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OneScience-Group/
TargetDiff

Name: TargetDiff
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TargetDiff

Model Overview

TargetDiff is a bioinformatics model for target-aware molecule generation and protein-ligand affinity prediction.

The original paper is 3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction (ICLR 2023).

Model Description

TargetDiff is based on a 3D equivariant diffusion network. Given a protein binding pocket, it generates candidate small molecules and can use an EGNN property prediction branch to predict protein-ligand complex affinity.

The current package organizes TargetDiff example scripts, a snapshot of the model implementation, sample inputs, and pretrained weights in the same directory. Datasets will be uploaded later.

Use Cases

Use case	Description
Protein-ligand affinity prediction	Takes a protein PDB file and ligand SDF file as input, and outputs molar concentration predictions for `Ki`, `Kd`, or `IC50`
Target-aware small molecule generation	Takes a protein binding pocket PDB file as input, and outputs generated molecule `sample.pt` files and SDF files for molecules that can be reconstructed
Diffusion model training	Trains the TargetDiff molecule generation model using CrossDocked2020 pocket data
Property prediction training	Trains an EGNN affinity prediction model using PDBbind data
Generated result evaluation	Computes metrics such as stability, reconstruction success rate, QED, SA, and optional Vina docking metrics for sampling results

Usage

1. Using OneCode

You can try intelligent one-click AI4S programming through the OneCode online environment:

Try intelligent one-click AI4S programming

2. Manual Installation and Usage

Hardware Requirements

Running on a GPU or DCU is recommended.
CPU can be used for connectivity checks, but it is relatively slow.
DCU users need to install DTK in advance. DTK 25.04.2 or later is recommended, or the OneScience-recommended version that matches the current cluster.

Software Requirements

First enter an environment where the OneScience bioinformatics dependencies have been installed.

Environment Checks

NVIDIA GPU:

nvidia-smi

Hygon DCU:

hy-smi

Quick Start

1. Install the Runtime Environment

bash

conda create -n onescience311 python=3.11 -y
conda activate onescience311
pip install onescience[bio] -i http://mirrors.onescience.ai:3141/pypi/simple/  --trusted-host mirrors.onescience.ai

If the following code cannot find required libraries at runtime, activate CUDA as shown below.

bash

source ${ROCM_PATH}/cuda/env.sh
export LD_LIBRARY_PATH="$CONDA_PREFIX/lib:$LD_LIBRARY_PATH"
export LD_LIBRARY_PATH="$CONDA_PREFIX/lib/python3.11/site-packages/fastpt/torch/lib:$LD_LIBRARY_PATH"

2. Download the Model

bash

hf download OneScience-Group/targetdiff --local-dir ./targetdiff
cd targetdiff

Training Weights and Datasets

Training weights are already included in the weights folder and can be used directly after downloading the model package. Datasets will be uploaded to Hugging Face soon, and command-line downloads will be supported later.

3. Affinity Prediction

3.1 Affinity Prediction Training

bash scripts/train_prop.sh

This script automatically performs the following steps:

Extract binding pockets from the PDBbind refined set.
Split the training, validation, and test sets according to the coreset.
Train an EGNN-based protein-ligand binding affinity prediction model.

3.2 Affinity Prediction Evaluation

Use the trained affinity prediction model to evaluate on the test set. The expected official metrics on PDBBind v2016 are:

RMSE	MAE	R²	Pearson	Spearman
1.316	1.031	0.633	0.797	0.782

Run:

bash

export PYTHONPATH=../../../src:$PYTHONPATH
python scripts/property_prediction/eval_prop.py \
    --ckpt_path ${ONESCIENCE_MODELS_DIR}/targetdiff/pretrained_models/egnn_pdbbind_v2016.pt \
    --device cuda

3.3 Affinity Prediction Inference

bash scripts/inference.sh

By default, this script performs affinity prediction on the example protein-ligand pair 3ug2, using the default weights and example data:

Model weights: ${ONESCIENCE_MODELS_DIR}/targetdiff/pretrained_models/egnn_pdbbind_v2016.pt
Protein: ${ONESCIENCE_DATASETS_DIR}/targetdiff/examples/3ug2_protein.pdb
Ligand: ${ONESCIENCE_DATASETS_DIR}/targetdiff/examples/3ug2_ligand.sdf
Affinity type: default Kd
Compute device: default cuda

4. Molecular Sampling

4.1 Single Test-Set Sample

bash

export PYTHONPATH=../../../src:$PYTHONPATH
python -m scripts.sample_diffusion configs/sampling.yml -i 0 --batch_size 50 --result_path ./outputs

This script reads the diffusion model checkpoint from configs/sampling.yml, generates candidate ligand molecules for the i-th pocket in the test set, and saves the result as result_i.pt.

