Generate protein-ligand binding poses with confidence scores via diffusion.
npx skills add https://github.com/JosephWoodall/noosphere --skill diffdock-josephwoodall
Please help me install this Agent Skill. Skill: diffdock Source: https://github.com/JosephWoodall/noosphere/tree/main/.agent/skills/diffdock Command: npx skills add https://github.com/JosephWoodall/noosphere --skill diffdock-josephwoodall
DiffDock generates protein-ligand binding poses with accompanying confidence scores.
Run a GPU-enabled docking job by providing a protein (PDB or sequence) and a ligand (SMILES or file) to generate and inspect the top poses with confidence scores.
High-intent search queries and answers about installing and using this skill.
Protein-ligand binding pose prediction is done by providing a protein PDB structure and a ligand SMILES string. DiffDock uses diffusion-based generation to output 3D binding poses with accompanying confidence scores for screening and design.
Batch virtual screening is supported by preparing a CSV file containing multiple ligand SMILES strings. The batch docking workflow processes the ligands against a single protein target and generates top poses with confidence scores for structure-based drug design.
Sequence-based ensemble docking works by using ESMFold to generate a 3D protein structure directly from the input sequence. The workflow then proceeds with diffusion-based pose generation to predict ligand binding poses and confidence scores.
A GPU-enabled environment is required to run diffusion-based pose generation efficiently. The workflow relies on PyTorch Lightning and Torch to execute the computational diffusion models and generate 3D binding poses with confidence scores.
Analyzing confidence scores from generated docking poses is done using provided utility scripts for results analysis. The scripts process the output files to evaluate per-pose confidence scores, enabling you to filter and rank candidates for downstream rescoring.
Yezez9Predict protein-ligand binding poses and confidence scores from PDB files or SMILES.
crazymsnPredict 3D protein-ligand binding poses with confidence scores via diffusion docking.
AlterLab-IEUPredict protein-ligand binding poses and confidence scores via diffusion-model docking.
silversteinPredict ranked protein–ligand binding poses with confidence scores from PDB structures or sequences and SMILES inputs.
