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

```bash 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

bash 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 splitbyname.pt files and put them in the _data directory. Then, you can run the following command: bash python scripts/train_diffusion_decomp.py configs/training.yml

Sampling

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

bash 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.