ReZero-Dock

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# ReZero-Dock: Realistic and flexible protein ligand docking from Zero

### [Paper on arXiv](https://arxiv.org/abs/2402.11459)
### This repository is under construction and will be updated frequently.

# Dataset

The files in `data` contain the names for the time-based data split.

If you want to train one of our models with the data then: 
1. download it from [zenodo](https://zenodo.org/record/6408497) 
2. unzip the directory and place it into `data` such that you have the path `data/PDBBind_processed`

## Setup Environment

We will set up the environment using [Anaconda](https://docs.anaconda.com/anaconda/install/index.html). Clone the
current repo

git clone https://github.com/Kirito-Ausna/ReDock.git

This is an example for how to set up a working conda environment to run the code (but make sure to use the correct pytorch, pytorch-geometric, cuda versions or cpu only versions):

conda create --name ReDock python=3.9
    conda activate ReDock
    conda install pytorch==1.11.0 pytorch-cuda=11.7 -c pytorch -c nvidia
    pip install torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric==2.0.4 -f https://data.pyg.org/whl/torch-1.11.0+cu117.html
    python -m pip install PyYAML scipy "networkx[default]" biopython rdkit-pypi e3nn spyrmsd pandas biopandas

Then you need to install ESM that we use both for protein sequence embeddings and for the protein structure prediction in case you only have the sequence of your target. Note that OpenFold (and so ESMFold) requires a GPU. If you don't have a GPU, you can still use DiffDock with existing protein structures.

pip install "fair-esm[esmfold]"
    pip install 'dllogger @ git+https://github.com/NVIDIA/dllogger.git'
    pip install 'openfold @ git+https://github.com/aqlaboratory/openfold.git@4b41059694619831a7db195b7e0988fc4ff3a307'

# Running ReDock on your own complexes
We support multiple input formats depending on whether you only want to make predictions for a single complex or for many at once.\
The protein inputs need to be `.pdb` files or sequences that will be folded with ESMFold. The ligand input can either be a SMILES string or a filetype that RDKit can read like `.sdf` or `.mol2`.

For a single complex: specify the protein with `--protein_path protein.pdb` or `--protein_sequence GIQSYCTPPYSVLQDPPQPVV` and the ligand with `--ligand ligand.sdf` or `--ligand "COc(cc1)ccc1C#N"`

For many complexes: create a csv file with paths to proteins and ligand files or SMILES. It contains as columns `complex_name` (name used to save predictions, can be left empty), `protein_path` (path to `.pdb` file, if empty uses sequence), `ligand_description` (SMILE or file path)  and `protein_sequence` (to fold with ESMFold in case the protein_path is empty).
An example .csv is at `data/protein_ligand_example_csv.csv` and you would use it with `--protein_ligand_csv protein_ligand_example_csv.csv`.

Before running our inference, you need to prepare the ESM embedding in a folder with the name of protein, proteins without names(e.g. in sequence format) with be renamed as complex_i. You can refer to next section or the ESM(https://github.com/facebookresearch/esm) to see how to generate esm_embeddings.
And you are ready to run inference:

python Reock_evaluate.py --protein_ligand_csv data/protein_ligand_example_csv.csv --out_dir results/user_predictions_small --inference_steps 20 --samples_per_complex 40 --batch_size 10 --actual_steps 18 --no_final_step_noise

Or `bash ./script/inference.sh` I recomand the inference style with many complexes(i.e. via a csv file) with explict name of every complex.

When providing the `.pdb` files you can run DiffDock also on CPU, however, if possible, we recommend using a GPU as the model runs significantly faster. Note that the first time you run DiffDock on a device the program will precompute and store in cache look-up tables for SO(2) and SO(3) distributions (typically takes a couple of minutes), this won't be repeated in following runs.

# Retraining ReDock
Download the data and place it as described in the "Dataset" section above.

### Generate the ESM2 embeddings for the proteins
First run:

python datasets/pdbbind_lm_embedding_preparation.py

Use the generated file `data/pdbbind_sequences.fasta` to generate the ESM2 language model embeddings using the library https://github.com/facebookresearch/esm by installing their repository and executing the following in their repository:

python scripts/extract.py esm2_t33_650M_UR50D pdbbind_sequences.fasta embeddings_output --repr_layers 33 --include per_tok --truncation_seq_length 4096

This generates the `embeddings_output` directory which you have to copy into the `data` folder of our repository to have `data/embeddings_output`.
Then run the command:

python datasets/esm_embeddings_to_pt.py

### Using the provided model weights for evaluation
We first generate the language model embeddings for the testset, then run inference with DiffDock, and then evaluate the files that DiffDock produced:

python datasets/esm_embedding_preparation.py --protein_ligand_csv data/testset_csv.csv --out_file data/prepared_for_esm_testset.fasta
    git clone https://github.com/facebookresearch/esm 
    cd esm
    pip install -e .
    cd ..
    HOME=esm/model_weights python esm/scripts/extract.py esm2_t33_650M_UR50D data/prepared_for_esm_testset.fasta data/esm2_output --repr_layers 33 --include per_tok
    python -m inference --protein_ligand_csv data/testset_csv.csv --out_dir results/user_predictions_testset --inference_steps 20 --samples_per_complex 40 --batch_size 10 --actual_steps 18 --no_final_step_noise
    python evaluate_files.py --results_path results/user_predictions_testset --file_to_exclude rank1.sdf --num_predictions 40

