https://github.com/d-f/binding-affinity
Protein-Ligand binding affinity prediction with Protein LLMs and Graph Attention Networks.
https://github.com/d-f/binding-affinity
binding-affinity binding-affinity-prediction graph-attention-networks pdb-dataset protein-llm pytorch
Last synced: 27 days ago
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Protein-Ligand binding affinity prediction with Protein LLMs and Graph Attention Networks.
- Host: GitHub
- URL: https://github.com/d-f/binding-affinity
- Owner: d-f
- Created: 2025-03-20T17:33:38.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-04-27T22:50:16.000Z (about 1 year ago)
- Last Synced: 2025-04-27T23:24:44.648Z (about 1 year ago)
- Topics: binding-affinity, binding-affinity-prediction, graph-attention-networks, pdb-dataset, protein-llm, pytorch
- Language: Python
- Homepage:
- Size: 24.4 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
To determine the mean and standard deviation of dataset features:
```
python determine_affinity_stats.py -save_filepath /affinity/ds_stats.json
```
To partition the dataset into training, validation and test partitions:
```
python partition_dataset.py -data_folder /affinity/processed/ -save_folder /affinity/
```
To embed the dataset proteins with ProtBERT and dataset ligands with ChemBERTa:
```
python process_plapt_ds.py -stat_json_path /affinity/ds_stats.json -save_dir /affinity/processed/
```
To train a PLAPT model to predict binding affinity:
```
python train_plapt.py -data_folder /affinity/processed/ -csv_folder /affinity/csv/ -batch_size 64 -lr 1e-3 -num_epochs 64 -result_folder /affinity/models/ -model_save_name model_1.pth.tar -patience 5 -prot_hidden 512 -lig_hidden 512
```