Protein–ligand affinity prediction
E-CloudBind
Electron-density point clouds and molecular graphs for protein–ligand affinity prediction, with model code, pretrained weights and example complexes.
What you can use
E-CloudBind combines electron-density point clouds and molecular graphs to predict protein–ligand binding affinity. The public repository provides the model implementation, a pretrained weight file, five preprocessed protein–ligand examples and a workflow for generating input features.
Use the original repository for code and instructions. The Nature Communications article also links processed research data on figshare and a fixed code archive on Zenodo.
Start with the provided examples
- Read the environment requirements. The repository lists its Python packages and GPU dependencies. Its current example entry point selects an NVIDIA CUDA device.
- Inspect an example complex. The five prepared cases show how molecular graphs and point clouds are organized for the model.
- Follow the repository’s inference demo. The example loads the provided weights and reports evaluation metrics for the labeled examples.
- Read the preprocessing workflow before using new structures. Feature generation includes coordinate preparation, ligand electron-density calculations, point-cloud processing and graph construction.
The supplied demo is an evaluation example. Its reported RMSE and correlation describe those example cases; they are not performance estimates for an arbitrary new dataset.
Read the example script · Find the pretrained weights
Preparing a new input
The documented workflow starts with a protein PDB file and a ligand MOL2 file. It requires three-dimensional structural input, with the protein and ligand prepared in the appropriate coordinate context. A protein sequence and a SMILES string alone are not the documented input to the supplied demo.
Ligand electron-density generation uses xTB and Multiwfn. Protein-pocket point clouds use the representation described in the paper. Review each preprocessing step and its file conventions before extending the examples to a new complex.
The current demo reads known labels to calculate evaluation metrics. To predict unlabeled complexes, an inference workflow must retain the model predictions as per-complex outputs instead of requiring reference labels for evaluation.
Explore the preprocessing scripts
Data, versions and citation
The article’s data statement links the processed training/validation and test data. Follow its references for the underlying datasets and their usage conditions. Cite the paper for the method and record the code version used for a reproducible analysis.
The GitHub repository contains the working project; the Zenodo DOI identifies the archived code release. This page provides an entry into those original resources.