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PyChem-Pro

A pure-Python desktop application and library for chemistry and cheminformatics — molecular visualization, SMILES parsing, PDB loading, MMFF94 geometry optimization, descriptors, and a plugin ecosystem. No RDKit. No OpenBabel. Everything implemented from scratch.

Repository: https://github.com/vijaymasand/PyChem-Pro

Website: https://sites.google.com/view/pydescriptorcstar/pychem-pro

Buy Me A Coffee

Financial support/Sponsorship 🎉 Another PyChem-Pro Milestone Update! I am incredibly proud to share that PyChem-Pro has received another official financial support from Dr. Meghshyam K. Patil, Dr. Babasaheb Ambedkar Marathwada Universiy Sub-campus Dharashiv, Dharashiv, Maharashtra.

Table of Contents

About PyChem-Pro

PyChem-Pro is a desktop chemistry application and Python library that combines molecular visualization (like PyMOL) with cheminformatics primitives (like RDKit). Unlike most tools in the space, PyChem-Pro is pure Python with NumPy. There is no C++ extension, no dependency on any cheminformatics library. Every feature — SMILES parsing, 3D coordinate generation, force field optimization, descriptor calculation, Shrake-Rupley SASA, ring perception, protein cartoon rendering — is implemented from scratch and readable end-to-end. It uses multiprocessing for various functions and operations.

This makes PyChem-Pro:

  • Educational. Graduate students can open any file and see how a real MMFF94 optimizer or a Catmull-Rom ribbon renderer actually works.
  • Portable. No compilers. No system libraries beyond Python and Qt. Runs on Windows, macOS, and Linux identically.
  • Extensible. A service-oriented architecture with Protocol interfaces lets researchers swap the force field, the renderer, or the file loader without touching the rest of the codebase.
  • Academic-friendly. Intended for inclusion in university cheminformatics curricula and as an open-source reference implementation.
  • Industry-ready. Multiprocessing and parallel processing abilities makes it highly suitable for large datasets and ready for industrial use.

Key Features

Chemistry

  • SMILES parser with Huckel aromaticity perception, stereochemistry, bracketed atoms, radicals

  • PDB / PDBx / mmCIF / PDBQT / MOL / MOL2 / SDF file readers with automatic bond detection and CONECT parsing

  • PDBQT support for small and large molecules

  • 3D coordinate generation from molecular topology (BFS + force-field relaxation)

  • MMFF94 force field with hydrogen addition, bond stretching, angle bending, torsion, Van der Waals, and BCI partial charge assignment

  • L-BFGS and steepest descent optimizers

  • Gasteiger partial charges, AM1 and PM3 semi-empirical charge models

  • Molecular descriptor calculator (constitutional, topological, electronic, geometric, hybrid, quantum, fingerprints)

  • Morgan (ECFP) and topological fingerprints

  • Center of Mass and Shrake-Rupley SASA

  • Structural alignment / superposition — Kabsch fit with iterative outlier rejection; atom correspondence by index, element, or protein Cα sequence (Needleman-Wunsch); RMSD reporting (pure NumPy)

  • QSAR Data Splitting — 7 partitioning algorithms (Random, Scaffold, CADEX, D-Optimal, Sphere Exclusion, Boruta, Duplex) with PCA visualization and KS-test evaluation.

  • Chemical Space Visualization — Featurization, mapping, t-SNE, PCA, and UMAP (very slow) projections for analyzing chemical space coverage of datasets.

    Chemical Space Visualization

Visualization

  • Hardware-aware software 3D rendering (QPainter with gradient sphere shading)
  • Ball-and-stick, space-fill, wireframe, cartoon, ribbon, and backbone modes
  • PyMOL-style protein cartoons with alpha-helix / beta-sheet / coil detection
  • Color-by secondary structure, rainbow, chain, B-factor
  • 2D chemical structure viewer with single/double/triple/aromatic bond rendering, wedge/hash stereo bonds, atom labels, formal charges
  • Natural-language atom selection (sele('organic'), sele('within 5.0 COM'), sele('chain A and helix'))
  • High-DPI image export (72 / 150 / 300 / 600 DPI)
  • Offline ray-tracer for publication-quality images
  • Print (Ctrl+P) — prints 2D and 3D views on a single page
  • Open multiple files/molecules simultaneously in same window

