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pillarszhang/deepblock

By pillarszhang

•Updated about 2 years ago

https://github.com/BioChemAI/DeepBlock

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pillarszhang/deepblock repository overview

⁠DeepBlock

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This is the official implementation of the paper "A Deep Learning Approach for Rational Ligand Generation with Property Control via Reactive Building Blocks". Additionally, we offer a user-friendly online platform⁠ to implement the functionality of DeepBlock.

Github⁠ - Docker Hub⁠ - Online Platform⁠

⁠Table of Contents

⁠Installation

git clone [email protected]:BioChemAI/DeepBlock.git
cd deepblock
conda env create -f environment.yml
conda activate deepblock_env
pip install -e .

Also, for Docker

git clone [email protected]:BioChemAI/DeepBlock.git
cd deepblock
docker build --target base -t deepblock .
docker run -it --rm deepblock

The image has been uploaded to Docker Hub.

docker pull pillarszhang/deepblock:20240801A

⁠Usage

Quick start, --input-type can be 'seq', 'pdb', 'url', 'pdb_fn', 'pdb_id'.

python scripts/quick_start/generate.py \
    --input-data 4IWQ \
    --input-type pdb_id \
    --num-samples 16 \
    --output-json tmp/generate_result.json

Docker.

docker run --rm \
    -v "$(pwd)"/tmp:/app/tmp \
    pillarszhang/deepblock:20240801A \
    bash -lc "python scripts/quick_start/generate.py \
        --input-data 4IWQ \
        --input-type pdb_id \
        --num-samples 16 \
        --output-json tmp/generate_result.json"

⁠Develop

It is recommended to use VSCode for development, as debugging configuration files are already available in .vscode.

⁠Dataset
⁠ChEMBL Dataset
wget https://ftp.ebi.ac.uk/pub/databases/chembl/ChEMBLdb/releases/chembl_31/chembl_31_chemreps.txt.gz
gzip -dk chembl_31_chemreps.txt.gz

Finally, run the script

python scripts/preprocess/chembl.py \
    --chembl-chemreps <PATH TO chembl_31_chemreps.txt>
⁠CrossDocked Dataset (Index by 3D-Generative-SBDD⁠)

Download from the compressed package we provide https://figshare.com/articles/dataset/crossdocked_pocket10_with_protein_tar_gz/25878871⁠ (recommended). The alternative method is to obtain the files from the 3D-Generative-SBDD's index file⁠ and the raw data for the CrossDocked2020 set⁠. The script will re-fetch the required files.

tar xzf crossdocked_pocket10_with_protein.tar.gz

The following files are required to exist:

  • $sbdd_dir/split_by_name.pt
  • $sbdd_dir/index.pkl
  • $sbdd_dir/1B57_HUMAN_25_300_0/5u98_D_rec_5u98_1kx_lig_tt_min_0_pocket10.pdb
  • $sbdd_dir/1B57_HUMAN_25_300_0/5u98_D_rec.pdb (Recommended method)
  • $crossdocked_dir/1B57_HUMAN_25_300_0/5u98_D_rec.pdb (Alternative method)

Finally, run the script

python scripts/preprocess/crossdocked.py \
    --sbdd-dir <PATH TO crossdocked_pocket10_with_protein> \
    # --crossdocked-dir <PATH TO CrossDocked2020> # Not needed when using the recommended method
⁠PDBbind Dataset (Optional)

Please download the following 3 files from the PDBbind v2020⁠ to the same directory (assuming it is $pdbbind_dir).

  1. Index files of PDBbind -> PDBbind_v2020_plain_text_index.tar.gz
  2. Protein-ligand complexes: The general set minus refined set -> PDBbind_v2020_other_PL.tar.gz
  3. Protein-ligand complexes: The refined set -> PDBbind_v2020_refined.tar.gz

To extract the files, first navigate to the $pdbbind_dir directory and then use the following command.

tarballs=("PDBbind_v2020_plain_text_index.tar.gz" "PDBbind_v2020_refined.tar.gz" "PDBbind_v2020_other_PL.tar.gz")
for tarball in "${tarballs[@]}"
do
  dirname=${tarball%%.*}
  mkdir -p "$dirname" && pv -N "Extracting $tarball" "$tarball" | tar xzf - -C "$dirname"
done

The following files are required to exist:

  • $pdbbind_dir/PDBbind_v2020_plain_text_index/index/INDEX_general_PL.2020
  • $pdbbind_dir/PDBbind_v2020_refined/refined-set/1a1e/1a1e_ligand.sdf
  • $pdbbind_dir/PDBbind_v2020_other_PL/v2020-other-PL/1a0q/1a0q_protein.pdb

Finally, run the script

python scripts/preprocess/pdbbind.py \
    --pdbbind-dir <PATH TO PDBBind>

# Filter test set
python scripts/preprocess/pdbbind_pick_set.py
⁠Merge Dictionary
python scripts/preprocess/merge_vocabs.py \
    --includes chembl crossdocked
⁠Train
⁠Pretraining on ChEMBL Dataset with ChEMBL+CrossDocked Dictionary
python scripts/cvae_complex/train.py \
    --include chembl \
    --device cuda:0 \
    --config scripts/cvae_complex/frag_pretrain_config.yaml \
    --vocab-fn "saved/preprocess/merge_vocabs/chembl,crossdocked&frag_vocab.json" \
    --no-valid-prior
⁠Training on CrossDocked Dataset

Replace 20230303_191022_be9e with pretrain ID.

python scripts/cvae_complex/train.py \
    --include crossdocked \
    --device cuda:0 \
    --config scripts/cvae_complex/complex_config.yaml \
    --base-train-id 20230303_191022_be9e

The checkpoint trained on ChEMBL and CrossDocked has been provided in the repository at deepblock/public/saved/cvae_complex/20230305_163841_cee4. By executing cp -r deepblock/public/saved ., you can directly continue with the following commands.

⁠Inference
⁠Ligand Generation

Replace 20230305_163841_cee4 with train ID.

python scripts/cvae_complex/sample.py \
    --include crossdocked \
    --device cuda:0 \
    --base-train-id 20230305_163841_cee4 \
    --num-samples 100 \
    --validate-mol \
    --embed-mol \
    --unique-mol
⁠Affinity optimization
python scripts/cvae_complex/optimize.py \
    --device cpu \
    --base-train-id 20230305_163841_cee4 \
    --num-samples 5000 \
    --complex-id F16P1_HUMAN_1_338_0/3kc1_A_rec_3kc1_2t6_lig_tt_min_0
⁠Property optimization (SA)

Train a drug toxicity decision tree predictor based on molecular fingerprints. The script will automatically download the dataset from TOXRIC⁠ and use TPOT for automatic parameter tuning.

The trained checkpoint has already been provided in the repository at deepblock/public/saved/regress_tox/20230402_135859_c91a.

python scripts/regress_tox/train.py

python scripts/cvae_complex/sample_sa.py \
    --device cpu \
    --base-train-id 20230305_163841_cee4 \
    --num-samples 100 \
    --num-steps 50 \
    --complex-id F16P1_HUMAN_1_338_0/3kc1_A_rec_3kc1_2t6_lig_tt_min_0

Tag summary

Content type

Image

Digest

sha256:f8ac0ffe7…

Size

3.4 GB

Last updated

about 2 years ago

docker pull pillarszhang/deepblock