ConGLUDe

Contrastive Geometric Learning Unlocks Unified Structure- and Ligand-Based Drug Design

ConGLUDe is a single contrastive geometric architecture that unifies structure- and ligand-based data and tasks. It couples a geometric protein encoder that produces whole-protein representations and implicit embeddings of predicted binding sites with a fast ligand encoder. By aligning ligands with both global protein representations and multiple candidate binding sites through contrastive learning, ConGLUDe supports a variety of drug discovery tasks, including virtual screening, target fishing, binding site prediction, and ligand-conditioned pocket ranking.

📄 Link to paper

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⚙️ Environment Set-Up

Clone the repository:

git clone https://github.com/ml-jku/conglude.git
cd ConGLUDe

The following creates and activates a conda environment with all necessary dependencies including the ConGLUDe source code:

bash setup_env.sh
conda activate conglude

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📥 Download Data

The evaluation datasets corresponding to this repository are available here.

To download and unzip all datasets into the default data folder, run:

python download_data.py

You can download individual datasets by specifying the --dataset_name argument. For example, to download the LIT-PCBA dataset:

python download_data.py --dataset_name litpcba

Available datasets: litpcba, dude, kinobeads, pdbbind_time, posebusters, asd, coach420, holo4k, pdbbind_refined

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✅ Reproduce Paper Results

To reproduce the results reported in the paper, use the evaluation script:

python eval.py

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📊 Evaluate a New Dataset

You can evaluate a custom labeled dataset with ConGLUDe by following these steps:

Step 1 — Create a dataset folder for your dataset:

data/datasets/test_datasets/<dataset_name>

Step 2 - Add required files:

At minimum, include info/proteins.txt. This file must contain a list of PDB IDs (one per line). If ligands cannot be extracted directly from the PDB files, provide active and inactive molecules for each protein:

raw/smiles_files/<pdb_id>/actives.txt
raw/smiles_files/<pdb_id>/inactives.txt

Step 3 - Create a YAML configuration for your dataset:

configs/datamodule/test_datasets/{dataset_name}/{dataset_name}.yaml

For details on configuration parameters see conglude/utils/data_processing.py and conglude/datamodule.py.

Step 4 - Register the dataset:

Add your dataset name to configs/datamodule/test_datasets.yaml.

Step 5 - Run the evaluation script.

python eval.py

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🧬 Embed Proteins

To generate ConGLUDe protein and pocket embeddings for a custom dataset, first, create a file listing the PDB IDs of the proteins you want to embed (one PDB ID per line): data/datasets/predict_datasets/<dataset_name>/info/proteins.txt

By default, the corresponding PDB files are automatically downloaded from https://www.rcsb.org/. If you already have PDB files locally, specify the directory when running the script.

python embed_proteins.py --dataset_name <dataset_name> --pdb_dir <path_to_pdbs>

The output embeddings will be saved in results/<dataset_name>/<timestamp>/embeddings.

Additionally, pocket predictions are saved in a data frame results/<dataset_name>/<timestamp>/predictions/pp_predictions.csv with the following columns:

Column	Meaning
protein_name	PDB ID of the protein
pocket_name	Identifier of the predicted binding pocket
pred_x, pred_y, pred_z	X, Y and Z-coordinates of the pocket center (in Å)
confidence	Confidence score of the pocket prediction (higher = more confident)

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🧪 Embed Ligands

To generate ligand embeddings, create a file containing SMILES strings of small molecules: data/datasets/predict_datasets/<dataset_name>/info/smiles.txt

Then, run:

python embed_ligands.py --dataset_name <dataset_name>

The output embeddings will be saved as results/<dataset_name>/<timestamp>/embeddings/ligand_embeddings.npy.

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🔮 Make Predictions

To make virtual screening and ligand-conditioned pocket ranking predictions, place both proteins.txt and smiles.txt (as in the previous two sections) in data/datasets/predict_datasets/<dataset_name>/info/ and run:

python predict.py --dataset_name <dataset_name>

Predictions are saved in results/<dataset_name>/<timestamp>/predictions/ as vs_predictions.npy (protein–ligand similarity matrix) and pr_predictions.npy (pocket–ligand similarity matrix).

To match rows of these similarity matrices to protein/pocket names, those are saved in results/<dataset_name>/<timestamp>/embeddings. Column ID to SMILES mappings can be found in data/datasets/predict_datasets/<dataset_name>/processed/ligand_embeddings/index2smiles.json

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📚 Citation

If you use ConGLUDe in your research, please cite:

@misc{schneckenreiter2026conglude,
  title={Contrastive Geometric Learning Unlocks Unified Structure- and Ligand-Based Drug Design},
  author={Lisa Schneckenreiter and Sohvi Luukkonen and Lukas Friedrich and Daniel Kuhn and Günter Klambauer},
  year={2026},
  eprint={2601.09693},
  archivePrefix={arXiv},
  primaryClass={cs.LG},
  url={https://arxiv.org/abs/2601.09693}
}