Free energy

Claude Plugin for CompChem , Drug Discovery & Organic Chemistry reasoning

Install

npx -y skills add Kdevos12/ALKYL --skill free-energy

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Use when computing free energy differences for drug discovery. Covers FEP/TI/BAR/MBAR theory, alchemical transformations with OpenMMTools, relative binding free energy (RBFE) protocols, absolute binding free energy (ABFE), pymbar analysis, convergence diagnostics, and standard state corrections.

SKILL.md

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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

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