Free Energy

Free Energy Calculations

Compute ΔG of binding, solvation, or mutation via alchemical transformations — coupling/decoupling atoms along a λ pathway. Gold standard for lead optimization in drug discovery: accuracy ~1 kcal/mol for congeneric series.

When to Use This Skill

• Predict ΔΔG_bind between two ligands (RBFE / lead optimization)
• Compute absolute ΔG_bind of a ligand to a protein (ABFE)
• Calculate ΔGsolvation or ΔGhydration for ADME
• Rank compounds from a small congeneric series (~5-50 molecules)
• Validate force field parameters against experimental affinities
• Analyze convergence of FEP simulations (MBAR, overlap matrix)

Key Methods

| Method | Estimator | Windows | Notes | |--------|-----------|---------|-------| | FEP (Zwanzig) | Exponential avg | Any | High variance; avoid for large ΔG | | TI | Numerical integration of ⟨∂H/∂λ⟩ | 10-20 | Requires smooth integrand | | BAR | Bennett Acceptance Ratio | Adjacent pairs | Better than TI for same data | | MBAR | Multistate BAR | All pairs | Best variance; recommended | | RBFE | Relative: A→B via alchemical | 12-24 λ | Lead optimization | | ABFE | Absolute: ligand → unbound | ~20 λ | More expensive, independent |

Accuracy Expectations

| System | Typical error | Sim. time per edge | |--------|--------------|-------------------| | Congeneric RBFE (neutral) | 0.5-1.5 kcal/mol | 5-10 ns/window | | RBFE with charge change | 1-3 kcal/mol | 10-20 ns/window | | ABFE | 1-3 kcal/mol | 20-50 ns/window | | Solvation ΔG | 0.3-1.0 kcal/mol | 2-5 ns/window |

Quick Start

# pymbar: MBAR from energy matrix (u_kln)
import numpy as np
from pymbar import MBAR

# u_kln[k, l, n] = u_l(x_n^k) / kBT
# k: state from which sample was drawn
# l: state at which energy is evaluated
# n: sample index

K = 12  # number of lambda windows
N_k = np.array([1000] * K)  # samples per window

# u_kln shape: (K, K, max(N_k))
mbar = MBAR(u_kln, N_k)
results = mbar.compute_free_energy_differences()

dG = results['Delta_f'][0, -1]           # ΔG (kBT units)
ddG = results['dDelta_f'][0, -1]         # uncertainty
kBT = 0.5961  # kcal/mol at 298 K

print(f"ΔG = {dG * kBT:.2f} ± {ddG * kBT:.2f} kcal/mol")

Router — What to Read

| Task | Reference | |------|-----------| | FEP/TI/BAR/MBAR theory, thermodynamic cycles, alchemical path | references/fep-theory.md | | OpenMMTools: AlchemicalFactory, ThermodynamicState, MCMC sampling | references/openmmtools-alchemical.md | | RBFE protocol: edge network, protein-ligand, results | references/rbfe-protocol.md | | ABFE: restraints, double-decoupling, standard state correction | references/abfe-protocol.md | | pymbar, overlap matrix, convergence, uncertainty, phase space | references/pymbar-analysis.md |

Software Stack

| Package | Install | Role | |---------|---------|------| | pymbar | pip install pymbar | MBAR/BAR/FEP estimators | | openmmtools | conda install -c conda-forge openmmtools | Alchemical factories, MCMC | | perses | conda install -c conda-forge perses | RBFE pipeline (OpenMM-native) | | openfe | pip install openfe | FE campaign management (Lomap + OpenMM) | | alchemtest | pip install alchemtest | Test datasets for FE code | | lomap2 | pip install lomap2 | Ligand network RBFE planning |

Related Skills

• force-fields — system parameterization; OpenFF Sage for ligands
• docking — starting poses for ABFE; initial ranking before FEP
• mdanalysis — trajectory analysis from FEP runs
• scientific-skills:rowan — cloud FEP without local HPC

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: Kdevos12
• Source: Kdevos12/ALKYL
• License: MIT

Install and usage instructions live in the source repository linked above.