[R] PLAPT: Protein Ligand Binding Affinity Prediction using Pretrained Transformers

Introducing PLAPT: Protein Ligand Binding Affinity Prediction using Pretrained Transformers.

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, …

accuracy discovery drug discovery identification machinelearning novel prediction protein transfer transfer learning transformers