Does DiffDock predict binding affinity?

No, DiffDock is specifically designed to predict 3D binding poses and provide confidence scores for those poses. It does not predict binding affinity (ΔG or Kd); for affinity assessment, results should be paired with scoring functions like GNINA or MM/GBSA.

What ligand formats are supported?

DiffDock supports a variety of ligand inputs including SMILES strings, SDF files, and MOL2 files.

How do I interpret the confidence scores?

Scores above 0 indicate high confidence in the predicted pose. Scores between -1.5 and 0 are considered moderate confidence, while scores below -1.5 are low confidence and require careful manual validation.

Can I run DiffDock on a CPU?

While DiffDock can run on a CPU, a GPU is strongly recommended. Using a GPU typically results in a 10-100x speedup, which is essential for batch processing and virtual screening tasks.

Can I use amino acid sequences instead of PDB files?

Yes, DiffDock can utilize protein sequences by leveraging ESMFold to generate the necessary structural embeddings for the docking process.

DiffDock Molecular Docking

Name: DiffDock Molecular Docking
Author: Activer007

byActiver007

Ciencia de Datos y ML

Predicts high-accuracy 3D protein-ligand binding poses using diffusion-based deep learning for structure-based drug discovery.

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DiffDock is a state-of-the-art molecular docking tool that leverages diffusion models to predict the 3D orientation of small molecule ligands within protein binding sites. It supports protein structures via PDB files or sequences (using ESMFold) and handles diverse ligand formats like SMILES and SDF. This skill is ideal for researchers and computational biologists performing virtual screening, lead optimization, or structural analysis who need reliable binding pose predictions accompanied by statistical confidence scores to guide drug design efforts.

Características Principales

Batch processing capabilities for large-scale virtual screening campaigns
0 GitHub stars
Built-in analysis tools for ranking and filtering docking results
Support for both PDB protein structures and amino acid sequences via ESMFold
Automated confidence scoring to assess prediction reliability
Diffusion-based deep learning for highly accurate 3D binding pose prediction

Casos de Uso

Structure-based drug design and lead compound optimization
Predicting binding sites for novel proteins or ligands with unknown poses
Virtual screening of large chemical libraries against specific protein targets

Acerca de

Características Principales

Batch processing capabilities for large-scale virtual screening campaigns
0 GitHub stars
Built-in analysis tools for ranking and filtering docking results
Support for both PDB protein structures and amino acid sequences via ESMFold
Automated confidence scoring to assess prediction reliability
Diffusion-based deep learning for highly accurate 3D binding pose prediction

Casos de Uso

Structure-based drug design and lead compound optimization
Predicting binding sites for novel proteins or ligands with unknown poses
Virtual screening of large chemical libraries against specific protein targets