A unified generative AI framework for 3D molecular generation using diffusion models, designed to streamline the entire workflow from model training to deployment in data-driven computational chemistry pipelines.
MolCraftDiffusion enables researchers to train 3D molecular diffusion models, develop predictive models, and perform guided molecular generation for applications such as catalyst discovery, drug design, and exploration of chemical space.
MolCraftDiffusion provides a complete pipeline for training/fine-tuning diffusion models, building predictive property models, and applying them to data-driven molecular generation tasks within a unified framework.
- End-to-End 3D Molecular Generation Workflow: Support training diffusion model, and preditive models, and utilize them for various molecular generation tasks, all within a unified framework.
- Curriculum learning: Efficient way for training and fine-tuning 3D molecular diffusion models
- Guidance Tools: MolCraftDiffusion includes several guidance mechanisms that enable the generation of molecules with desired structural or physicochemical properties.
- Property-Targeted Generation: Generate molecules with a target physicochemical or electronic properties (e.g., excitation energy, dipole moment)
- Inpainting: Systematically explore structural variants around reference molecules
- Outpainting: Extend a molecule by generating new parts.
- Command-Line Interface: A simple and flexible CLI interface enables users to perform training, generation, prediction, and analysis tasks directly from the command line.
Try our interactive demo for molecular generation: MolCraftDiffusion-demo
# 1. Create environment
conda create -n molcraft python=3.11 -y
conda activate molcraft
# 2. Install conda-only tools (xtb, openbabel)
conda install -c conda-forge xtb==6.7.1 openbabel -y
# 3. Install MolCraftDiffusion with PyTorch + PyG + sparse extensions
pip install molcraftdiffusion[gpu] \
--find-links https://data.pyg.org/whl/torch-2.6.0+cu124.html
# or CPU-only:
pip install molcraftdiffusion[cpu] \
--extra-index-url https://download.pytorch.org/whl/cpu \
--find-links https://data.pyg.org/whl/torch-2.6.0+cpu.html
# Optional: symmetry metrics (requires numpy==1.24.*)
pip install cosymlib
git clone https://github.com/pregHosh/MolCraftDiffusion
cd MolCraftDiffusion
pip install -e .[gpu] \
--find-links https://data.pyg.org/whl/torch-2.6.0+cu124.html
Pre-trained diffusion models are available at Hugging Face or in the models/edm_pretrained/ directory. We suggest to start from this model for downstream application.
There are two ways to run experiments: using the MolCraftDiff command-line tool (recommended) or by executing the Python scripts directly.
Make sure you have installed the package in editable mode as described above, and that you run the commands from the root of the project directory.
Commands:
train: Run a training job.generate: Run a molecule generation job.predict: Run prediction with a trained model.eval_predict: Evaluate predictions.analyze: Perform analysis and post-processing on generated molecules.data: Data processing utilities (preparation, augmentation, and dataset operations).
Command Syntax:
MolCraftDiff [COMMAND] [CONFIG_NAME/ARGUMENTS]
[COMMAND]: One oftrain,generate,predict,eval_predict,analyze, ordata.[CONFIG_NAME]: The name of the configuration file from theconfigs/directory (e.g.,train,example_diffusion_config).[ARGUMENTS]: Additional command-line arguments to override configuration settings.
Examples:
# Train a model using the 'example_diffusion_config.yaml' configuration
MolCraftDiff train example_diffusion_config
# Generate molecules using the 'my_generation_config.yaml' configuration
MolCraftDiff generate my_generation_config
# Predict properties using a trained model
MolCraftDiff predict my_prediction_config
# Compile molecular data into an ASE database
MolCraftDiff data prepare compile -s data_dir/ -d dataset.db
Getting Help:
To see the main help message and a list of all commands:
MolCraftDiff --help
To get help for a specific command:
MolCraftDiff train --help
You can also execute the scripts in the scripts/ directory directly.
Training:
python scripts/train.py tasks=[TASK]
where TASK is one of the following: diffusion, guidance, regression.
Generation:
python scripts/generate.py interference=[INTERFERENCE]
where INTERFERENCE is one of the following: gen_cfg, gen_cfggg, gen_conditional, gen.
Prediction:
python scripts/predict.py
The analyze command provides a suite of tools for processing and evaluating generated molecules.
Subcommands:
optimize: Optimize molecular geometries using GFN-xTB.metrics: Compute validity and connectivity metrics.compare: Calculate RMSD, energy differences, and geometric properties (bonds/angles) between generated and reference structures.xyz2mol: Convert XYZ files to SMILES and extract fingerprints/scaffolds.
Examples:
# Optimize geometries in a directory
MolCraftDiff analyze optimize -i generated_molecules/
# Compute validity metrics
MolCraftDiff analyze metrics -i generated_molecules/
# Compare generated structures with ground truth (requires optimized counterparts)
MolCraftDiff analyze compare generated_molecules/ --bonds
# Convert XYZ to SMILES
MolCraftDiff analyze xyz2mol -x generated_molecules/
Generated 3D molecules and their properties can be visualized using the 3DMolViewer package.
We also recommend our in-house and lightweight X11 molecular viewer V package.
Tutorials are now hosted in the docs site: https://preghosh.github.io/MolCraftDiffusion/
The local tutorials/ directory is deprecated and will be removed in a future release.
├── .project-root
├── justfile
├── pyproject.toml
├── README.md
├── setup.py
└── src
└── MolecularDiffusion
├── __init__.py
├── _version.py
├── molcraftdiff.py
├── callbacks
│ ├── __init__.py
│ └── train_helper.py
├── cli
│ ├── __init__.py
│ ├── analyze.py
│ ├── eval_predict.py
│ ├── generate.py
│ ├── main.py
│ ├── predict.py
│ └── train.py
├── configs
│ ├── data
│ ├── hydra
│ ├── interference
│ ├── logger
│ ├── tasks
│ └── trainer
├── core
│ ├── __init__.py
│ ├── core.py
│ ├── engine.py
│ ├── logger.py
│ └── meter.py
├── data
│ ├── __init__.py
│ ├── dataloader.py
│ ├── dataset.py
│ └── component
├── modules
│ ├── __init__.py
│ ├── layers
│ ├── models
│ └── tasks
├── runmodes
│ ├── __init__.py
│ ├── analyze
│ │ ├── __init__.py
│ │ ├── compute_energy_rmsd.py
│ │ ├── compute_metrics.py
│ │ ├── compute_pair_geometry.py
│ │ ├── xtb_optimization.py
│ │ └── xyz2mol.py
│ ├── generate
│ └── train
└── utils
├── __init__.py
├── comm.py
├── diffusion_utils.py
├── file.py
├── geom_analyzer.py
├── geom_constant.py
├── geom_constraint.py
├── geom_metrics.py
├── geom_utils.py
├── io.py
├── molgraph_utils.py
├── plot_function.py
├── pretty.py
├── sascore.py
├── smilify.py
└── torch.py
This project is licensed under the MIT License.
If you use MolecularDiffusion in your research, please cite the following:
ChemRxiv: MolecularDiffusion: A Unified Generative-AI Framework for 3D Molecular Design