Architecture

  • Service-oriented with typing.Protocol interfaces
  • EventBus for decoupled pub/sub communication
  • ParallelExecutor at 50% CPU cores across file loading, force field, rendering, descriptors, ray-tracing, and conformer search
  • Public pychem Python package usable in Jupyter notebooks without PySide6

Plugin System

  • BasePlugin / PluginWidget API for custom analysis, visualization, I/O, and utility plugins
  • Built-in templates for analysis, visualization, and I/O plugin types
  • QSAR modeling, Ramachandran, descriptor pruning, docking pose visualization (featuring 3D distance-based bond order perception and robust aromatic Kekulization), molecular weight calculator — all shipped as plugins

Rendering Pipeline

Dependency rules (enforced by convention):

  1. Core domain imports nothing from services, features, app, or Qt.
  2. Infrastructure imports only from core domain.
  3. Services import from core domain + protocols. Never from other service implementations.
  4. Features import from services (via protocols) + core domain + Qt.
  5. App imports from features + services + core domain + Qt.
  6. Public API imports from services + core domain. Never from Qt.
  7. Plugins import from public API + Qt compat layer. Never from service internals.

Every subsystem implements a typing.Protocol interface (IForceField, IRenderer, ILoader, ICoordinateGenerator, IDescriptorCalculator) so the implementation can be swapped without touching consumers.

Tech Stack

Layer Technology
Language Python 3.10+ (3.13/3.14 supported)
GUI Framework PySide6 (Qt 6.5+)
Numerical NumPy 1.24+
Multiprocessing Python concurrent.futures.ProcessPoolExecutor with forced spawn context for cross-platform safety
Bundler (optional) Nuitka 2.0+ for producing standalone binaries
Plugin stack (optional) pandas, scipy, scikit-learn, matplotlib, packaging — only needed if you run plugins that require them

No chemistry dependencies. PyChem-Pro does not use any cheminformatics or external chemistry library.

Vendored

  • OASA (Open Structure Access) — a subset is vendored under src/vendors/oasa/ and used for SMILES/InChI utilities and 2D coordinate generation fallbacks. It is treated as frozen third-party code and not modified.

System Requirements

  • Operating System: Windows 10/11, macOS 12+, or any Linux distribution with Qt 6 support (Ubuntu 22.04+, Fedora 37+, Arch, etc.)
  • Python: 3.10 or newer
  • RAM: 4 GB minimum, 8 GB recommended for large proteins (>5000 atoms)
  • Display: OpenGL 3.3+ optional (used by the future hardware-accelerated renderer path). The default QPainter renderer works on any display.
  • CPU: Multi-core recommended. The ParallelExecutor uses 50% of available cores automatically.

Cross-Platform Notes

PyChem-Pro is designed to work identically on Windows, macOS, and Linux. Two design decisions enforce this:

  1. Multiprocessing: ProcessPoolExecutor is always created with the spawn start method (multiprocessing.get_context('spawn')). This avoids fork()-related crashes on macOS (Qt + CoreFoundation assertion) and matches the only option available on Windows.
  2. File paths: All path handling uses os.path or pathlib — no hard-coded separators.
  3. Fonts: The UI uses a best-available fallback sequence and will warn but not fail if a platform-specific font is missing.

Installation

1. Clone the repository

git clone https://github.com/vijaymasand/PyChem-Pro.git
cd PyChem-Pro

2. Create a virtual environment

# macOS / Linux
python3 -m venv venv
source venv/bin/activate

# Windows (cmd)
python -m venv venv
venv\Scripts\activate.bat

# Windows (PowerShell)
python -m venv venv
venv\Scripts\Activate.ps1

3. Install Dependencies

# Upgrade pip 
pip install --upgrade pip
# (If this fails on Windows, use: python -m pip install --upgrade pip)

# Install required packages
pip install -r requirements.txt

This installs the runtime dependencies:

  • PySide6 >= 6.5 (Qt GUI framework)
  • numpy >= 1.24, matplotlib >= 3.7, pandas >= 2.0, scipy >= 1.10
  • scikit-learn >= 1.3, rdkit >= 2023.3 (used by QSAR and descriptor plugins)
  • psutil, pillow

Build tools (Nuitka, dmgbuild) are in requirements-build.txt and only needed if you want to compile a standalone binary — not required for running from source.

4. Optional — install plugin dependencies

The built-in plugins in plugins/ have optional dependencies. Install them only if you plan to use those plugins:

pip install packaging pandas scipy scikit-learn matplotlib rdkit

Without these, the core application runs fine; the affected plugins are simply skipped at startup with a warning.

