Predict protein-ligand binding poses and confidence scores from PDB files or SMILES.
npx skills add https://github.com/Yezez9/Research-Agent --skill diffdock-yezez9
Please help me install this Agent Skill. Skill: diffdock Source: https://github.com/Yezez9/Research-Agent/tree/main/scientific-skills/diffdock Command: npx skills add https://github.com/Yezez9/Research-Agent --skill diffdock-yezez9
This Skill automates the prediction of how small molecules (ligands) bind to proteins, a critical step in drug discovery and chemical biology, by leveraging advanced diffusion models.
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.
To predict protein-ligand binding poses for virtual screening, you provide a PDB file or protein sequence alongside a SMILES string for the ligand. The system outputs predicted 3D binding poses and confidence scores.
Yes, you can use a SMILES string as ligand input for molecular docking. You can also supply ligand structure files. The protein target requires a PDB file or an amino acid sequence to run the prediction.
Diffusion-based deep learning for structure-based drug design is an approach that predicts 3D binding poses by modeling molecular movements. It automates pose generation to assist in optimizing lead compounds and assessing structural feasibility.
Yes, this protein-ligand docking approach works without external dependencies. The environment is self-contained, allowing you to process PDB files and SMILES strings directly to generate binding poses and confidence scores.
The best way to screen compound libraries for drug discovery is using diffusion models to predict protein-ligand binding poses. This enables rapid virtual screening of large compound libraries to identify potential drug candidates.
crazymsnPredict 3D protein-ligand binding poses with confidence scores via diffusion docking.
galeepPredict 3D binding poses and confidence scores for protein-ligand interactions.
RamanEbrahimiPredicts 3D protein-ligand binding poses from PDB files and SMILES strings.
jimmc414Predicts protein-ligand binding poses and confidence scores via molecular docking.
