How do I interpret DiffDock confidence scores?

Scores above 0 indicate high confidence, scores between -1.5 and 0 are moderate, and scores below -1.5 represent low certainty in the predicted pose.

Does DiffDock predict binding affinity?

No, DiffDock predicts 3D binding poses and confidence scores, but not binding affinity (ΔG). It should be combined with scoring functions like GNINA or MM/GBSA for affinity assessment.

What inputs does the DiffDock skill require?

You need a protein source (either a PDB file or an amino acid sequence) and a ligand description (either a SMILES string or a structure file like SDF/MOL2).

Can I run DiffDock on a CPU?

While it can run on a CPU, a GPU is strongly recommended for practical use as it provides a 10-100x speedup, especially for batch virtual screening.

Is this skill suitable for protein-protein docking?

No, DiffDock is specifically optimized for small molecule ligands. For protein-protein interactions, tools like AlphaFold-Multimer or DiffDock-PP are recommended.

DiffDock Molecular Docking

Name: DiffDock Molecular Docking
Author: Activer007

byActiver007

データサイエンスとML

Predicts high-accuracy 3D protein-ligand binding poses using diffusion-based deep learning models.

概要

DiffDock is a specialized skill for structure-based drug discovery that enables Claude to perform state-of-the-art molecular docking. It leverages diffusion models to predict the 3D orientations of small molecule ligands within protein binding sites, supporting inputs from PDB files, SMILES strings, and protein sequences via ESMFold. This tool is ideal for researchers and computational chemists looking to conduct virtual screening, analyze binding sites, and generate confidence-ranked structural models for lead optimization. While it excels at structural prediction, it is designed to be used alongside scoring functions for affinity assessment.

主な機能

Predicts 3D ligand binding poses with state-of-the-art deep learning accuracy
Supports diverse inputs including PDB files, SMILES strings, and protein sequences
Generates statistical confidence scores to rank prediction reliability
0 GitHub stars
Enables large-scale batch processing for virtual screening campaigns
Integrates with ESMFold for docking even when protein structures are unavailable

ユースケース

Identifying potential binding sites and evaluating structural complementarity
Predicting binding orientations for novel small molecules in drug design
Virtual screening of compound libraries against specific protein targets

概要

主な機能

Predicts 3D ligand binding poses with state-of-the-art deep learning accuracy
Supports diverse inputs including PDB files, SMILES strings, and protein sequences
Generates statistical confidence scores to rank prediction reliability
0 GitHub stars
Enables large-scale batch processing for virtual screening campaigns
Integrates with ESMFold for docking even when protein structures are unavailable

ユースケース

Identifying potential binding sites and evaluating structural complementarity
Predicting binding orientations for novel small molecules in drug design
Virtual screening of compound libraries against specific protein targets