4.2 Multi-GPU Batch Sampling

bash scripts/batch_sample_diffusion.sh configs/sampling.yml outputs 4 0 0

Parameter descriptions:

Parameter	Description
`$1`	Sampling configuration file path
`$2`	Result output directory
`$3`	Total number of worker nodes
`$4`	Current node index, starting from 0
`$5`	Starting data index

Example of multi-GPU parallel sampling:

bash

CUDA_VISIBLE_DEVICES=0 bash scripts/batch_sample_diffusion.sh configs/sampling.yml outputs 4 0 0 &
CUDA_VISIBLE_DEVICES=1 bash scripts/batch_sample_diffusion.sh configs/sampling.yml outputs 4 1 0 &
CUDA_VISIBLE_DEVICES=2 bash scripts/batch_sample_diffusion.sh configs/sampling.yml outputs 4 2 0 &
CUDA_VISIBLE_DEVICES=3 bash scripts/batch_sample_diffusion.sh configs/sampling.yml outputs 4 3 0 &
wait

The script internally fixes TOTAL_TASKS=100 and BATCH_SIZE=50, and assigns samples to nodes by index modulo.

4.3 Sampling for a Custom PDB Pocket

bash

export PYTHONPATH=../../../src:$PYTHONPATH
python -m scripts.sample_for_pocket configs/sampling.yml \
    --pdb_path /path/to/pocket.pdb \
    --result_path ./outputs_pdb \
    --num_samples 5 \
    --batch_size 1

Parameter descriptions:

Parameter	Required	Description
`config`	Yes	Sampling configuration file path
`--pdb_path`	Yes	Protein pocket PDB file path. A 10 Å pocket is recommended.
`--result_path`	No	Result output directory, default `./outputs_pdb`
`--num_samples`	No	Number of molecules to sample, read from the configuration file by default
`--batch_size`	No	Batch size, default `100`
`--device`	No	Compute device, default `cuda:0`

Sampling results are saved as outputs_pdb/sample.pt, and successfully reconstructed molecules are additionally written to outputs_pdb/sdf/.

5. Generated Molecule Evaluation

5.1 Evaluation from Sampling Results

If docking evaluation is required (vina_score / vina_dock / qvina), also install:

bash

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

bash

export PYTHONPATH=../../../src:$PYTHONPATH
python scripts/evaluate_diffusion.py ./outputs --docking_mode vina_score --protein_root /path/to/protein_root

Parameter descriptions:

Parameter	Required	Description
`sample_path`	Yes	Sampling result directory containing `result_*.pt` files
`--docking_mode`	Yes	Docking mode. Options: `none`, `vina_score`, `vina_dock`, `qvina`
`--protein_root`	No	Root directory of the original protein files, used for docking evaluation
`--eval_step`	No	Which sampling step to evaluate, default `-1` (the final step)
`--eval_num_examples`	No	Number of samples to evaluate, default all
`--exhaustiveness`	No	Docking search intensity, default `16`
`--save`	No	Whether to save evaluation results, default `True`

Supported docking modes:

Mode	Description
`none`	Computes only metrics such as validity, uniqueness, and novelty, without docking
`vina_score`	Uses AutoDock Vina to score generated molecules
`vina_dock`	Uses AutoDock Vina to redock generated molecules
`qvina`	Uses QuickVina for docking

The first run in vina_score or vina_dock mode may take some time to prepare pdbqt and pqr files.

5.2 Evaluation from Meta Files

The official project provides sampled and docked meta files, including TargetDiff and baselines such as CVAE, AR, and Pocket2Mol. These files can be downloaded and evaluated directly:

Meta file	Corresponding paper
`crossdocked_test_vina_docked.pt`	Original test-set docking results
`cvae_vina_docked.pt`	liGAN
`ar_vina_docked.pt`	AR
`pocket2mol_vina_docked.pt`	Pocket2Mol
`targetdiff_vina_docked.pt`	TargetDiff

Official meta file download URL: https://drive.google.com/drive/folders/19imu-mlwrjnQhgbXpwsLgA17s1Rv70YS?usp=share_link

Evaluation command:

bash

export PYTHONPATH=../../../src:$PYTHONPATH
python scripts/evaluate_from_meta.py sampling_results/targetdiff_vina_docked.pt --result_path eval_targetdiff

Parameter descriptions:

Parameter	Required	Description
`meta_file`	Yes	`.pt` file containing sampling and docking results
`--result_path`	No	Evaluation result output directory, default `eval_results`

6. Diffusion Model Training

bash train_diffusion.sh

By default, this reads configs/training.yml, trains on the CrossDocked2020 pocket dataset, and saves logs and checkpoints to ./logs_diffusion/.

