PLAPT: Protein-Ligand Binding Affinity Prediction Using Pretrained Transformers
Tyler Rose, Nicolò Monti, N Koodagi Anand, Tianyu Shen · bioRxiv (Cold Spring Harbor Laboratory) · 2024
A bstract Predicting protein-ligand binding affinity is crucial for drug discovery, as it enables efficient identification of drug candidates. We introduce PLAPT, a novel model utilizing 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.