Can I use this for large-scale virtual screening?

Yes, the skill supports batch processing via CSV inputs and provides scripts for pre-computing protein embeddings to accelerate large screening campaigns.

How do I interpret the DiffDock confidence scores?

Scores above 0 indicate high confidence, scores between -1.5 and 0 are moderate, and scores below -1.5 suggest the prediction is uncertain and requires careful validation.

Is a GPU required to run this skill?

A GPU is strongly recommended for practical use, as it typically provides a 10-100x speedup compared to running docking simulations on a CPU.

Does DiffDock predict binding affinity?

No, DiffDock predicts the 3D binding pose and its own confidence in that structure. For affinity estimation (ΔG), it should be paired with scoring tools like GNINA or MM/GBSA.

What inputs does the DiffDock skill require?

You can provide protein structures as PDB files or amino acid sequences, and ligands as SMILES strings or structure files such as SDF or MOL2.

DiffDock Molecular Docking

Name: DiffDock Molecular Docking
Author: jimmc414

byjimmc414

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324

Ciencia de Datos y ML

Predicts accurate 3D protein-ligand binding poses using diffusion-based deep learning for computational drug discovery.

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DiffDock is a specialized scientific AI skill that integrates diffusion-based deep learning into Claude Code to perform state-of-the-art molecular docking. It predicts the 3D orientation and conformation of small molecule ligands within protein targets using inputs such as PDB files, SMILES strings, or amino acid sequences. Designed for researchers in chemical biology and drug design, this skill automates complex workflows including single-complex docking, batch virtual screening, and detailed result analysis with confidence scoring, bridging the gap between raw genomic data and structural insights.

Características Principales

Enables automated batch processing for large-scale virtual screening
Predicts high-accuracy 3D ligand binding poses using diffusion models
Supports PDB structures, SMILES strings, and amino acid sequences
Provides confidence scores to assess prediction reliability and plausibility
Integrates with scoring functions like GNINA for affinity assessment
324 GitHub stars

Casos de Uso

Automating the preparation and analysis of molecular docking ensembles
Screening a library of SMILES-encoded compounds against a protein sequence
Identifying the optimal binding pose for a new drug candidate on a target protein

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Características Principales

Enables automated batch processing for large-scale virtual screening
Predicts high-accuracy 3D ligand binding poses using diffusion models
Supports PDB structures, SMILES strings, and amino acid sequences
Provides confidence scores to assess prediction reliability and plausibility
Integrates with scoring functions like GNINA for affinity assessment
324 GitHub stars

Casos de Uso

Automating the preparation and analysis of molecular docking ensembles
Screening a library of SMILES-encoded compounds against a protein sequence
Identifying the optimal binding pose for a new drug candidate on a target protein