Can I use amino acid sequences if I don't have a PDB file?

Yes, this skill supports protein sequences by utilizing ESMFold to generate structural embeddings and predictions necessary for the docking process.

What does a high confidence score indicate in DiffDock?

A score above 0 indicates high confidence in the predicted 3D structure and pose, but it does not necessarily mean the molecule has a high binding affinity.

Does DiffDock predict binding affinity?

No, DiffDock is specifically designed to predict 3D binding poses and confidence levels. For binding affinity (ΔG or Kd), you should combine DiffDock's output with scoring functions like GNINA or MM/GBSA.

What is the recommended hardware for this skill?

A GPU is strongly recommended. While it can run on a CPU, using a GPU typically results in a 10-100x speedup, which is essential for virtual screening or batch tasks.

DiffDock Molecular Docking

Name: DiffDock Molecular Docking
Author: Activer007

byActiver007

数据科学与机器学习

Predicts 3D binding poses for protein-ligand complexes using state-of-the-art diffusion-based deep learning.

关于

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.

主要功能

Generates confidence scores to assess prediction reliability
0 GitHub stars
Predicts high-accuracy 3D binding poses using diffusion models
Automates batch processing for large-scale virtual screening
Handles diverse ligand inputs including SMILES, SDF, and MOL2
Supports both PDB files and protein sequences via ESMFold

使用场景

Predicting binding sites and poses for novel protein-ligand complexes
Structure-based drug design and lead optimization workflows
Virtual screening of compound libraries against specific protein targets

关于

主要功能

Generates confidence scores to assess prediction reliability
0 GitHub stars
Predicts high-accuracy 3D binding poses using diffusion models
Automates batch processing for large-scale virtual screening
Handles diverse ligand inputs including SMILES, SDF, and MOL2
Supports both PDB files and protein sequences via ESMFold

使用场景

Predicting binding sites and poses for novel protein-ligand complexes
Structure-based drug design and lead optimization workflows
Virtual screening of compound libraries against specific protein targets