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 ΔG_solvation or ΔG_hydration 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

Accuracy Expectations

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

Software Stack

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