What input formats does DiffDock accept for ligands?

DiffDock is highly flexible, accepting SMILES strings directly as well as structure files in SDF or MOL2 formats.

Does DiffDock predict binding affinity?

No, DiffDock predicts the 3D binding pose and a confidence score for that pose. You should combine it with scoring functions like GNINA or MM/GBSA for binding affinity (ΔG) assessment.

Is DiffDock suitable for protein-protein docking?

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

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. Remember that confidence reflects structural certainty, not binding strength.

Can I run DiffDock on a CPU?

While DiffDock can run on a CPU, a GPU is strongly recommended as it provides a 10-100x speedup, which is essential for practical research and batch processing.

DiffDock Molecular Docking

Name: DiffDock Molecular Docking
Author: x-cmd

byx-cmd

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数据科学与机器学习

Predicts accurate protein-ligand binding poses and 3D structures using state-of-the-art diffusion-based deep learning models.

关于

DiffDock is a specialized AI skill designed for computational chemists and researchers to perform high-fidelity molecular docking within their coding environment. By leveraging diffusion models, it predicts the 3D orientations of small molecules within protein binding sites, providing both structural coordinates and confidence rankings. It is an essential tool for structure-based drug design, virtual screening of chemical libraries, and understanding molecular interactions, offering a significant performance leap over traditional search-based docking methods by supporting both PDB structures and raw amino acid sequences.

主要功能

Predicts 3D ligand binding poses using diffusion-based deep learning
Batch processes multiple complexes for large-scale virtual screening
Supports both protein structures (PDB) and amino acid sequences (ESMFold)
Generates confidence scores to assess structural prediction reliability
Handles diverse ligand inputs via SMILES strings, SDF, or MOL2 formats
8 GitHub stars

使用场景

Structure-based drug discovery and lead optimization workflows
High-throughput virtual screening of compound libraries against targets
Predicting binding sites and poses for novel protein-ligand complexes

关于

主要功能

Predicts 3D ligand binding poses using diffusion-based deep learning
Batch processes multiple complexes for large-scale virtual screening
Supports both protein structures (PDB) and amino acid sequences (ESMFold)
Generates confidence scores to assess structural prediction reliability
Handles diverse ligand inputs via SMILES strings, SDF, or MOL2 formats
8 GitHub stars

使用场景

Structure-based drug discovery and lead optimization workflows
High-throughput virtual screening of compound libraries against targets
Predicting binding sites and poses for novel protein-ligand complexes