Interformer: An Interaction-Aware Model for Protein-Ligand Docking and Affinity Prediction

The code, dataset, and model weights are described in the paper "Interformer: An Interaction-Aware Model for Protein-Ligand Docking and Affinity Prediction."

experiment_results.zip: Contains generated results that can reproduce the result from the reported paper.

benchmark.zip: Contains docking and affinity input data of the interformer. You can use the source code to make predictions and reproduce the number of the reported paper.

checkpoints.zip: Contains one weight for the Energy and four PoseScore and Affinity models.

source_code_1.0.zip: Contains the initial version of the source code.

interformer_train.tar.gz: Contains prepared training data for interformer. poses/ contains all structure need for training, poses/ligand contains the re-docking poses generated by interformer energy, poses/ligand/rcsb contains the conformation of reference ligand, poses/pocket contains all pocket extract by raw PDB from rcsb, poses/uff contains all ligand conformation minimized using UFF from reference ligand, and train/ contains the training csv.

baseline_results.tar.gz:  Contains the predictions from three methods: Interformer, DiffDock, and DeepDock. The results align with the exact numbers reported in the paper. For further details, please refer to the eda/ directory.

You can also find the newest version of the source code at https://github.com/tencent-ailab/Interformer