bio-diffdock

Predict protein-ligand binding poses by using diffusion models to calculate the 3D molecular docking interactions. This approach automates structure-based drug design and virtual screening by processing PDB files or protein sequences to generate accurate ligand poses and confidence scores.

Yes, you can run virtual screening by inputting a target protein structure as a PDB file alongside a library of potential drug molecules. The system processes these inputs to predict the binding pose of each molecule, identifying the most promising candidates for further study.

Yes, predicting molecular docking poses requires Python, PyTorch with CUDA, RDKit, and ESM. These dependencies are necessary to run the diffusion models for accurate pose generation and to calculate binding confidence scores.

When you only have a protein sequence, you can still predict molecular docking poses for drug candidate screening. The system utilizes ESM to process protein sequences directly, enabling binding site prediction and ligand interaction analysis without a complete PDB structure.

Diffusion model-based molecular docking supports cheminformatics workflows by predicting 3D protein-ligand binding poses and calculating confidence scores. It integrates with cheminformatics processes like virtual screening and lead compound optimization to identify potential drug candidates.