CBGBench: Complex Binding Graph Benchmark is a benchmark for generative target-aware molecule design.
Paper [DiffBP] | Paper [D3FG] | Benchmark
Note
- Oct. 2024: CBGBench paper v3 has just integrated the state-of-the-art MolCraft and VoxBind into evaluation, and the integration of their codes is in process.
- Sep. 2024: CBGBench has joined the DeepModeling community, a community devoted to AI for science, as a sandbox-level project. Learn more about DeepModeling
This is the official code repository of the paper 'CBGBench: Fill in the Blank of Protein-Molecule Binding Graph', which aims to unify target-aware molecule design with single code implementation. Until now, we have included 7 methods as shown below:
| Model | Paper link | Github |
|---|---|---|
| Pocket2Mol | https://arxiv.org/abs/2205.07249 | https://github.com/pengxingang/Pocket2Mol |
| GraphBP | https://arxiv.org/abs/2204.09410 | https://github.com/divelab/GraphBP |
| DiffSBDD | https://arxiv.org/abs/2210.13695 | https://github.com/arneschneuing/DiffSBDD |
| DiffBP | https://arxiv.org/abs/2211.11214 | Here is the official implementation. |
| TargetDiff | https://arxiv.org/abs/2303.03543 | https://github.com/guanjq/targetdiff |
| FLAG | https://openreview.net/forum?id=Rq13idF0F73 | https://github.com/zaixizhang/FLAG |
| D3FG | https://arxiv.org/abs/2306.13769 | Here is the official implementation. |
These models are initially established for de novo molecule generation, and we extend more tasks including linker design, fragment growing, scaffold hopping, and side chain decoration.
conda env create -f environment.yml
conda activate cbgbench
conda install pytorch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 pytorch-cuda=11.8 -c pytorch -c nvidia
conda install pyg pytorch-scatter pytorch-cluster -c pyg
# install rdkit, efgs, obabel, etc.
pip install --use-pep517 EFGs
pip install biopython
pip install lxml
conda install rdkit openbabel tensorboard tqdm pyyaml easydict python-lmdb -c conda-forge
# install plip
mkdir tools
cd tools
git clone https://github.com/pharmai/plip.git
cd plip
python setup.py install
alias plip='python plip/plip/plipcmd.py'
cd ..
### Note that if there is an error in setup.py, it can be ignored as long as openbabel is installed.
# install docking tools
conda install -c conda-forge numpy swig boost-cpp sphinx sphinx_rtd_theme
python -m pip install git+https://github.com/Valdes-Tresanco-MS/AutoDockTools_py3
pip install meeko==0.1.dev3 scipy pdb2pqr vina
# If you are unable to install vina, you can try: conda install vina
# If you encounter the following error:
# ImportError: libtiff.so.5: cannot open shared object file: No such file or directory
# Please try the following steps to resolve it:
pip uninstall pillow
pip install pillow
(i) Download the processed dataset from Google Drive. For each dataset, it corresponds to different methods and tasks. Please refer to the Table below to find the processed data that you need to use. Note that to train D3FG, it is a two-stage model that firstly generates functional groups and then generates linker atoms, so we named the two-stage model 'D3FG_fg' and 'D3FG_linker', and the training data is different for them.
Table: The data file name corresponding to different methods and tasks.