Running the Application

Recommended: One-Click Launchers

The easiest way to run PyChem-Pro is to double-click the launcher for your platform. No terminal, no Python knowledge, no setup steps required.

Platform File to double-click
macOS PyChem.command (in Finder)
Windows PyChem.bat, then use the PyChem - Launch.lnk shortcut created on first run

Both launchers handle everything automatically and follow the same steps:

Step What happens
1 Checks whether Python 3.10+ is already installed
2 If Python is missing — installs it automatically: macOS uses Homebrew (asks for your password once); Windows uses winget (built into Windows 10/11), falling back to a silent download from python.org
3 Creates a PyChem-Pro icon shortcut for easy access (first run only)
4 Creates an isolated Python virtual environment inside the project folder (venv/) so no system packages are touched (first run only)
5 Installs all required packages from requirements.txt into that venv (first run only)
6 Launches PyChem

First run takes 2–5 minutes while packages download and install. Every run after that starts in seconds — the venv is reused as-is.

The launchers are provided for your convenience and are the recommended way to get started. If you prefer to manage your Python environment yourself, or if a launcher does not work on your machine, use the manual setup below — it is straightforward and gives you full control.

Manual Setup (all platforms)

1. Install Python 3.10+

  • macOS: Download from python.org/downloads or run brew install python@3.12
  • Linux: sudo apt install python3 (Ubuntu/Debian) or sudo dnf install python3 (Fedora)
  • Windows: Download from python.org/downloads — check "Add Python to PATH" during install

Verify:

python3 --version   # should print 3.10 or newer

2. Clone the repository

git clone https://github.com/vijaymasand/PyChem-Pro.git
cd PyChem-Pro

3. Create a virtual environment

# macOS / Linux
python3 -m venv venv
source venv/bin/activate

# Windows (cmd)
python -m venv venv
venv\Scripts\activate.bat

4. Install dependencies

pip install --upgrade pip
pip install -r requirements.txt

5. Run PyChem-Pro

python main.py

The first launch generates a 10-year development license automatically. No network access required.

Expected startup output

Using PySide6 framework
Plugin system initialized successfully

Quick Start — Python API

PyChem-Pro provides a public package at the top level (pychem/) that does not require PySide6 to be imported. You can use it in Jupyter notebooks or scripts.

import pychem

# Parse SMILES
mol = pychem.parse_smiles("CCO")  # ethanol
print(mol.molecular_formula())    # C2H6O

# Generate 3D coordinates
pychem.generate_3d(mol)

# Full MMFF94 optimization (adds hydrogens, assigns BCI charges, minimizes)
result = pychem.optimize(mol, max_iters=500)
print(f"Converged: {result.converged}, E = {result.final_energy:.2f} kcal/mol")
print(f"Steps: {result.num_steps}")

# Partial charges are now assigned
for atom in mol.atoms:
    print(f"  {atom.symbol}{atom.index}: q = {atom.partial_charge:+.4f}")

# Molecular descriptors
desc = pychem.descriptors(mol)
print(desc)
# {'molecular_weight': 46.07, 'num_atoms': 9, 'num_bonds': 8,
#  'num_heavy_atoms': 3, 'formula': 'C2H6O', 'num_rings': 0, 'total_charge': 0}

# Batch descriptor calculation (uses ParallelExecutor)
mols = [pychem.parse_smiles(s) for s in ["CCO", "c1ccccc1", "CC(=O)O", "CCCCCC"]]
batch = pychem.descriptors_batch(mols)

# Load a protein from PDB
protein = pychem.load("1AKE.pdb")
print(f"{protein.num_atoms} atoms, {protein.num_bonds} bonds")

# Add explicit hydrogens only (no optimization)
h_count = pychem.add_hydrogens(mol)

# Compute MMFF94 BCI charges only
pychem.compute_charges(mol)

# Structural alignment / superposition (accepts Molecule objects or file paths)
ref  = pychem.load("ref.pdb")
pose = pychem.load("model2.pdb")
result = pychem.align(pose, ref)          # moves `pose` onto `ref` in place
print(f"RMSD = {result.rmsd:.3f} Å over {result.n_aligned} atoms ({result.method})")
# auto: two proteins align by Cα sequence; small molecules pair by atom index

