DiTMC is a modular framework for molecular conformer generation using Diffusion Transformers (DiTs). It integrates molecular graph information with 3D geometry through flexible graph-based conditioning and supports both non-equivariant and SO(3)-equivariant attention. DiTMC achieves state-of-the-art results on the GEOM benchmarks.

Our modular repository consists of 3 main entry points:

• dit_mc/app.py: the main entry point for training.
• dit_mc/generate_confs.py: script for sampling conformers from pre-trained checkpoints.
• dit_mc/evaluate.py: script for evaluating conformers against reference data.

Our repository is further organized in the following way:

• dit_mc/backbones: our architectural blocks that can be used to assemble the DiTMC models.
• dit_mc/data_loader: a custom data loader for tfrecords, that supports custom batching using jraph tuples and multiprocessing.
• dit_mc/generative_process: our modular package structure contains an abstraction layer different generative algorithms, we support flow matching with constant noise for now.
• dit_mc/model_zoo: our assembled DitMC architectures.
• dit_mc/training: main components for training and validation, as well as EMA calculation and checkpointing.
• tf_datasets: tensorflow dataset builder to run preprocessing and store GEOM QM9, Drugs and XL datasets as tfrecords.

For running the code, we recommend to download the code, data and checkpoints directly from our anonymized zenodo link. This ensures that the data is located in ./data and checkpoints are located in ./checkpoints folders relative to the dit_mc repository.

You can install our package directly via pip from source.

pip install .

Our code expects preprocessed data in tfrecord format for the GEOM datasets. For the final version of the paper, we will provide preprocessed datasets for convenience in tfrecord format. For now, we provide tfrecords for testing via our anonymized zenodo link. To run preprocessing yourself do the following:

Step 1 - Download and extract the GEOM dataset from the original source.

wget https://dataverse.harvard.edu/api/access/datafile/4327252 -O ./data/geom/rdkit_folder.tar.gz
tar -xvf ./data/geom/rdkit_folder.tar.gz

Step 2 – Download the data splits from GeoMol and move them to the respective subfolders within the ./data/geom directory.

https://github.com/PattanaikL/GeoMol/blob/main/data/QM9/splits/split0.npy
https://github.com/PattanaikL/GeoMol/blob/main/data/DRUGS/splits/split0.npy

Step 3 - Install requirements. Besides the requirements above, we need additional requirements for generating the data:

pip install mlcroissant apache-beam

Step 4 - Run the provided preprocessing script.

cd ./tf_datasets/geom
bash build_ds.sh

Step 5 - Copy the generated tfrecord files to ./data.

We perform evaluation in a two-step procedure.

First, we generate conformers and save them as $workdir/pred_mols.pkl. Second, we evaluate the saved predictions and save the results (metrics). You should copy the provided tfrecord files (under data/geom..) for testing to ./data/geom.. in the folder of the repository for this to work. You can specify one of the pre-trained checkpoints we provide for inference, e.g. workdir={your-download-folder}/checkpoints/drugs/apeB.

Example usage:

python generate_confs.py --workdir $workdir
python evaluate.py --workdir $workdir

During training, we use multiprocessing for distributed data loading using tensorflow datasets. You can adjust the number of CPUs in hydra as globals.n_cpus. The dataloader will start n_cpus parallel processes.

We are ready to start runs now.

• You need to adapt the wandb entity in config/config.yaml. If you don't have a wandb account, you should create one.
• You can edit all settings in configs/config.yaml and start a run like this

python dit_mc/app.py --multirun data_loader.data_cfg.data_dir=/tmp/data

Our test and evaluation scripts are in part based on the following open source contributions:

• GeoMol
• MCF
• ETFlow

If you use the code please cite the corresponding paper

@article{frank2025sampling,
  title={Sampling 3D Molecular Conformers with Diffusion Transformers},
  author={Frank, J Thorben and Ripken, Winfried and Lied, Gregor and M{\"u}ller, Klaus-Robert and Unke, Oliver T and Chmiela, Stefan},
  journal={arXiv preprint arXiv:2506.15378},
  year={2025}
}