Papers › PLAPT: Protein-Ligand Binding Affinity Prediction Using Pretrained Transformers
PLAPT: Protein-Ligand Binding Affinity Prediction Using Pretrained Transformers
Tyler Rose, Nicolo Monti, Navvye Anand, Tianyu Shen
Understanding protein-ligand binding affinity is crucial for drug discovery, enabling the identification of promising drug candidates efficiently. We introduce PLAPT, a novel model leveraging transfer learning from pre-trained transformers like ProtBERT and ChemBERTa to predict binding affinities with high accuracy. Our method processes one-dimensional protein and ligand sequences, leveraging a branching neural network architecture for feature integration and affinity estimation. We demonstrate PLAPT's superior performance through validation on multiple datasets, achieving state-of-the-art results while requiring significantly less computational resources for training compared to existing models. Our findings indicate that PLAPT offers a highly effective and accessible approach for accelerating drug discovery efforts.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Protein-Ligand Affinity Prediction | CSAR-HiQ | PLAPT | RMSE | 1.349 | #2 of 3 | Archive leaderboard | report |
| Protein-Ligand Affinity Prediction | PDBbind | PLAPT | RMSE | 1.211 | #2 of 7 | Archive leaderboard | report |
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