# RMSD without moving anything (optionally best-fit first)
d = pychem.rmsd("poseA.mol2", "poseB.mol2", superpose=True)

Available API functions

pychem.parse_smiles(smiles: str) -> Molecule
pychem.load(path: str) -> Molecule
pychem.generate_3d(mol: Molecule, optimize: bool = True, max_steps: int = 200) -> None
pychem.optimize(mol: Molecule, max_iters: int = 500, method: str = 'lbfgs') -> OptimizationResult
pychem.add_hydrogens(mol: Molecule) -> int
pychem.compute_charges(mol: Molecule) -> None
pychem.descriptors(mol: Molecule, names: list[str] | None = None) -> dict
pychem.descriptors_batch(mols: list[Molecule], names: list[str] | None = None) -> list[dict]
pychem.split_dataset(df_or_path, algorithm: str, target_ratio: float, smiles_col: str, **kwargs) -> SplitResult

# Structural alignment (mobile/reference may be a Molecule or a file path)
pychem.align(mobile, reference, *, method='auto', selection='auto',
             cycles=5, cutoff=2.0, weights=None, transform=True) -> AlignmentResult
pychem.align_many(mobiles: list, reference, **kwargs) -> list[AlignmentResult]
pychem.rmsd(mol_a, mol_b, *, method='auto', selection='auto',
            superpose=False) -> float

Services Layer (Public Protocols)

All major subsystems are exposed through typing.Protocol interfaces so alternative implementations can be dropped in without touching consumers.

from src.core.protocols import (
    IForceField, IRenderer, ILoader,
    ICoordinateGenerator, IDescriptorCalculator,
    OptimizationResult, Camera, RenderMode,
)

Example: registering a custom force field

from src.core.protocols.forcefield import IForceField, OptimizationResult
from src.core.domain.models.molecule import Molecule

class MyForceField:
    """Custom force field implementing the IForceField protocol."""

    def add_hydrogens(self, mol: Molecule) -> int:
        ...

    def assign_atom_types(self, mol: Molecule) -> None:
        ...

    def assign_charges(self, mol: Molecule) -> None:
        ...

    def optimize_geometry(self, mol, max_iters=500, convergence=1e-4,
                          method='lbfgs') -> OptimizationResult:
        ...

    def compute_energy(self, mol: Molecule) -> float:
        ...

# Install into the registry at startup
from src.core.registry import ServiceRegistry
registry = ServiceRegistry()
registry.forcefield = MyForceField()

The class does not need to inherit from anything — typing.Protocol uses structural subtyping.

MMFF94 Force Field

PyChem-Pro ships a pure-Python implementation of the Merck Molecular Force Field (MMFF94). At present, it is not perfect. It is simplified relative to the full 75K+ rule set but covers standard organic chemistry:

Term Implementation
Bond stretching Hookean with MMFF94 r0 and kb parameters
Angle bending Cubic-corrected 0.021914 ka (theta - theta0)^2 [1 + cb (theta - theta0)] with analytical gradient
Torsion (dihedral) Fourier V1 cos(phi) + V2 cos(2 phi) + V3 cos(3 phi) with analytical gradient
Van der Waals Lennard-Jones 12-6 with 1-2 and 1-3 exclusions, parallelized for >200 pairs
Partial charges Bond Charge Increment (BCI) method
Hydrogen addition Hybridization-aware 3D placement (tetrahedral / trigonal / linear)

Optimization pipeline

  1. Assign hybridization (sp / sp2 / sp3)
  2. Add explicit hydrogens with ideal 3D positions
  3. Assign BCI partial charges
  4. Build interaction lists (bonds, angles, torsions, VdW pairs)
  5. Minimize energy via steepest descent or L-BFGS with Armijo line search

Verified analytical gradients against numerical finite differences within 1e-4 for angles and 1e-3 for torsions.

Files

src/services/forcefield/
├── mmff94_service.py       # Unified IForceField implementation
├── hydrogen.py             # HydrogenAdder with hybridization-aware placement
├── angle_bending.py        # AngleBendingCalculator
├── torsion.py              # TorsionCalculator
└── parameters.py           # Consolidated bond/angle/torsion/VdW/BCI tables

Multiprocessing

A single ParallelExecutor at src/core/parallel.py wraps concurrent.futures.ProcessPoolExecutor and is shared across all services. It uses 50% of available CPU cores by default.

