bio-free-energy-calculations
Performs alchemical free-energy calculations including relative binding free energy (RBFE / FEP+) and absolute binding free energy (ABFE) via OpenFE, FEP+, GROMACS, AMBER pmemd, and OpenMM with explicit lambda window scheduling, soft-core potentials, REST2 enhanced sampling, MBAR/BAR analysis, and cycle closure validation. Compares ML alternatives (Boltz-2 affinity, DeepDock). Use when ranking analogs by binding affinity beyond docking accuracy, performing prospective lead optimization, or validating SAR predictions.
npx skills add BioTender-max/awesome-bio-agent-skills --skill free-energy-calculations --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Version Compatibility Reference examples tested with: OpenFE 1.7+, OpenMM 8.1+, GROMACS 2024+, AMBER pmemd 22+, alchemlyb 2.1+, pymbar 4.0+, RDKit 2024.09+. Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` to check signatures - CLI: `openfe --version`; `gmx --version`; `pmemd.cuda --version` If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # Free Energy Calculations Predict binding affinity differences (RBFE) or absolute binding affinities (ABFE) using alchemical free-energy methods. FEP+ (Schrödinger) is the commercial industry standard; OpenFE (Open Free Energy) is the open-source reference. Modern best practice achieves 1-2 kcal/mol RMSE vs experimental for well-set-up RBFE on rigid receptors. Boltz-2 affinity module (Wohlwend 2025) approaches FEP accuracy at 1000x speed on benchmarks, but FEP remains gold standard for production lead optimization. For docking input poses, see `chemoinformatics/virtual-screening`. For pose validation before FEP, see `chemoinformatics/pose-valid
- Version Compatibility
- FEP Method Taxonomy
- Decision Tree by Scenario
- Relative Binding Free Energy (RBFE) Setup
- Lambda Window Scheduling
- REST2 Enhanced Sampling
- MBAR/BAR Analysis
- Cycle Closure Analysis
- Absolute Binding Free Energy (ABFE)
- MM/PBSA, MM/GBSA Endpoint Methods
- Force Field Selection
- Per-Tool Failure Modes
- Insufficient sampling
- Force-field artifacts
openfe absolute-free-energy run \ MM/GBSA via AMBER MMPBSA.py MMPBSA.py -i input.in -cp complex.parm7 -rp receptor.parm7 \
What does the bio-free-energy-calculations skill do?
Performs alchemical free-energy calculations including relative binding free energy (RBFE / FEP+) and absolute binding free energy (ABFE) via OpenFE, FEP+, GROMACS, AMBER pmemd, and OpenMM with explicit lambda window scheduling, soft-core potentials, REST2 enhanced sampling, MBAR/BAR analysis, and cycle closure validation. Compares ML alternatives (Boltz-2 affinity, DeepDock). Use when ranking analogs by binding affinity beyond docking accuracy, performing prospective lead optimization, or validating SAR predictions.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill free-energy-calculations --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.