Predicts 3D binding poses for protein-ligand complexes using state-of-the-art diffusion-based deep learning.
This skill integrates DiffDock into the Claude environment, providing a specialized interface for structure-based drug discovery and chemical biology. It allows researchers to automate the prediction of how small molecule ligands bind to protein targets, supporting both established PDB structures and amino acid sequences. Beyond simple docking, the skill facilitates batch virtual screening, result analysis with confidence scoring, and complex workflow automation, making it an essential tool for computational chemists and bioinformaticians using Claude for molecular modeling.
Key Features
01Generates confidence scores to assess prediction reliability
020 GitHub stars
03Predicts high-accuracy 3D binding poses using diffusion models
04Automates batch processing for large-scale virtual screening
05Handles diverse ligand inputs including SMILES, SDF, and MOL2
06Supports both PDB files and protein sequences via ESMFold
Use Cases
01Predicting binding sites and poses for novel protein-ligand complexes
02Structure-based drug design and lead optimization workflows
03Virtual screening of compound libraries against specific protein targets
