Is DiffDock suitable for protein-protein docking?

No, DiffDock is optimized for small molecule ligands. For protein-protein docking, you should use specialized tools such as DiffDock-PP or AlphaFold-Multimer.

Does it support protein sequences if I don't have a PDB file?

Yes, DiffDock can handle amino acid sequences by utilizing ESMFold to generate the protein structure before performing the docking simulation.

Can DiffDock predict binding affinity?

No, DiffDock is specifically designed to predict 3D binding poses and confidence scores. To estimate binding affinity (ΔG), you should combine DiffDock results with scoring functions like GNINA or MM/GBSA.

What hardware is required to run DiffDock efficiently?

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

DiffDock Molecular Docking

Name: DiffDock Molecular Docking
Author: Microck

byMicrock

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Data Science & ML

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

About

DiffDock is a state-of-the-art computational biology tool designed for molecular docking, allowing researchers to predict the precise 3D orientation of small molecule ligands within protein binding sites. By utilizing diffusion models, it offers superior accuracy compared to traditional docking methods and provides automated confidence scores to help prioritize results. It is an essential asset for structure-based drug design, virtual screening of chemical libraries, and lead optimization, supporting various input formats including PDB files, protein sequences, SMILES strings, and SDF files.

Key Features

Compatibility with diverse ligand formats including SMILES, SDF, and MOL2
Batch processing for large-scale virtual screening campaigns
Support for protein sequences (via ESMFold integration) and PDB structures
Automated confidence scoring to assess prediction reliability
81 GitHub stars
High-accuracy 3D binding pose prediction using generative diffusion models

Use Cases

Predicting the binding orientation of a drug candidate to a target protein
Conducting virtual screening to identify lead compounds from chemical libraries
Optimizing molecular leads by analyzing 3D structural interactions in the binding pocket

About

Key Features

Compatibility with diverse ligand formats including SMILES, SDF, and MOL2
Batch processing for large-scale virtual screening campaigns
Support for protein sequences (via ESMFold integration) and PDB structures
Automated confidence scoring to assess prediction reliability
81 GitHub stars
High-accuracy 3D binding pose prediction using generative diffusion models

Use Cases

Predicting the binding orientation of a drug candidate to a target protein
Conducting virtual screening to identify lead compounds from chemical libraries
Optimizing molecular leads by analyzing 3D structural interactions in the binding pocket