Bindwell
/

PLAPT_V1

@@ -12,7 +12,7 @@ This is the official code repository for PLAPT, a state-of-the-art protein-ligan
 ### Abstract
 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.
-![PLAPT Architecture](https://huggingface.co/Navvye/PLAPT/PLAPT.png)
 ---
 # Usage

 ### Abstract
 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.
+![PLAPT Architecture](PLAPT.png)
 ---
 # Usage