Custom configuration or parameter overrides:

bash

# Specify a custom configuration file
bash train_diffusion.sh configs/custom_training.yml

# Override training parameters from the command line
bash train_diffusion.sh --train.batch_size 8 --train.max_iters 500000

Main training parameters:

Parameter	Default value	Description
`data.path`	`${ONESCIENCE_DATASETS_DIR}/targetdiff/data/crossdocked_v1.1_rmsd1.0_pocket10`	Training data directory
`data.split`	`${ONESCIENCE_DATASETS_DIR}/targetdiff/data/crossdocked_pocket10_pose_split.pt`	Training/validation/test split file
`train.batch_size`	`4`	Batch size
`train.max_iters`	`10000000`	Maximum number of iterations
`train.lr`	`5.e-4`	Learning rate
`logdir`	`./logs_diffusion`	Log output directory

7. Data Preprocessing

7.1 CrossDocked2020 Data Preprocessing

To process CrossDocked2020 data from scratch, follow these steps:

Download CrossDocked2020 v1.1 and save it to data/CrossDocked2020.
Filter samples with RMSD < 1 Å:

bash
1export PYTHONPATH=../../../src:$PYTHONPATH 2python scripts/data_preparation/clean_crossdocked.py \ 3 --source data/CrossDocked2020 \ 4 --dest data/crossdocked_v1.1_rmsd1.0 \ 5 --rmsd_thr 1.0
Extract 10 Å binding pockets from proteins:

bash
1python scripts/data_preparation/extract_pockets.py \ 2 --source data/crossdocked_v1.1_rmsd1.0 \ 3 --dest data/crossdocked_v1.1_rmsd1.0_pocket10
Split the training and test sets:

bash
1python scripts/data_preparation/split_pl_dataset.py \ 2 --path data/crossdocked_v1.1_rmsd1.0_pocket10 \ 3 --dest data/crossdocked_pocket10_pose_split.pt \ 4 --fixed_split data/split_by_name.pt

7.2 PDBbind Data Preprocessing

The affinity prediction training script train_prop.sh automatically performs pocket extraction and dataset splitting. To run these steps separately, use the following commands:

bash

export PYTHONPATH=../../../src:$PYTHONPATH

python scripts/property_prediction/extract_pockets.py \
    --source data/pdbbind_v2020 \
    --dest data/pdbbind_v2020_processed \
    --subset refined \
    --num_workers 16

python scripts/property_prediction/pdbbind_split.py \
    --split_mode coreset \
    --index_path data/pdbbind_v2020_processed/pocket_10_refined/index.pkl \
    --test_path data/pdbbind_v2016/coreset \
    --save_path data/pdbbind_v2020_processed/pocket_10_refined/split.pt

Official OneScience Information

Platform	OneScience main repository	Skills repository
Gitee	https://gitee.com/onescience-ai/onescience	https://gitee.com/onescience-ai/oneskills
GitHub	https://github.com/onescience-ai/OneScience	https://github.com/onescience-ai/oneskills

Citation and License

The original TargetDiff code is licensed under the MIT License. This repository retains source attribution and is organized for OneScience Hugging Face automated runtime scenarios.
If you use TargetDiff results in research, we recommend citing the original TargetDiff paper and relevant OneScience project information. Depending on the actual task, also add citations for datasets or tools such as CrossDocked2020, PDBbind, RDKit, OpenBabel, and Vina/QVina.

bibtex

@inproceedings{guan3d,
  title={3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction},
  author={Guan, Jiaqi and Qian, Wesley Wei and Peng, Xingang and Su, Yufeng and Peng, Jian and Ma, Jianzhu},
  booktitle={International Conference on Learning Representations},
  year={2023}
}

OneScience-Group/TargetDiff

Model Overview

Model Description

Use Cases

Usage

1. Using OneCode

2. Manual Installation and Usage

Quick Start

1. Install the Runtime Environment

2. Download the Model

Training Weights and Datasets

3. Affinity Prediction

3.1 Affinity Prediction Training

3.2 Affinity Prediction Evaluation

3.3 Affinity Prediction Inference

4. Molecular Sampling

4.1 Single Test-Set Sample

4.2 Multi-GPU Batch Sampling

4.3 Sampling for a Custom PDB Pocket

5. Generated Molecule Evaluation

5.1 Evaluation from Sampling Results

5.2 Evaluation from Meta Files

6. Diffusion Model Training

7. Data Preprocessing

7.1 CrossDocked2020 Data Preprocessing

7.2 PDBbind Data Preprocessing

Official OneScience Information

Citation and License

OneScience-Group/
TargetDiff