molcrafts-molpy 0.6.0

Modern Python framework for molecular simulation and analysis

 molpy

A programmable toolkit for molecular simulation workflows

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 — packed solvent boxes, virtual-site models, polymer chains and networks (the stress test for MolPy's editing machinery), AmberTools parameterization — are in the Example Gallery and the task-oriented Guides.

Documentation

Full documentation, including executable notebooks: molpy.molcrafts.org

• Getting Started — install and first example
• Example Gallery — short copy-paste workflows
• Guides — task-oriented notebooks
• Concepts — data model deep dives
• API Reference — full API

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.

MolPy ports or adapts code, algorithms, and data from other projects (freud, foyer, OpenMM, moltemplate, RDKit, and others). All third-party attributions and their license terms are consolidated in Third-Party Attributions.

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_{Crafted with 💚 by MolCrafts}