| model | task | file name |
|---|---|---|
| Pocket2Mol | de novo | data/pl/crossdocked_v1.1_rmsd1.0_pocket10_processed_fullatom.lmdb |
| GraphBP | de novo | data/pl/crossdocked_v1.1_rmsd1.0_pocket10_processed_fullatom.lmdb |
| DiffSBDD | de novo | data/pl/crossdocked_v1.1_rmsd1.0_pocket10_processed_fullatom.lmdb |
| DiffBP | de novo | data/pl/crossdocked_v1.1_rmsd1.0_pocket10_processed_fullatom.lmdb |
| TargetDiff | de novo | data/pl/crossdocked_v1.1_rmsd1.0_pocket10_processed_fullatom.lmdb |
| FLAG | de novo | data/pl_arfg/crossdocked_v1.1_rmsd1.0_pocket10_processed_arfuncgroup.lmdb |
| D3FG_fg | de novo | data/pl_fg/crossdocked_v1.1_rmsd1.0_pocket10_processed_funcgroup.lmdb |
| D3FG_linker | de novo | data/pl/crossdocked_v1.1_rmsd1.0_pocket10_processed_linker.lmdb |
| Pocket2Mol | fragment | data/pl_decomp/crossdocked_v1.1_rmsd1.0_pocket10_processed_frag.lmdb |
| GraphBP | fragment | data/pl_decomp/crossdocked_v1.1_rmsd1.0_pocket10_processed_frag.lmdb |
| DiffSBDD | fragment | data/pl_decomp/crossdocked_v1.1_rmsd1.0_pocket10_processed_frag.lmdb |
| DiffBP | fragment | data/pl_decomp/crossdocked_v1.1_rmsd1.0_pocket10_processed_frag.lmdb |
| TargetDiff | fragment | data/pl_decomp/crossdocked_v1.1_rmsd1.0_pocket10_processed_frag.lmdb |
| Pocket2Mol | linker | data/pl_decomp/crossdocked_v1.1_rmsd1.0_pocket10_processed_linker.lmdb |
| GraphBP | linker | data/pl_decomp/crossdocked_v1.1_rmsd1.0_pocket10_processed_linker.lmdb |
| DiffSBDD | linker | data/pl_decomp/crossdocked_v1.1_rmsd1.0_pocket10_processed_linker.lmdb |
| DiffBP | linker | data/pl_decomp/crossdocked_v1.1_rmsd1.0_pocket10_processed_linker.lmdb |
| TargetDiff | linker | data/pl_decomp/crossdocked_v1.1_rmsd1.0_pocket10_processed_linker.lmdb |
| Pocket2Mol | scaffold | data/pl_decomp/crossdocked_v1.1_rmsd1.0_pocket10_processed_scaffold.lmdb |
| GraphBP | scaffold | data/pl_decomp/crossdocked_v1.1_rmsd1.0_pocket10_processed_scaffold.lmdb |
| DiffSBDD | scaffold | data/pl_decomp/crossdocked_v1.1_rmsd1.0_pocket10_processed_scaffold.lmdb |
| DiffBP | scaffold | data/pl_decomp/crossdocked_v1.1_rmsd1.0_pocket10_processed_scaffold.lmdb |
| TargetDiff | scaffold | data/pl_decomp/crossdocked_v1.1_rmsd1.0_pocket10_processed_scaffold.lmdb |
| Pocket2Mol | side chain | data/pl_decomp/crossdocked_v1.1_rmsd1.0_pocket10_processed_sidechain.lmdb |
| GraphBP | side chain | data/pl_decomp/crossdocked_v1.1_rmsd1.0_pocket10_processed_sidechain.lmdb |
| DiffSBDD | side chain | data/pl_decomp/crossdocked_v1.1_rmsd1.0_pocket10_processed_sidechain.lmdb |
| DiffBP | side chain | data/pl_decomp/crossdocked_v1.1_rmsd1.0_pocket10_processed_sidechain.lmdb |
| TargetDiff | side chain | data/pl_decomp/crossdocked_v1.1_rmsd1.0_pocket10_processed_sidechain.lmdb |
(ii) Copy the datasets to ./data dir, in which the complete data file directory will look like
- CGBBench
- data
- pl
- crossdocked_name2id.pt
- crossdocked_v1.1_rmsd1.0_pocket10_processed_fullatom.lmdb
- crossdocked_v1.1_rmsd1.0_pocket10_processed_linker.lmdb
- pl_arfg
- crossdocked_name2id_arfuncgroup.pt
- crossdocked_v1.1_rmsd1.0_pocket10_processed_arfuncgroup.lmdb
- pl_decomp
- crossdocked_name2id_frag.pt
- crossdocked_name2id_linker.pt
- crossdocked_name2id_scaffold.pt
- crossdocked_name2id_sidechain.pt
- crossdocked_v1.1_rmsd1.0_pocket10_processed_frag.lmdb
- crossdocked_v1.1_rmsd1.0_pocket10_processed_linker.lmdb
- crossdocked_v1.1_rmsd1.0_pocket10_processed_scaffold.lmdb
- crossdocked_v1.1_rmsd1.0_pocket10_processed_sidechain.lmdb
- pl_fg
- crossdocked_name2id_funcgroup.pt
- crossdocked_v1.1_rmsd1.0_pocket10_processed_funcgroup.lmdb
(i) Download crossdocked_v1.1_rmsd1.0.tar.gz from TargetDiff Drive, and copy it to ./raw_data/ with
mkdir raw_data
tar -xzvf crossdocked_v1.1_rmsd1.0.tar.gz ./raw_data
(ii) Run the following:
python ./scripts/extract_pockets.py --source raw_data/crossdocked_v1.1_rmsd1.0 --dest raw_data/crossdocked_v1.1_rmsd1.0_pocket10
(iii) In training, the Dataset will be prepared for each task. Please refer to Training.
