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Modern Python framework for molecular simulation and analysis

Project description

 molpy

A programmable toolkit for molecular simulation workflows

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Documentation  ·  Quick start  ·  Examples  ·  Ecosystem

MolPy is a Python toolkit for the full molecular-system workflow — parsing, building, editing, typing, analyzing, packing, and reading/writing simulation formats.

Under active development. Public APIs may change between minor releases.

Vision

Molecular modeling is fragmented. Every simulation code has its own file formats and conventions; every task — parsing, building, typing, analysis, visualization — lives in a separate library; and moving a system between them means writing throwaway glue.

molpy aims to be the common foundation beneath that workflow: one explicit, programmable representation of a molecular system that every stage can share. Parse a structure into it, build on it, type and analyze it — then hand the same object onward, with no conversion step in between.

That representation is meant to be built on, not just used. It is the data model the MolCrafts ecosystem extends — visualization, experiment management, agent access — and it reads the same whether a human writes it or an agent calls it.

Capabilities

Each row is one src/molpy/ module — parse or build a structure, edit and type it, analyze or minimize it, then read and write it across formats.

Module Capability
core Explicit data model — editable Atomistic topology graph (atoms, bonds, angles, dihedrals), Frame/Block columnar arrays, ForceField, Box
parser Grammar-based parsing — SMILES, SMARTS, BigSMILES, G-BigSMILES, CGSmiles
builder System assembly — linear / branched / cyclic polymers, polydispersity sampling (Schulz-Zimm, Poisson, Flory-Schulz), residue management
embed 3D coordinate generation for parsed or built topologies
op · reacter Structure editing — geometric transforms; template-based reactions with leaving-group selectors and LAMMPS fix bond/react templates
typifier Atom typing — OPLS-AA, GAFF / GAFF2, custom SMARTS / SMIRKS typifiers
potential · optimize Energy & force potentials with L-BFGS minimization
compute Analysis — RDF, MSD, clustering, shape & gyration, dielectric, neighbor lists, custom operators
pack Packmol-based packing with density targets
io Read and write — PDB, GRO, LAMMPS data, XYZ, JSON, HDF5, force fields, and trajectories
engine MD input generation & run management — LAMMPS, CP2K
wrapper · adapter External CLIs (Antechamber, Prepgen) and library bridges (RDKit, OpenBabel)

Install

pip install molcrafts-molpy

Core dependencies: NumPy, python-igraph, Lark, Pint, and molrs (the Rust numerical core). Optional: RDKit (3D geometry), AmberTools (GAFF charges).

Nightly builds. Bleeding-edge snapshots are published to the separate project molcrafts-molpy-nightly (versioned X.Y.Z.devN) on every push to the nightly branch. Install with pip install --pre molcrafts-molpy-nightly. It imports as molpy, so it cannot be installed alongside the stable molcrafts-molpy (same as tensorflow vs tf-nightly).

Install from source (development)
git clone https://github.com/MolCrafts/molpy.git
cd molpy
pip install -e ".[dev]"
pre-commit install
pytest tests/ -m "not external"

pip install -e ".[dev]" pulls the published molcrafts-molrs wheel from PyPI. To develop molpy against a local molrs checkout (e.g. when changing the Rust core), build molrs editable first — molrs ships its Python bindings as a maturin project that needs the Rust toolchain via rustup:

git clone https://github.com/MolCrafts/molrs.git
cd molrs
pip install maturin
maturin develop -m molrs-python/Cargo.toml --release   # installs `molrs` editable
cd ../molpy
pip install -e ".[dev]"                                # resolves molrs from the local build

See docs/developer/development-setup for the full workflow.

Quick start

Parse a SMILES string, assign OPLS-AA types, and write LAMMPS input files:

import molpy as mp

mol   = mp.parser.parse_molecule("CCO")          # ethanol from SMILES
ff    = mp.io.read_xml_forcefield("oplsaa.xml")  # bundled OPLS-AA
typed = mp.typifier.OplsAtomisticTypifier(ff).typify(mol)

mp.io.write_lammps_system("output/", typed.to_frame(), ff)
# → output/system.data  output/system.in

More workflows — polymer chains, polydisperse melts, AmberTools parameterization — are in the Example Gallery and the task-oriented Guides.

Documentation

Full documentation, including executable notebooks: molcrafts.github.io/molpy

MolCrafts ecosystem

Project Role
molpy Python toolkit — the shared molecular data model & workflow layer — this repo
molrs Rust core — molecular data structures & compute kernels (native + WASM)
molpack Packmol-grade molecular packing (Rust + Python)
molvis WebGL molecular visualization & editing
molexp Workflow & experiment-management platform
molnex Molecular machine-learning framework
molq Unified job queue — local / SLURM / PBS / LSF
molcfg Layered configuration library
mollog Structured logging, stdlib-compatible
molhub Molecular dataset hub
molmcp MCP server for the ecosystem
molrec Atomistic record specification

Contributing

Issues and pull requests are welcome — see Contributing.

License

BSD-3-Clause — see LICENSE.


Crafted with 💚 by MolCrafts

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