Can I use this for protein-protein docking?

No, DiffDock is specifically designed for small molecule ligands. For protein-protein docking, you should use specialized tools like AlphaFold-Multimer.

What are the required inputs for docking?

You need a protein target (either a PDB file or an amino acid sequence) and a ligand description (SMILES string, SDF, or MOL2 file).

Does DiffDock predict binding affinity?

No, DiffDock predicts 3D binding poses and confidence scores. For binding affinity (ΔG), it is recommended to combine results with scoring functions like GNINA or MM/GBSA.

Do I need a GPU to run this skill?

A GPU is strongly recommended. While it can run on a CPU, a GPU provides a 10-100x speedup, which is necessary for batch processing and virtual screening.

How do I interpret the confidence scores?

Scores > 0 indicate high confidence, -1.5 to 0 are moderate, and < -1.5 are low confidence. Note that high confidence refers to the model's certainty in the pose, not necessarily the strength of the binding.

DiffDock Molecular Docking

Name: DiffDock Molecular Docking
Author: Activer007

byActiver007

Ciencia de Datos y ML

Performs state-of-the-art diffusion-based molecular docking to predict 3D binding poses of small molecule ligands to protein targets.

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This skill integrates DiffDock, a powerful deep learning tool, into the Claude environment to facilitate structure-based drug discovery. It allows users to predict high-accuracy binding poses using protein PDB files or sequences and ligand SMILES strings. Beyond single docking tasks, it supports batch processing for virtual screening and provides confidence scores to assess prediction reliability, making it an essential tool for medicinal chemists and computational biologists looking to streamline their drug design workflows and lead optimization tasks.

Características Principales

0 GitHub stars
Accepts diverse ligand formats including SMILES, SDF, and MOL2
Supports protein structures (PDB) and sequences (via ESMFold)
Batch processing capabilities for large-scale virtual screening
Predicts 3D ligand-protein binding poses using diffusion models
Generates confidence scores to evaluate prediction reliability

Casos de Uso

Predicting the binding site and orientation of a new drug candidate
Screening large compound libraries against a specific protein target
Visualizing structural interactions for lead optimization in drug discovery

Acerca de

Características Principales

0 GitHub stars
Accepts diverse ligand formats including SMILES, SDF, and MOL2
Supports protein structures (PDB) and sequences (via ESMFold)
Batch processing capabilities for large-scale virtual screening
Predicts 3D ligand-protein binding poses using diffusion models
Generates confidence scores to evaluate prediction reliability

Casos de Uso

Predicting the binding site and orientation of a new drug candidate
Screening large compound libraries against a specific protein target
Visualizing structural interactions for lead optimization in drug discovery