Can I use this for virtual screening?

Yes, DiffDock supports batch processing. For large libraries, you can pre-compute protein embeddings to significantly accelerate the process.

How do I interpret the confidence scores?

Scores above 0 indicate high confidence, scores between -1.5 and 0 are moderate, and scores below -1.5 are considered low confidence.

Does DiffDock predict binding affinity?

No, DiffDock predicts 3D binding poses and confidence scores. It should be paired with scoring functions like GNINA or MM/GBSA for affinity assessment.

What inputs does DiffDock require?

You need either a protein structure (PDB file) or a sequence, and a ligand description such as a SMILES string or a structure file (SDF/MOL2).

Is a GPU required for this skill?

While it can run on a CPU, a GPU is strongly recommended as it provides a 10-100x speedup, making practical docking tasks feasible.

DiffDock Molecular Docking

Name: DiffDock Molecular Docking
Author: lifangda

bylifangda

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データサイエンスとML

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

概要

DiffDock is a state-of-the-art computational tool for structure-based drug design that leverages diffusion models to determine how small molecule ligands bind to protein targets. It allows researchers to input protein structures or sequences alongside ligand SMILES to generate ranked 3D conformations with associated confidence scores. Ideal for virtual screening and lead optimization, this skill streamlines the complex setup and execution of molecular docking workflows within the Claude environment, enabling rapid hypothesis testing in chemical biology and drug discovery without requiring manual environment configuration.

主な機能

Batch processing capabilities for large-scale virtual screening campaigns
Accurate 3D binding pose prediction using diffusion-based deep learning
Support for protein structures (PDB) and amino acid sequences (ESMFold)
16 GitHub stars
Seamless handling of diverse ligand formats including SMILES, SDF, and MOL2
Automated confidence scoring to assess prediction reliability

ユースケース

Predicting the binding site and orientation of a novel drug candidate
Virtual screening of compound libraries against a specific protein target
Lead optimization by analyzing structural interactions of ligand derivatives

概要

主な機能

Batch processing capabilities for large-scale virtual screening campaigns
Accurate 3D binding pose prediction using diffusion-based deep learning
Support for protein structures (PDB) and amino acid sequences (ESMFold)
16 GitHub stars
Seamless handling of diverse ligand formats including SMILES, SDF, and MOL2
Automated confidence scoring to assess prediction reliability

ユースケース

Predicting the binding site and orientation of a novel drug candidate
Virtual screening of compound libraries against a specific protein target
Lead optimization by analyzing structural interactions of ligand derivatives