Also, you can download the processes targets file case_study from Google Drive, and copy it to the ./data directory, which will lead the dir to
- CGBBench
- data
- case_study
- processed
- case_study_processed_fullatom.lmdb
- case_study_processed_funcgroup.lmdb
- case_study_name2id.pt
...
python train.py --config ./configs/{task}/train/{method}.yml --logdir ./logs/{task}/{method}
{task} can be replaced with denovo, linker, frag, scaffold and sidechain, and {method} can be replaced with the model name. The following table gives the detailed method-task pairs and the replacement.
Table: method-task pairs used to train from scratch.
| Method | Task | {method} + {task} |
|---|---|---|
| Pocket2Mol | de novo | pocket2mol + denovo |
| GraphBP | de novo | graphbp + denovo |
| DiffSBDD | de novo | diffsbdd + denovo |
| DiffBP | de novo | diffbp + denovo |
| TargetDiff | de novo | targetdiff + denovo |
| FLAG | de novo | flag + denovo |
| D3FG | de novo | d3fg_fg + denovo ; d3fg_linker + denovo |
| Pocket2Mol | linker design | pocket2mol + linker |
| GraphBP | linker design | graphbp + linker |
| DiffSBDD | linker design | diffsbdd + linker |
| DiffBP | linker design | diffbp + linker |
| TargetDiff | linker design | targetdiff + linker |
| Pocket2Mol | fragment growing | pocket2mol + frag |
| GraphBP | fragment growing | graphbp + frag |
| DiffSBDD | fragment growing | diffsbdd + frag |
| DiffBP | fragment growing | diffbp + frag |
| TargetDiff | fragment growing | targetdiff + frag |
| Pocket2Mol | scaffold hopping | pocket2mol + scaffold |
| GraphBP | scaffold hopping | graphbp + scaffold |
| DiffSBDD | scaffold hopping | diffsbdd + scaffold |
| DiffBP | scaffold hopping | diffbp + scaffold |
| TargetDiff | scaffold hopping | targetdiff + scaffold |
| Pocket2Mol | side chain decoration | pocket2mol + sidechain |
| GraphBP | side chain decoration | graphbp + sidechain |
| DiffSBDD | side chain decoration | diffsbdd + sidechain |
| DiffBP | side chain decoration | diffbp + sidechain |
| TargetDiff | side chain decoration | targetdiff + sidechain |
Note that D3FG and FLAG are not compatible with the extended tasks, and D3FG utilizes 2-stage-training strategies, so if you want to train D3FG yourself, you need to run:
python train.py --config ./configs/denovo/train/d3fg_fg.yml --logdir ./logs/denovo/d3fg_fg
python train.py --config ./configs/denovo/train/d3fg_linker.yml --logdir ./logs/denovo/d3fg_linker
2. Also, you can use our pretrained ones from Google Drives [TODO: Release the pretrained models], the corresponding pretrained data file names to different tasks and models are provided in the table below:
TODO: Table with pretrained models
Download the pretrained .pt checkpoints that you need, and copy them into ./logs/, which will lead to the following directory structure:
# TODO
- CBGBench
- logs
- denovo
- d3fg_fg
- pretrain
- checkpoints
- 951000.pt
- d3fg_linker
- pretrain
- checkpoints
- 4840000.pt
- diffbp
- pretrain
- checkpoints
- 4848000.pt
...
