Predict 3D ligand binding poses to protein targets using diffusion models.
npx skills add https://github.com/robinbarvaag/poynt --skill diffdock-robinbarvaag
Please help me install this Agent Skill. Skill: diffdock Source: https://github.com/robinbarvaag/poynt/tree/main/.github/skills/diffdock Command: npx skills add https://github.com/robinbarvaag/poynt --skill diffdock-robinbarvaag
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 predict the binding pose of 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.
Predict protein-ligand binding poses by providing a protein structure (PDB) or sequence and a ligand input (SMILES, SDF, or MOL2). The Skill uses diffusion models to output 3D binding poses for structure-based drug design.
Molecular docking pose prediction requires protein structures in PDB format or as sequences, and ligands inputted as SMILES strings, SDF, or MOL2 files to accurately predict 3D binding poses.
Yes, you can screen large libraries of compounds for drug discovery by inputting a list of potential drug compounds as SMILES strings alongside your protein target to predict binding likelihood and sites.
Diffusion models work for small molecule docking by mathematically generating 3D binding poses of ligands to protein targets, facilitating virtual screening and chemical biology research without exhaustive physical simulations.
Pose prediction works with either a 3D protein structure in PDB format or a protein sequence. Providing the PDB structure or sequence alongside your SMILES ligand ensures accurate binding pose prediction.
The best way to identify potential drug candidates is structure-based drug design: supply a protein target and compound library to predict which molecules most likely bind and where they attach on the protein.
crazymsnPredict 3D protein-ligand binding poses with confidence scores via diffusion docking.
LeonChaoXPredict ranked 3D protein–ligand binding poses with confidence scores.
Estrella-231Predict ranked protein–ligand binding poses from PDB or SMILES inputs.
ovachieverPredict protein-ligand docking poses and confidence scores from PDB structures or sequences.