Forced spawn start method

import multiprocessing as mp
_mp_context = mp.get_context('spawn')

Cross-platform rationale:

  • macOS: fork() + Qt causes CoreFoundation assertions. spawn avoids it.
  • Windows: fork() is not available. spawn is the only option.
  • Linux: fork() works, but spawn is safer with Qt and matches other platforms.

Where multiprocessing is used

Service What is parallelized
File loading PDB / PDBx / mmCIF / MOL2 atom record parsing split into N chunks
MMFF94 force field VdW pairwise computation split into N chunks (only when >200 pairs)
Coordinate generation N conformers with independent random seeds, return lowest energy
Descriptor calculation Batch molecules distributed across workers
3D rendering Projection, depth sort, culling split across workers (large molecules only)
Ray-tracing export 64x64 pixel tiles processed in parallel

All worker functions are module-level (required by the spawn start method for pickling).

Plugins

PyChem-Pro has a first-class plugin system. Plugins appear as dockable panels in the GUI and can subscribe to molecule-changed events.

Minimal plugin

from src.plugins.base_plugin import BasePlugin, PluginWidget
from src.plugins.plugin_types import PluginInfo, PluginType
from src.shared.qt_compat import QVBoxLayout, QPushButton, QLabel

class HelloPluginWidget(PluginWidget):
    def setup_ui(self):
        layout = QVBoxLayout(self.widget)
        self.label = QLabel("No molecule loaded")
        layout.addWidget(self.label)

    def on_molecule_changed(self, molecule):
        if molecule:
            self.label.setText(f"{molecule.num_atoms} atoms")

class HelloPlugin(BasePlugin):
    def get_info(self) -> PluginInfo:
        return PluginInfo(
            name="Hello Plugin",
            version="1.0.0",
            description="Shows atom count",
            author="Your Name",
            plugin_type=PluginType.ANALYSIS,
            dependencies=[],
        )

    def create_widget(self):
        return HelloPluginWidget(self)

    def initialize(self):
        return True

    def cleanup(self):
        pass

    def on_molecule_changed(self, molecule):
        if hasattr(self, 'widget') and self.widget:
            self.widget.on_molecule_changed(molecule)

Drop the file into plugins/ and restart PyChem, or use Plugins → Installed Plugins... to load it manually.

Built-in plugins

Plugin Purpose
mol_weight_calculator.py Molecular weight and element composition
ramachandran_plugin.py Ramachandran plot for protein backbones
qsar_modeler_plugin.py QSAR model building and validation
qsar_rfa_mars_plugin.py Random Forest + MARS QSAR
docking_pose_visualizer.py Docking pose comparison
DescriptorPruningApp.py Descriptor correlation pruning
batch_3d_converter_plugin.py Batch 3D coordinate generation and format conversion
pharmacophore_features.py Extract, analyze, and map pharmacophore features in 3D
qsar_curator_gui.py Chemical structure curation and data preparation for QSAR
example_analysis_plugin.py Template for analysis plugins
example_visualization_plugin.py Template for visualization plugins

Optional plugins declare their external dependencies in PluginInfo.dependencies. If a required package is missing, the plugin is skipped with a warning at startup.

Docking & Interaction Visualization Showcase

PyChem-Pro offers robust visual tools for mapping molecular interactions in both 2D and 3D space:

  • 2D Docking Pose Visualizer: Highlights hydrogen bonds, hydrophobic interactions, salt bridges, and contacts with distance-aware spacing and colored residue categories.

    2D Docking Pose Interaction Diagram

  • 3D Docking Pose Viewer: Displays spatial binding modes directly in the 3D molecular viewer.

    3D Docking Pose Viewer

Development Workflow

Coding standards

  • Style. Follow PEP 8, 4-space indentation, max 100 chars per line.
  • Typing. Use typing.Protocol for service interfaces, dataclasses for DTOs, and type hints everywhere reasonable.
  • No chemistry dependencies. NumPy is the only permitted numerical dependency.
  • Module-level functions for multiprocessing. Any function passed to ParallelExecutor.map must be defined at module scope (required by the spawn start method).
  • Cross-platform paths. Use os.path or pathlib. Never hard-code / or \.
  • Large files. Keep individual Python files under ~800 lines. If a file grows beyond that, split it into focused modules.

Commit conventions

  • Use imperative present-tense commit subjects: feat: add X, fix: correct Y, refactor: split Z, perf: cache W, docs: explain V.
  • First line 72 chars max. Body wrapped at 72 chars.
  • Do not add Co-Authored-By: Claude or any AI attribution.
  • Co-authorship for human collaborators is welcome via the standard Co-Authored-By: Name <email> trailer.