- frag
- diffbp
- pretrain
- checkpoints
- 476000.pt
...
...
Once the model is trained, you can draw samples from them on the test pockets, with the following:
bash generate.sh --method {method} --task {task} --tag {tag} --checkpoint {ckpt_number}
In the command, {method} and {task} pair can be found in the former Table on method-task pairs. {tag} should be replaced with selftrain or pretrain, according to the checkpoints you use.
If the --checkpoint parameter is provided without a number, it will automatically find the latest .pt file. If a number is provided, it will use the specified checkpoint file.
For example, if you want to generate samples on the test set for de novo design with self-trained targetdiff model, you can run:
bash generate.sh --method targetdiff --task denovo --tag selftrain --checkpoint
Or if you want to test the checkpoint of the 100000-th.
bash generate.sh --method targetdiff --task denovo --tag selftrain --checkpoint 100000
Note that D3FG uses a two-step generation strategy, so the command should be
bash generate.sh --method d3fg_fg --task denovo --tag {tag} --checkpoint # generate the functional groups
bash generate.sh --method d3fg_linker --task denovo --tag {tag} --checkpoint # generate the linkers
Run the following:
cd evaluate_scripts
bash evaluate.sh --method {method} --task {task} --tag {tag}
For example, if you have trained TargetDiff by yourself, run
bash evaluate.sh --method targetdiff --task denovo --tag selftrain
bash evaluate.sh --method targetdiff --task frag --tag selftrain
bash evaluate.sh --method targetdiff --task linker --tag selftrain
bash evaluate.sh --method targetdiff --task scaffold --tag selftrain
bash evaluate.sh --method targetdiff --task sidechain --tag selftrain
Or if you have downloaded pretrained models, please run
cd evaluate_scripts
bash bash evaluate.sh --method {method} --task {task} --tag pretrained
-
Use the pretrained models to generate molecules on real-world targets flexibly.
-
Include more models, such as 3DSBDD, DecompDiff, VoxMol, and LiGAN.
-
Release the pretrained models, test the feasibility and reproducivity once the paper is published.
The codebase is initialized by Haitao Lin, with the CBGbench v1 as our motivation; The subsequent running of the code repository will be managed by Guojiang Zhao and his team of DP Technology.
If you find the repository is helpful to your research or projects, please cite to our benchmark paper as reference:
@misc{lin2024cbgbench,
title={CBGBench: Fill in the Blank of Protein-Molecule Complex Binding Graph},
author={Haitao Lin and Guojiang Zhao and Odin Zhang and Yufei Huang and Lirong Wu and Zicheng Liu and Siyuan Li and Cheng Tan and Zhifeng Gao and Stan Z. Li},
year={2024},
eprint={2406.10840},
archivePrefix={arXiv},
}
And our former works of DiffBP and D3FG:
@misc{lin2024diffbpgenerativediffusion3d,
title={DiffBP: Generative Diffusion of 3D Molecules for Target Protein Binding},
author={Haitao Lin and Yufei Huang and Odin Zhang and Siqi Ma and Meng Liu and Xuanjing Li and Lirong Wu and Jishui Wang and Tingjun Hou and Stan Z. Li},
year={2024},
eprint={2211.11214},
archivePrefix={arXiv},
primaryClass={q-bio.BM},
url={https://arxiv.org/abs/2211.11214},
}
@inproceedings{
lin2023functionalgroupbased,
title={Functional-Group-Based Diffusion for Pocket-Specific Molecule Generation and Elaboration},
author={Haitao Lin and Yufei Huang and Odin Zhang and Yunfan Liu and Lirong Wu and Siyuan Li and Zhiyuan Chen and Stan Z. Li},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023},
url={https://openreview.net/forum?id=lRG11M91dx}
}
This project is licensed under the terms of the GPL-3.0 license.