Or inference with our script
```bash
bash ./script/infernece.sh
```

<!--
To predict binding structures using the provided model weights run:

python -m evaluate --model_dir workdir/paper_score_model --ckpt best_ema_inference_epoch_model.pt --confidence_ckpt best_model_epoch75.pt --confidence_model_dir workdir/paper_confidence_model --run_name DiffDockInference --inference_steps 20 --split_path data/splits/timesplit_test --samples_per_complex 40 --batch_size 10 --actual_steps 18 --no_final_step_noise

To additionally save the .sdf files of the generated molecules, add the flag `--save_visualisation`
-->
### Training a model yourself and using those weights
Train the large score model:

python -m train --run_name big_score_model --test_sigma_intervals --esm_embeddings_path data/esm2_3billion_embeddings.pt --log_dir workdir --lr 1e-3 --tr_sigma_min 0.1 --tr_sigma_max 19 --rot_sigma_min 0.03 --rot_sigma_max 1.55 --batch_size 16 --ns 48 --nv 10 --num_conv_layers 6 --dynamic_max_cross --scheduler plateau --scale_by_sigma --dropout 0.1 --remove_hs --c_alpha_max_neighbors 24 --receptor_radius 15 --num_dataloader_workers 1 --cudnn_benchmark --val_inference_freq 5 --num_inference_complexes 500 --use_ema --distance_embed_dim 64 --cross_distance_embed_dim 64 --sigma_embed_dim 64 --scheduler_patience 30 --n_epochs 850

Or you can just run bash.
```bash
bash ./script/train.sh
```
The model weights are saved in the `workdir` directory.

Train a small score model with higher maximum translation sigma that will be used to generate the samples for training the confidence model:

python -m train --run_name small_score_model --test_sigma_intervals --esm_embeddings_path data/esm2_3billion_embeddings.pt --log_dir workdir --lr 1e-3 --tr_sigma_min 0.1 --tr_sigma_max 34 --rot_sigma_min 0.03 --rot_sigma_max 1.55 --batch_size 16 --ns 24 --nv 6 --num_conv_layers 5 --dynamic_max_cross --scheduler plateau --scale_by_sigma --dropout 0.1 --remove_hs --c_alpha_max_neighbors 24 --receptor_radius 15 --num_dataloader_workers 1 --cudnn_benchmark --val_inference_freq 5 --num_inference_complexes 500 --use_ema --scheduler_patience 30 --n_epochs 300

In practice, you could also likely achieve the same or better results by using the first score model for creating the samples to train the confidence model, but this is what we did in the paper.  
The score model used to generate the samples to train the confidence model does not have to be the same as the score model that is used with that confidence model during inference.

Train the confidence model by running the following:

python -m confidence.confidence_train --original_model_dir workdir/small_score_model --run_name confidence_model --inference_steps 20 --samples_per_complex 7 --batch_size 16 --n_epochs 100 --lr 3e-4 --scheduler_patience 50 --ns 24 --nv 6 --num_conv_layers 5 --dynamic_max_cross --scale_by_sigma --dropout 0.1 --all_atoms --remove_hs --c_alpha_max_neighbors 24 --receptor_radius 15 --esm_embeddings_path data/esm2_3billion_embeddings.pt --main_metric loss --main_metric_goal min --best_model_save_frequency 5 --rmsd_classification_cutoff 2 --cache_creation_id 1 --cache_ids_to_combine 1 2 3 4

first with `--cache_creation_id 1` then `--cache_creation_id 2` etc. up to 4

Now everything is trained and you can run inference with:

python ReDock_evaluate --model_dir workdir/big_score_model --ckpt best_ema_inference_epoch_model.pt --confidence_ckpt best_model_epoch75.pt --confidence_model_dir workdir/confidence_model --run_name DiffDockInference --inference_steps 20 --split_path data/splits/timesplit_test --samples_per_complex 40 --batch_size 10 --actual_steps 18 --no_final_step_noise
Or with
```bash
bash ./script/inference.sh
```
Note: the notebook `data/apo_alignment.ipynb` contains the code used to align the ESMFold-generated apo-structures to the holo-structures.

## License
MIT

## Citation
If you use this code or the data, please cite the following paper:

```
@article{huang2024re,
  title={Re-dock: towards flexible and realistic molecular docking with diffusion bridge},
  author={Huang, Yufei and Zhang, Odin and Wu, Lirong and Tan, Cheng and Lin, Haitao and Gao, Zhangyang and Li, Siyuan and Li, Stan and others},
  journal={arXiv preprint arXiv:2402.11459},
  year={2024}
}
```

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