FLOWR is a research repository that investigates continuous and discrete flow matching methods applied to structure-based drug discovery. It provides a complete workflow for training models, generating novel ligand molecules, and evaluating the generated structures.
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Create the Environment
Install the required environment using mamba:mamba env create -f environment.yml
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Activate the Environment
conda activate flowr
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Set PYTHONPATH
Ensure the repository directory is in your Python path:export PYTHONPATH="$PWD"
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Install ADFR Install the ADFR suite. Adjust the prepare_receptor_bin_path in genbench3d/configs/default.yaml to match the ADFR installation.
We provide the full SPINDR data in both .smol and .cif format, as well as a fully trained FLOWR model checkpoint and generated samples.
For training, generation and evaluation, we provide basic bash and SLURM scripts in the scripts/ directory. These scripts are intended to be modified and adjusted according to your computational resources and experimental needs.
Download the SPINDR dataset, the FLOWR checkpoint and generated samples here: Zenodo.
Download the genbench3d_data.tar and place it untarred in the repo (we will get rid of this dependency in the future).
To train a model, unzip the smol_data.zip to get the smol-files. Specify the directory they are placed in the respective scripts (see below). We also provide the cif-files for all protein pockets splitted into train, validation and test.
Generation of novel molecules either de novo or fragment-based can be done with the provided checkpoint (flowr.ckpt).
Start by training the model using the provided training script. This script sets hyperparameters such as batch size, learning rate, and network architecture.
Modify scripts/train_spindr.sh as needed, then run:
bash scripts/train_spindr.shAfter training (or with the provided flowr.ckpt), generate novel molecules using the provided generation script.
Modify scripts/gen_spindr.sl according to your requirements, then submit the job via SLURM:
sbatch scripts/gen_spindr.slAfter training (or with the provided flowr.ckpt), you can generate novel molecules given a PDB or CIF protein/pocket file.
If you provide a protein PDB/CIF file, you need to provide a ligand file as well to cut out the pocket (default: 6A cutoff - modify if needed). Currently, Multimers are not supported. Please modify (single-chain) or cut the protein in advance.
Note, if you want to run conditional generation, you need to provide a ligand file as reference.
Modify scripts/gen_pdb.sl according to your requirements, then submit the job via SLURM:
sbatch scripts/gen_pdb.slGenerated ligands are saved as an SDF file at the specified location (save_dir).
Evaluate the generated molecules using the evaluation script. This step calculates metrics including molecular validity, uniqueness, and interaction recovery.
Modify scripts/eval_spindr.sh as needed, then run:
bash scripts/eval_spindr.shContributions are welcome! If you have ideas, bug fixes, or improvements, please open an issue or submit a pull request.
This project is licensed under the MIT License.
If you use FLOWR in your research, please cite it as follows:
@misc{cremer2025flowrflowmatchingstructureaware,
title={FLOWR: Flow Matching for Structure-Aware De Novo, Interaction- and Fragment-Based Ligand Generation
5471
},
author={Julian Cremer and Ross Irwin and Alessandro Tibot and Jon Paul Janet and Simon Olsson and Djork-Arné Clevert},
year={2025},
eprint={2504.10564},
archivePrefix={arXiv},
primaryClass={q-bio.QM},
url={https://arxiv.org/abs/2504.10564},
}