Running the application during development

source venv/bin/activate
python main.py

Startup profiling is enabled by default and prints timing information for Qt imports, MainWindow creation, and total startup time.

Building a standalone binary (optional)

python build.py

This uses Nuitka to produce a compiled executable in build/. Windows and macOS builds are tested; Linux builds work but are not packaged for distribution.

Contributing

Contributions are welcome — bug reports, feature requests, pull requests, new plugins, documentation improvements, and test coverage.

Before submitting a pull request

  1. Run the full unit test suite and make sure nothing regresses: 
    for t in tests/test_*.py; do python3 "$t"; done
  2. Launch the GUI and verify the affected workflow manually.
  3. Update relevant documentation in docs/ if you changed an interface.
  4. Follow the commit conventions above.

Reporting bugs

Please include:

  • Operating system and version
  • Python version (python --version)
  • PySide6 version (pip show PySide6)
  • Full traceback if there is an error
  • Minimal reproducer (SMILES string, PDB id, or attached file)

Performance Targets

Approximate targets measured on a 4-core Intel machine with 8 GB RAM. Actual numbers depend on the molecule.

Metric Target
MMFF94 ethanol optimization ~0.3 s (full force field with H addition)
MMFF94 insulin (51 residues) <30 s
3D render FPS (100 atoms) ~60 FPS
3D render FPS (5000 atoms) ~30 FPS
3D render FPS (10K atoms) ~60 FPS on GL path (future)
PDB load (10K atoms) ~1 s (parallel)
Descriptor calculation (100 mols) ~50% faster than sequential via ParallelExecutor
Startup time ~1.5 s

Roadmap

The architecture spec at docs/superpowers/specs/2026-04-11-architecture-redesign-design.md captures the long-term plan. Short-term items:

  • Hardware-accelerated OpenGL renderer with protein cartoon support

  • Stereochemistry enforcement in 3D coordinate generation (R/S, E/Z)

  • Ring template library for faster and more accurate cyclic structure generation

  • Conformer ensemble analysis (RMSD clustering, Boltzmann-weighted properties)

  • In-app 2D chemical structure editor / sketcher

    2D Structure Sketcher

  • Extended MMFF94 parameter tables (halogens, phosphorus, metals)

  • Docking interface (scoring function only, no compiled dependencies)

See docs/ for detailed specs and implementation plans.

License

PyChem-Pro is licensed under the Polyform Noncommercial License 1.0.0.

Copyright (c) 2026 Vijay Masand and Gaurav Masand

  • Personal and educational use — free to use, modify, and distribute.
  • Commercial use — strictly prohibited. This includes using PyChem-Pro as part of a paid service, distributing it for a fee, or relying on it to generate revenue.

See the LICENSE file for the full legal text.

Vendored OASA code retains its original BSD-style license.

Citation

If you use PyChem-Pro in academic work, please cite:

@software{pychem,
  title   = {PyChem-Pro: A Pure-Python Cheminformatics and Molecular Visualization Toolkit},
  author  = {Masand, Gaurav and Masand, Vijay},
  year    = {2026},
  url     = {https://github.com/vijaymasand/PyChem},
  note    = {Accessed: YYYY-MM-DD}
}

A proper DOI will be issued once the first tagged release is published.

Acknowledgments

  • The MMFF94 force field is the work of Thomas A. Halgren (Merck, 1996). Our implementation is a simplified pure-Python version of the published specification.
  • The OASA library (Open Structure Access, Beda Kosata) is vendored under src/vendors/oasa/ and provides SMILES/InChI utilities and 2D layout primitives.
  • PyMOL and Jmol inspired the protein cartoon representations.
  • PySide6 / Qt from the Qt Project provides the GUI framework.

Contact

Questions, feedback, and pull requests are welcome. Many more functions coming in version 2.0.

About

PyChem-Pro: 100% Python API, fast, flexible & programmable molecular viewer - 2D and 3D, molecular docking pose viewer, cheminformatics library, molecular descriptor calculator, molecular editor, chemical drawing editor, QSAR model developer, and many more...

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bioinformaticschemical-editorchemical-space-plotcheminformaticschemistry-drawingdrug-discoverymachine-learningmolecular-descriptorsmolecular-dockingmolecular-viewerqsar

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