MSCoD

Official implementation of "MSCoD: An Enhanced Bayesian Updating Framework with Multi-Scale Information Bottleneck and Cooperative Attention for Structure-Based Drug Design".

Acknowledgements

We thank the authors of MolCRAFT: Structure-Based Drug Design in Continuous Parameter Space for releasing their code. The code in this repository is based on their source code release MolCRAFT. If you find this code useful, please consider citing their work.

Framework

Environment

You can build your own environment through conda env create -f mscod.yml. Here the main packages are listed:

Package	Version
CUDA	11.6
NumPy	1.23.1
Python	3.9
PyTorch	1.12.0
PyTorch Geometric	2.1.0
RDKit	2023.9.5

For evaluation, you will need to install vina (affinity), posecheck (clash, strain energy, and key interactions), and spyrmsd (rmsd).

# 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

# for posecheck evaluation
git clone https://github.com/cch1999/posecheck.git
cd posecheck
git checkout 57a1938  # the calculation of strain energy used in our paper
pip install -e .
pip install -r requirements.txt
conda install -c mx reduce

# for spyrmsd
conda install spyrmsd -c conda-forge

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Data

Data used for training/evaluating the model should be placed in the data folder by default, and is accessible in the data Google Drive folder (from Targetdiff).

To train the model from scratch, download the lmdb file and split file into the data folder:

• crossdocked_v1.1_rmsd1.0_pocket10_processed_final.lmdb
• crossdocked_pocket10_pose_split.pt

To evaluate the model on the test set, download and unzip the test_set.zip into the data folder. It includes the original PDB files that will be used in Vina Docking.

---

Training

alternatively (with data folder correctly configured):

python train_bfn.py --exp_name ${EXP_NAME} --revision ${REVISION}

where the default values should be set the same as:

python train_bfn.py --sigma1_coord 0.03 --beta1 1.5 --lr 5e-4 --time_emb_dim 1 --epochs 15 --max_grad_norm Q --destination_prediction True --use_discrete_t True --num_samples 10 --sampling_strategy end_back_pmf

Testing

For quick evaluation of the official checkpoint:

python train_bfn.py --test_only --no_wandb --ckpt_path ./checkpoints/last.ckpt

Debugging

For quick debugging training process, run:

python train_bfn.py --no_wandb --debug --epochs 1

Sampling

We provide the pretrained checkpoint as last.ckpt.

Sampling for pockets in the testset

python train_bfn.py --config_file configs/default.yaml --exp_name ${EXP_NAME} --revision ${REVISION} --test_only --num_samples ${NUM_MOLS_PER_POCKET} --sample_steps 100

The output molecules vina_docked.pt for all 100 test pockets will be saved in ./logs/${USER}_bfn_sbdd/${EXP_NAME}/${REVISION}/test_outputs/${TIMESTAMP} folders.

Evaluation

Evaluating molecules

For binding affinity (Vina Score / Min / Dock) and molecular properties (QED, SA), it is calculated upon sampling.

For PoseCheck (strain energy, clashes) and other conformational results (bond length, bond angle, torsion angle, RMSD), please refer to eval/caculate_all_metrics folder.

Usage Instructions for Comprehensive Metrics Calculation

1. Obtain the vina_docked.pt file.
2. Run 1_add_rmsd_pt.py followed by 2_add_pose_pt.py.
3. After these steps, you will have vina_docked_pose_checked.pt. Finally, run 3_all_metrics.py to calculate all the metrics described in the paper.

Evaluating meta files

We provide samples for all SBDD baselines in the sample Google Drive folder.

Example: 7xkj Pocket Evaluation

Usage Instructions

First, generate ligands and obtain the JSON file containing some metrics:

1. python eval/7xkj/sample_for_pocket.py /home/xulong/AI/Codes/MSCoD/MSCoD/7xkj_gdp/7xkj_A_rec.pdb /home/xulong/AI/Codes/MSCoD/MSCoD/7xkj_gdp/7xkj_A_rec_7xkj_lig_gdp.sdf
2. python eval/7xkj/sample_for_pocket.py /home/xulong/AI/Codes/MSCoD/MSCoD/7xkj_6ic/7xkj_A_rec.pdb /home/xulong/AI/Codes/MSCoD/MSCoD/7xkj_6ic/7xkj_A_rec_7xkj_lig_6ic.sdf

Then, run the following command to visualize the results:

1. python view.py

Contact

If you have any questions or issues, please feel free to contact us via email([email protected]) or open an issue in this repository.