---
title: MM-GBSA quickstart
description: Step-by-step guide to rescoring docked complexes with MM-GBSA on the platform.
tags:
  - mmgbsa
  - quickstart
---

## Quick binding energy calculation with .bloom* platform

MM-GBSA combines molecular mechanics force fields with implicit solvation to rescore a docked complex. This guide walks through scoring your first complex, from importing a DiffDock result to interpreting the energies.

⚠︎ **This implementation returns no binding free energy.** It performs a single-point OpenMM evaluation of the minimized complex, so `delta_g_bind` is `null`, and there is no binding classification, no estimated Kd and no per-residue decomposition. Read [how MM-GBSA results compare to the bench](/docs/mmgbsa/translatability) before acting on a result.

## Step-by-step rescoring workflow

### 1. Access the MM-GBSA tool

Navigate to **MM-GBSA** in the .bloom* tool selector, or ask Ivy directly:

- "Rescore my DiffDock poses with MM-GBSA"
- "Calculate the binding energy of this complex"

### 2. Prepare your input complex

**Option A: From DiffDock results (recommended)**
- Select a completed DiffDock job from the canvas or job panel
- .bloom* automatically extracts the protein structure and docked ligand poses
- Choose which poses to rescore: top 1 (fastest), top 5, or all poses

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

**Option C: From previous jobs**
- Select a docked complex from any previous docking job
- Chain from a docking run (for example DiffDock) or any other step that produces protein-ligand complexes

### 3. Configure the solvent model

The force field is fixed at AMBER14 — there's no selector for it. What you can change:

**Basic settings (recommended for first run):**
- **Solvent model**: `gb_obc2` (default, best accuracy-speed balance)
- Keep all other parameters at defaults

**Advanced settings (via gear icon):**
- **Solvent model**: `gb_obc2`, `gb_obc1`, or `gb_hct`
- **Ionic strength**: 0–1.0 mol/L (default: 0.15)
- **Minimize complex**: Run energy minimization before evaluation (default: enabled)
- **Per-residue decomposition**: Enable for binding hotspot identification (default: enabled)

### 4. Submit and monitor the job

- Click **Calculate Binding Energy** to start the job
- .bloom* shows real-time progress through parameterization, minimization, and energy evaluation phases
- Typical completion: 1-5 minutes per pose on GPU
- Multiple poses are scored sequentially

### 5. Review and analyze results

**Energy summary:**
- **Complex potential energy** and **nonbonded energy** in kcal/mol, for the minimized complex
- **Generalized Born solvation term** in kcal/mol
- A **`caveat`** string stating what these are not. Read it
- **No DeltaG_bind, no binding classification and no estimated Kd** — none of the three is computed. See [the Kd question](/docs/mmgbsa/translatability#the-kd-conversion-is-not-defensible)

**Energy decomposition chart:**
- Van der Waals contribution (DeltaG_vdw)
- Electrostatic contribution (DeltaG_elec)
- Polar solvation penalty (DeltaG_polar)
- Non-polar solvation contribution (DeltaG_nonpolar)
- Stacked bar chart showing the relative magnitude of each term

**Per-residue hotspot analysis:**
- Table of residues ranked by their contribution to binding energy
- Negative values indicate binding-favorable residues (hotspots)
- Positive values indicate residues that destabilize the complex
- Residues highlighted on the 3D structure in the canvas viewer

**Download options:**
- **CSV**: Full energy decomposition and per-residue data
- **JSON**: Structured results with metadata
- **PDB**: Minimized complex structure

## Common rescoring scenarios

### Single pose evaluation
1. Import the top DiffDock pose
2. Run MM-GBSA with the `gb_obc2` solvent-model default
3. Check DeltaG_bind and binding classification
4. Review per-residue hotspots for mechanistic insight

### Multi-pose ranking
1. Import all DiffDock poses (typically 5-10)
2. Run MM-GBSA on all poses
3. Sort by DeltaG_bind to identify the best-scoring pose
4. Compare energy decomposition across poses to understand binding mode differences

### Compound series comparison
1. Dock a series of analogs with DiffDock
2. Run MM-GBSA on each compound's best pose
3. Rank compounds by DeltaG_bind
4. Use per-residue decomposition to understand SAR trends

### Binding hotspot identification
1. Score a complex with per-residue decomposition enabled
2. Identify the top 5-10 contributing residues
3. Map hotspot residues onto the 3D structure
4. Use insights to guide mutagenesis experiments or analog design

## Quality control checklist

### Before submission
- [ ] Docked complex has reasonable geometry (no steric clashes)
- [ ] Ligand is in a reasonable binding pose (not solvent-exposed)
- [ ] Protein has no missing residues in the binding site
- [ ] Protonation states are appropriate (especially His, Cys, Asp, Glu)

### After calculation
- [ ] Read the `caveat` string before quoting any number from the result
- [ ] Minimization converged (`convergence_achieved`)
- [ ] Energy magnitudes are not wildly large — that signals a parameterization artifact, not tight binding
- [ ] No parameterization warnings in the job log
- [ ] The upstream pose has been inspected. A physics-based energy on a wrong pose is a precise answer to the wrong question

## Next steps after rescoring

**Compound triage:**
- Feed MM-GBSA energies into Compound Passport for comprehensive assessment
- Compare energies with ADMET predictions to prioritize compounds
- Ranking a compound series is what the method is *for* — but only within one target, and only once it emits a real ΔG_bind, which this implementation does not. Confirm affinity with ITC, SPR or an enzymatic IC50/Ki; see [go / no-go](/docs/mmgbsa/translatability#go--no-go)

**Mechanistic analysis:**
- Use per-residue hotspots to design mutations that test binding hypotheses
- Analyze energy decomposition to understand whether binding is driven by shape complementarity (vdW) or electrostatics
- Compare on-target vs. off-target binding energies for selectivity assessment

**Further refinement:**
- Run molecular dynamics simulations starting from the MM-GBSA-minimized complex
- For relative ranking within a congeneric series, `fep_estimate` gives a heuristic ΔΔG from descriptor deltas. It is not an alchemical free-energy calculation, so treat it as triage rather than a more rigorous alternative to MM-GBSA.
- Use MM-GBSA hotspot data to guide scaffold decoration in GenMol

## Read next