

Hotspot identification: Identify which residues contribute most to binding for structure-activity relationship studies
Energy decomposition: Understand whether binding is driven by van der Waals contacts, electrostatics, or solvation effects
Mutant impact prediction: Estimate how point mutations at binding site residues might affect binding affinity

Lead optimization support

Analog ranking: Compare binding energies across a series of analogs to guide medicinal chemistry optimization
R-group analysis: Evaluate how different substituents affect binding through per-residue energy contributions
Selectivity analysis: Compare binding energies against on-target vs. off-target structures

Webapp integration and workflow

Input methods in .bloom*

From DiffDock results (most common):

Select a completed DiffDock job from the canvas or job panel
.bloom* automatically extracts the docked complex, protein, and ligand files
Choose which poses to rescore (top 1, top 5, or all)

Manual upload:

Upload a protein-ligand complex PDB file containing both receptor and ligand
Or upload separate protein PDB and ligand SDF files
.bloom* validates the complex structure and identifies the ligand automatically

From previous jobs:

Import complexes from any docking tool that produces PDB output

Solvent model selection

The tool always parameterizes with the AMBER14 force field (ff14SB for the protein, GAFF2 for the ligand) — there's no force-field selector. solvent_model picks the implicit-solvent (Generalized Born) term:

gb_obc2 (default): Onufriev-Bashford-Case II model with good accuracy-speed balance
gb_obc1: Onufriev-Bashford-Case I model, faster with slightly lower accuracy
gb_hct: Hawkins-Cramer-Truhlar model for fast approximate solvation

Integration with .bloom* ecosystem

Upstream data sources

Docking tools:

DiffDock: Primary source of docked poses for MM-GBSA rescoring
Automatic chaining: ask for structure → docking → rescoring in one request

Structure sources:

ESMFold: Predicted protein structures for novel targets
PDB: Experimental crystal structures
P2Rank: Binding site predictions to guide docking before MM-GBSA

Downstream applications

Decision support:

Compound Passport: Binding energy data feeds into comprehensive compound assessment
Comparison tables: Side-by-side binding energy comparison across compound series
Target Passport: Per-residue hotspot data informs target druggability assessment

Further analysis:

Molecular dynamics: Use MM-GBSA complexes as starting points for full MD simulations
Relative affinity ranking: fep_estimate ranks a congeneric series by heuristic ΔΔG derived from descriptor deltas. It takes SMILES rather than poses, and is not an alchemical free-energy calculation — the platform does not run one.

Best paired with

DiffDock: Generate docked poses, then rescore with MM-GBSA for physics-based ranking
P2Rank: Identify binding sites, dock to predicted pockets, then rescore with MM-GBSA
ESMFold: Predict target structure when no experimental structure is available
ADMET prediction: Combine binding affinity with ADMET profiling for compound triage

Protein: Standard amino acid residues; non-standard residues may cause parameterization failures
Ligand: Small organic molecules with common element types (C, N, O, S, P, F, Cl, Br, I)
Complex: Protein and ligand must be in close proximity (docked pose)
Format: PDB for complex or separate PDB + SDF files

Validation

Force field coverage: Ligands with unusual functional groups may fail parameterization
Protonation: Histidine and cysteine protonation states should be appropriate for the binding site pH
Missing atoms: Incomplete residues or missing heavy atoms cause errors

Accuracy considerations

Absolute vs. relative energies

This implementation emits no binding free energy. delta_g_bind is null on every run; the job returns single-point energies of the minimized complex. See reading the result against the bench
Absolute DeltaG: where MM-GBSA does produce one, published unsigned mean errors against measured affinity ran from 4.4 to 21.3 kcal/mol across six protein systems. Never convert it to a Kd
Relative ranking: the method is most reliable for ranking a congeneric series against the same target, where systematic errors partly cancel
Correlation: there is no single figure. Published Pearson r against measured affinity spans roughly 0 to 0.9 by target — and was negative on two of six systems with the GB model this tool defaults to. Always read a correlation with its n and its target class; figures and citations are on the translatability page

Known limitations

Entropy neglect: Standard MM-GBSA omits configurational entropy, which can affect ranking accuracy
Implicit solvent approximation: GB models may poorly capture water-mediated interactions or tightly bound water molecules
Single conformation: Without MD sampling, results depend heavily on the input pose quality
Metal coordination: Metalloprotein binding sites may not be well described by standard force fields

Performance characteristics

Speed and scaling

Typical runtime: 1-5 minutes per pose on GPU
Multi-pose jobs: Linear scaling with number of poses
GPU requirement: OpenMM acceleration requires GPU; CPU fallback is significantly slower

Deterministic behavior

Reproducible: Same input complex and parameters produce identical results
Force field sensitivity: Different force field choices can significantly change absolute energies but generally preserve relative rankings

Chat usage

Example prompts

"Rescore my DiffDock poses with MM-GBSA"
"Calculate binding energy for the docked complex on the canvas"
"Run MM-GBSA on the top 5 DiffDock poses using AMBER14 and OBC2"
"Compare binding energies for my three docked ligands"
"Show per-residue energy decomposition for the best DiffDock pose"

Workflow chaining

Feeds into: Compound Passport (binding energy data), comparison tables, downstream analysis
Accepts from: DiffDock (docking poses), other docking tools with PDB output

MM-GBSA