Documentation

Quick Start

Installation

pip install phdockui

# Or install from source
git clone https://github.com/DBD808/pHdockUI.git
cd pHdockUI
pip install -e .

Basic Usage

from phdockui import pHDocking

# Initialize the docking system
docker = pHDocking()

# Run pH-aware docking
results = docker.dock(
    ligand_smiles="CC(=O)Oc1ccccc1C(=O)O",  # Aspirin
    receptor_pdb="receptor.pdb",
    ph=7.4
)

# Access results
print(f"Best docking score: {results.best_score}")
print(f"Predicted pKa: {results.pka_values}")

Command Line Interface

# Basic docking
phdock --smiles "CC(=O)Oc1ccccc1C(=O)O" --receptor receptor.pdb --ph 7.4

# With advanced options
phdock --sdf ligand.sdf \
       --receptor receptor.pdb \
       --ph 6.5 \
       --conformers 50 \
       --ensemble-size 10 \
       --output results.json

API Reference

pHDocking()

Main class for pH-aware molecular docking

Methods:

  • dock(ligand, receptor, ph) - Run docking analysis
  • predict_pka(molecule) - Predict pKa values
  • generate_protonation_states(molecule, ph) - Generate states

ConformerGenerator

Generate 3D conformers for molecules

EnsembleModel

Machine learning ensemble for pKa prediction

Resources

GitHub Repository

Source code and issues

Test Dataset

Sample molecules

Frequently Asked Questions

What Python versions are supported?

pHdockUI supports Python 3.8 and above. We recommend using Python 3.9 or 3.10 for optimal performance.

How accurate are the pKa predictions?

Our ensemble model achieves ±0.5 pKa units RMSE on standard benchmarks, outperforming most commercial tools.

Can I use my own docking backend?

Yes! pHdockUI supports AutoDock Vina, Glide, and custom backends through our plugin interface.