Predict protein-ligand binding poses and confidence scores from PDB files.
npx skills add https://github.com/yf8578/clawomics --skill diffdock-yf8578
Please help me install this Agent Skill. Skill: diffdock Source: https://github.com/yf8578/clawomics/tree/main/skills/diffdock Command: npx skills add https://github.com/yf8578/clawomics --skill diffdock-yf8578
This Skill automates the prediction of how small molecules (ligands) bind to proteins, a critical step in drug discovery and understanding biological interactions.
Use the diffdock skill to dock the ligand with SMILES 'CC(=O)Oc1ccccc1C(=O)O' to the protein structure 'protein.pdb'.
High-intent search queries and answers about installing and using this skill.
You can predict protein-ligand binding poses by using diffusion models to generate 3D structures of ligand-protein interactions directly from PDB files or protein sequences.
Yes, you can run virtual screening campaigns using batch processing to dock large libraries of small molecules against a protein active site and identify promising drug candidates.
Yes, full molecular docking functionality requires PyTorch with CUDA, RDKit, and ESM to run the underlying diffusion models for protein-ligand interaction prediction.
Confidence scoring assesses the reliability of predicted protein-ligand binding poses by providing scores that help evaluate the quality of the 3D ligand-protein interaction structures.
The best way to dock a specific ligand is to provide the SMILES string and the protein PDB file as inputs, allowing the diffusion model to predict their binding pose.
felixboehmPredict protein-ligand binding poses and confidence scores with diffusion models.
jaechang-hitsPredict protein-ligand binding poses from PDB and SMILES inputs.
omar-A-hassanGenerate per-residue and per-sequence embeddings with ESM-2 and predict structures via ESMFold.
ovachieverPredict protein-ligand docking poses and confidence scores from PDB structures or sequences.
