alchemlyb
the simple alchemistry library
Decision gist · record as of 2026-08-14
Yes. alchemlyb is actively maintained, has no known vulnerabilities, carries a permissive BSD-3-Clause license, and requires only standard PyData stack dependencies (low install friction). It is purpose-built for a specific, well-defined task—free energy analysis from MD simulations—and does it with established statistical methods. Install it if you work with alchemical free energy calculations; skip it if you do not.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.11 or later.
- Input files must come from supported MD engines (GROMACS, AMBER, NAMD, etc.); parser availability depends on the engine used.
- Low friction: pure Python wheel with no compiled dependencies.
License · maintenance · safety
BSD-3-Clause (permissive) — BSD-3-Clause is permissive; you can use, modify, and distribute alchemlyb freely in commercial and private projects provided you include the license notice.
last release 2025-10-22 (296 days) · last repo commit 2026-06-01 · 243 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 392,931 downloads/mo, #7,002 on PyPI
Alternatives
Verify before relying
pip install alchemlyb
import alchemlyb
from alchemlyb.parsing import gromacs
# Parse GROMACS free energy output
data = gromacs.extract_u_nk('md.xvg')
from alchemlyb.estimators import MBAR
estimator = MBAR()
estimator.fit(data)
print(estimator.delta_f_) # Free energy differences- Whether parsers support all simulation engines mentioned (GROMACS, AMBER, NAMD, others) equally well or if some are more mature than others.
- Performance characteristics when handling large timeseries datasets or high-dimensional free energy landscapes.
- Whether the package includes example workflows or tutorials for common use cases beyond the basic API.
What it is and what it does
alchemlyb is a Python library for analyzing alchemical free energy calculations from molecular dynamics simulations. It sits atop the PyData stack (numpy, pandas, scipy, scikit-learn, matplotlib) and provides three main layers: parsers that extract raw data from output files of common MD engines (GROMACS, AMBER, NAMD, and others), subsamplers that extract uncorrelated and equilibrated samples from timeseries data using techniques like those in pymbar, and estimators that compute free energies directly using best-practices methods including multistate Bennett acceptance ratio (MBAR), BAR, and thermodynamic integration (TI).
The package is designed for computational chemists and molecular dynamics practitioners who need to post-process simulation output and extract thermodynamic quantities. It abstracts away the boilerplate of parsing heterogeneous file formats and handling statistical correlation in timeseries, letting users focus on the analysis and interpretation of results. The library is actively maintained, supports modern Python versions (3.11–3.14), and has no compiled dependencies.
Use it for
- Extract and analyze free energy differences from GROMACS, AMBER, or NAMD simulations using MBAR or BAR estimators.
- Detect equilibration in molecular dynamics trajectories and subsample to obtain independent, uncorrelated configurations for downstream analysis.
- Compute free energies via thermodynamic integration from alchemical simulation data.
- Parse and standardize output from multiple MD engines into a common pandas-based data structure for comparative analysis.
- Automate post-processing of alchemical free energy perturbation (FEP) calculations in high-throughput workflows.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
alchemlyb is actively maintained, has no known vulnerabilities, carries a permissive BSD-3-Clause license, and requires only standard PyData stack dependencies (low install friction). It is purpose-built for a specific, well-defined task—free energy analysis from MD simulations—and does it with established statistical methods. Install it if you work with alchemical free energy calculations; skip it if you do not.
Install
alchemlyb on PyPI
Before you install
Low friction: pure Python wheel with no compiled dependencies. Active maintenance (last commit 2026-06-01, 296 days since release). Requires Python 3.11 or later and 9 runtime dependencies from the PyData stack (numpy, pandas, scipy, scikit-learn, matplotlib, pymbar, loguru, pyarrow, joblib).
Requires Python 3.11 or later. Input files must come from supported MD engines (GROMACS, AMBER, NAMD, etc.); parser availability depends on the engine used.
License in practice
BSD-3-Clause is permissive; you can use, modify, and distribute alchemlyb freely in commercial and private projects provided you include the license notice.
Quickstart
pip install alchemlyb
import alchemlyb
from alchemlyb.parsing import gromacs
# Parse GROMACS free energy output
data = gromacs.extract_u_nk('md.xvg')
from alchemlyb.estimators import MBAR
estimator = MBAR()
estimator.fit(data)
print(estimator.delta_f_) # Free energy differencesVerify before relying
- Whether parsers support all simulation engines mentioned (GROMACS, AMBER, NAMD, others) equally well or if some are more mature than others.
- Performance characteristics when handling large timeseries datasets or high-dimensional free energy landscapes.
- Whether the package includes example workflows or tutorials for common use cases beyond the basic API.
Package facts
| License | BSD-3-Clause permissive |
| Python support | Supports the current Python release >=3.11 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 9 packagesnumpypandaspymbarscipyscikit-learnmatplotliblogurupyarrowjoblib |
| Maintenance | Actively maintained 296 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 392,931 / month, #7,002 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: Science/ResearchOperating System :: MacOS :: MacOS XOperating System :: Microsoft :: WindowsOperating System :: POSIXProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/EngineeringTopic :: Scientific/Engineering :: Bio-InformaticsTopic :: Scientific/Engineering :: ChemistryTopic :: Software Development :: Libraries :: Python Modules |
Evidence: alchemlyb-2.5.0-py3-none-any.whl
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