GenScore
# Model Overview GenScore is a **protein-ligand scoring framework** that can predict protein-small molecule binding affinity and evaluate docking pose quality. It demonstrates balanced scoring, ranking, docking, and virtual screening capabilities across multiple datasets. # Model Description GenScore is based on graph neural networks and extends RTMScore. Weights and datasets are not provided at the moment. They will be uploaded to Hugging Face soon, and command-line downloads will be supported later. # Use Cases | Use case | Description | | :---: | :---: | | Protein-ligand scoring | Predicts binding scores for a given protein, or an extracted binding pocket, and ligand conformation. | | Automatic pocket generation | Automatically extracts the binding pocket from a full protein structure based on the reference ligand position. | | Contribution analysis | Outputs atom-level and residue-level contributions to the final score for interpretability analysis. | | Model training | Trains the GenScore scoring network using preprocessed PDBbind protein-ligand graph data. | | CASF-2016 benchmark evaluation | Supports three standard tests: scoring/ranking, docking, and virtual screening. | # Usage ## 1. Using OneCode You can try intelligent one-click AI4S programming through the OneCode online environment: [Try intelligent one-click AI4S programming](https://web-2069360198568017922-iaaj.ksai.scnet.cn:58043/home) ## 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** DCU users who want to learn more about adaptation details can contact liubiao@sugon.com. **Environment Checks** - NVIDIA GPU: ```bash nvidia-smi ``` - Hygon DCU: ```bash 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:$LD_LIBRARY_PATH" ``` ### 2. Download the Model Package ```bash # By default, the package is downloaded to the GenScore folder under the current path. To change this, adjust the path after local_dir. hf download --model OneScience-Sugon/GenScore --local-dir ./model cd model ``` ### Training Weights and Datasets Weights and datasets are not provided at the moment. They will be uploaded to Hugging Face soon, and command-line downloads will be supported later. ### 3. Usage Modes #### 1. Protein-Ligand Scoring Inference (`run_genscore.sh`) ```bash cd ./scripts bash run_genscore.sh ``` This script runs four inference examples in sequence, covering common use cases: 1. **GT model + automatic pocket generation**: inputs a full protein, a reference ligand, and a decoy ligand, then automatically generates the binding pocket and scores it. 2. **GatedGCN model + pre-extracted pocket**: inputs a pre-extracted pocket PDB and a decoy ligand for scoring. 3. **Atom contribution analysis**: uses the GatedGCN model to compute each atom's contribution to the score. 4. **Residue contribution analysis**: uses the GatedGCN model to compute each residue's contribution to the score. #### 2. Model Training ##### 2.1 Small-Scale Smoke Test (`train_genscore_smoke.sh`) ```bash cd ./scripts bash train_genscore_smoke.sh ``` Default configuration: | Parameter | Default value | |------|--------| | Number of training epochs | 100 | | Batch size | 16 | | Number of validation samples | 1500 | | Encoder | `gatedgcn` | | Output model | `genscore_smoke_bs16.pth` | ##### 2.2 Full Training (`train_genscore_full.sh`) ```bash cd ./scripts bash train_genscore_full.sh ``` Key parameters can be overridden through environment variables: | Environment variable | Description | Default value | |----------|------|--------| | `GENSCORE_DATA_DIR` | Training data directory | `${ONESCIENCE_DATASETS_DIR}/GenScore/genscore_data/rtmscore_s` | | `GENSCORE_DATA_PREFIX` | Data file prefix | `v2020_train` | | `GENSCORE_ENCODER` | Graph encoder type | `gatedgcn` | | `GENSCORE_MODEL_PATH` | Output model path | `examples/biosciences/genscore/genscore_${GENSCORE_ENCODER}_full_3000.pth` | | `GENSCORE_NUM_EPOCHS` | Number of training epochs | `3000` | | `GENSCORE_BATCH_SIZE` | Batch size | `64` | | `GENSCORE_NUM_WORKERS` | Number of data loading workers | `8` | | `GENSCORE_VALNUM` | Number of validation samples | `1500` | | `GENSCORE_PATIENCE` | Early-stopping patience | `70` | --- #### 3. CASF-2016 Benchmark Evaluation (`run_genscore_benchmarks.sh`) ```bash cd ./scripts bash run_genscore_benchmarks.sh all ``` You can also run a single evaluation task: ```bash bash run_genscore_benchmarks.sh scoring bash run_genscore_benchmarks.sh docking bash run_genscore_benchmarks.sh screening ``` Evaluation task descriptions: | Task | Description | |------|------| | `scoring` | Scoring capability evaluation (scoring/ranking) | | `docking` | Docking capability evaluation (docking power) | | `screening` | Virtual screening capability evaluation (screening power) | ### Data Preprocessing Training requires preprocessed PDBbind graph data, including: ```text