Here we provide our implementation of the Confidence Bootstrapping method (https://arxiv.org/abs/2402.18396), pretrained diffusion and confidence models, and processed receptors from the DockGen validation and test sets.

The DockGen benchmark can be downloaded from Zenodo (https://zenodo.org/records/10656052) by running

curl -o data/BindingMOAD_2020_processed.tar https://zenodo.org/records/10656052/files/BindingMOAD_2020_processed.tar
curl -o data/DockGen.tar https://zenodo.org/records/10656052/files/DockGen.tar

After extraction, all 189 complexes in the DockGen test set can be found in data/BindingMOAD_2020_processed/test_names.npy, and the 85 complexes from DockGen-clusters can be found in data/BindingMOAD_2020_processed/test_names_bootstrapping.npy. The list of complexes in the DockGen benchmark can also be found at data/BindingMOAD_2020_processed/new_cluster_to_ligands.pkl, which is a dictionary with cluster names as keys and lists of ligand names as values. The downloaded files contain processed ligands and receptors from Binding MOAD.

We will set up the environment with anaconda Anaconda, and have provided an environment.yml file. While in the project directory, run

conda env create

Activate the environment

conda activate confidence-bootstrapping

In order to run the diffusion model, we need to generate ESM2 embeddings for complexes in Binding MOAD. First we prepare sequences:

python datasets/moad_lm_embedding_preparation.py --data_dir data/BindingMOAD_2020_processed/pdb_protein

Then, we install esm and generate embeddings for the test proteins:

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/BindingMOAD_2020_processed/moad_sequences_new.fasta data/esm2_output --repr_layers 33 --include per_tok

Then we convert the embeddings to a single .pt file:

python datasets/esm_embeddings_to_pt.py --esm_embeddings_path data/esm2_output    

We provide pretrained diffusion and confidence models at workdir/pretrained_score and workdir/pretrained_confidence respectively. In addition, we have provided eight finetuned models reported in the paper also in workdir, with the names of the folders as the prefix of the cluster names.

Finally, we can run the Confidence Bootstrapping finetuning on a cluster like Homo-oligomeric flavin-containing Cys decarboxylases, HFCD:

python -m finetune_train --sampling_alpha 1 --sampling_beta 1 --cudnn_benchmark --cb_inference_freq 5 --num_inference_complexes 100 --use_ema --n_epochs 10 --inference_samples 8 --moad_esm_embeddings_sequences_path data/sequences_to_id.fasta --moad_esm_embeddings_path data/BindingMOAD_2020_processed/moad_sequences_new.pt --moad_dir data/BindingMOAD_2020_processed --confidence_cutoff -4 --pretrain_ckpt best_ema_inference_epoch_model --pretrain_dir workdir/pretrained_score --filtering_ckpt best_model.pt --filtering_model_dir workdir/pretrained_confidence --max_complexes_per_couple 20 --cb_cluster "Homo-oligomeric flavin-containing Cys decarboxylases, HFCD" --fixed_length 100 --initial_iterations 5 --minimum_t 0 --cache_path cache --inference_batch_size 4 --save_model_freq 25 --split test --inference_iterations 4 --buffer_sampling_alpha 2 --buffer_sampling_beta 1

Note that the command above is not the same as the one used in experiments in the paper, which also samples random complexes from PDBBind at every bootstrapping step. To reproduce paper results, we need to download the PDBBind dataset:

1. download it from [zenodo](https://zenodo.org/record/6034088) 
2. unzip the directory and place it into `data` such that you have the path `data/PDBBind_processed`

Then, we can run the finetuning command with --keep_original_train and --totoal_trainset_size 100 to reproduce paper numbers.