DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Design

This repository is the official implementation of DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Design.

Dependencies

Install via Conda and Pip

conda create -n decompdiff python=3.8
conda activate decompdiff
conda install pytorch pytorch-cuda=11.6 -c pytorch -c nvidia
conda install pyg -c pyg
conda install rdkit openbabel tensorboard pyyaml easydict python-lmdb -c conda-forge

# For decomposition
conda install -c conda-forge mdtraj
pip install alphaspace2

# For Vina Docking
pip install meeko==0.1.dev3 scipy pdb2pqr vina==1.2.2 
python -m pip install git+https://github.com/Valdes-Tresanco-MS/AutoDockTools_py3

Preprocess

python scripts/data/preparation/preprocess_subcomplex.py configs/preprocessing/crossdocked.yml

We have provided the processed dataset file here.

Training

To train the model from scratch, you need to download the *.lmdb, *_name2id.pt and split_by_name.pt files and put them in the data directory. Then, you can run the following command:

python scripts/train_diffusion_decomp.py configs/training.yml

Sampling

To sample molecules given protein pockets in the test set, you need to download test_index.pkl and test_set.zip files, unzip it and put them in the data directory. Then, you can run the following command:

python scripts/sample_diffusion_decomp.py configs/sampling_drift.yml  \
  --outdir $SAMPLE_OUT_DIR -i $DATA_ID --prior_mode {ref_prior, beta_prior}

We have provided the trained model checkpoint here.

If you want to sample molecules with beta priors, you also need to download files in this directory.

Evaluation

python scripts/evaluate_mol_from_meta_full.py $SAMPLE_OUT_DIR \
  --docking_mode {none, vina_score, vina_full} \
  --aggregate_meta True --result_path $EVAL_OUT_DIR

Results

• JSD of bond distances

| Bond | liGAN | GraphBP | AR | Pocket2Mol | TargetDiff | Ours | |------|-------|---------|-------|------------|------------|-----------| | C-C | 0.601 | 0.368 | 0.609 | 0.496 | 0.369 | 0.359 | | C=C | 0.665 | 0.530 | 0.620 | 0.561 | 0.505 | 0.537 | | C-N | 0.634 | 0.456 | 0.474 | 0.416 | 0.363 | 0.344 | | C=N | 0.749 | 0.693 | 0.635 | 0.629 | 0.550 | 0.584 | | C-O | 0.656 | 0.467 | 0.492 | 0.454 | 0.421 | 0.376 | | C=O | 0.661 | 0.471 | 0.558 | 0.516 | 0.461 | 0.374 | | C:C | 0.497 | 0.407 | 0.451 | 0.416 | 0.263 | 0.251 | | C:N | 0.638 | 0.689 | 0.552 | 0.487 | 0.235 | 0.269 |

• JSD of bond angles

| Angle | liGAN | GraphBP | AR | Pocket2Mol | TargetDiff | Ours | |-------|-------|---------|-------|------------|------------|-----------| | CCC | 0.598 | 0.424 | 0.340 | 0.323 | 0.328 | 0.314 | | CCO | 0.637 | 0.354 | 0.442 | 0.401 | 0.385 | 0.324 | | CNC | 0.604 | 0.469 | 0.419 | 0.237 | 0.367 | 0.297 | | OPO | 0.512 | 0.684 | 0.367 | 0.274 | 0.303 | 0.217 | | NCC | 0.621 | 0.372 | 0.392 | 0.351 | 0.354 | 0.294 | | CC=O | 0.636 | 0.377 | 0.476 | 0.353 | 0.356 | 0.259 | | COC | 0.606 | 0.482 | 0.459 | 0.317 | 0.389 | 0.339 |

• Main results

| Methods | Vina Score (↓) | Vina Min (↓) | Vina Dock (↓) | High Affinity (↑) | QED (↑) | SA (↑) | Success Rate (↑) | |------------|---------------------|-------------------|--------------------|------------------------|--------------|-------------|-----------------------| | Reference | -6.46 | -6.49 | -7.26 | - | 0.47 | 0.74 | 25.0% | | liGAN | - | - | -6.20 | 11.1% | 0.39 | 0.57 | 3.9% | | GraphBP | - | - | -4.70 | 6.7% | 0.45 | 0.48 | 0.1% | | AR | -5.64 | -5.88 | -6.62 | 31.0% | 0.50 | 0.63 | 7.1% | | Pocket2Mol | -4.70 | -5.82 | -6.79 | 51.0% | 0.57 | 0.75 | 24.4% | | TargetDiff | -6.30 | -6.83 | -7.91 | 59.1% | 0.48 | 0.58 | 10.5% | | Ours | -6.04 | -7.09 | -8.43 | 71.0% | 0.43 | 0.60 | 24.5% |

Security

If you discover a potential security issue in this project, or think you may have discovered a security issue, we ask that you notify Bytedance Security via our security center or vulnerability reporting email.

Please do not create a public GitHub issue.

License

This project is licensed under the Creative Commons Attribution-NonCommercial 4.0 International Public License.