What is LigandExplorer

LigandExplorer is a workflow that combines cheminformatics tools and machine learning methods to automatically extract and classify ligands from PDB structures. It applies graph theory to identify covalent and non-covalent ligands based on molecular connectivity and uses machine learning models to filter out irrelevant molecules, ensuring that only biologically significant ligands are retained.

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V2.0 Update

Graph Neural Network (GNN) Backend

V2.0 introduces a new GNN-based classification backend, replacing the previous LightGBM models as the default. The GNN models learn molecular features end-to-end directly from raw atomic properties (atomic number, 3D coordinates, residue boundaries), eliminating the need for hand-crafted features.

Two GNN models are included:

• Molecule Classifier : 8-class classification — peptide, glycan, RNA, DNA, lipid, ion, organic, and cyclic peptide (new). Built on a SchNet. Test accuracy: 99.66%.
• Ligand Relevance Classifier : Binary classification — determines whether a molecule is a biologically relevant ligand. Uses pocket-aware graph construction and dual-channel (ligand + protein) pooling. Test accuracy: 97.61%, AUC: 99.41%.

Cyclic Peptide Support

V2.0 adds cyclic peptide as a new molecular category. Cyclic peptides are now correctly identified and classified rather than being grouped with linear peptides.

Backend Selection

Users can choose between the GNN and legacy LightGBM backends via the --backend flag:

# Use GNN backend (default)
ligandexplorer -i input.zip -o output/

# Use legacy LightGBM backend (7-class, no cyclic peptide support)
ligandexplorer -i input.zip -o output/ --backend lgbm

GPU Acceleration

When using the GNN backend, GPU acceleration is available via the --device flag:

# Run on CPU (default)
ligandexplorer -i input.zip -o output/

# Run on GPU
ligandexplorer -i input.zip -o output/ --device cuda

Additional Dependencies

The GNN backend requires PyTorch and PyTorch Geometric:

pip install torch torch_geometric

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Core Feature

• Automated Ligand Extraction: Automatically identifies and extracts ligands from PDB files.
• Intelligent Classification: Uses GNN models to accurately classify molecules into ions, solvents, nucleic acids, peptides, cyclic peptides, or biologically active ligands.
• Graph Theory Application: Distinguishes between covalent and non-covalent ligands based on molecular connectivity using graph theory algorithms.
• High-Throughput Processing: Supports multi-core parallel processing for rapid handling of large PDB datasets.
• Flexible Customization: Offers various command-line options for users to tailor the workflow to their specific needs.

Installation Guide

1. Prerequisites

   Before installing, please ensure you have the necessary dependencies. You can install them using pip:

    pip install numpy biopython networkx scikit-learn torch torch_geometric
   

   If you need the legacy LightGBM backend:

    pip install lightGBM
   

2. Clone the Repository

   Clone the latest version of the code to your local machine using Git:

    git clone https://github.com/dptech-corp/ligandexplorer.git
    cd ligandexplorer
   

3. Run the install script

   Use the Python setup script to install LigandExplorer and its dependencies:

    python setup.py install
   

Quick Start

Once installed, you can run the program using the ligandexplorer command.

Use the -h or --help flag to view all available options:

ligandexplorer -h

Example

Here is a basic usage example:

# Basic usage (GNN backend, CPU)
ligandexplorer -i /path/to/your/input.zip -o /path/to/your/output_directory

# With GPU acceleration
ligandexplorer -i /path/to/your/input.zip -o /path/to/your/output_directory --device cuda

# Using legacy LightGBM backend
ligandexplorer -i /path/to/your/input.zip -o /path/to/your/output_directory --backend lgbm

This command will process all PDB structures within input.zip and save the extracted and classified ligand information to the output_directory.

Command-Line Options

usage: ligandexplorer [-h] -o OUTPUT_DIR -i INPUT_ZIP [-f FIX_PDB_FILE] [-b BOX_SIZE] [-c CORE]
                      [-s STRICT_MODE] [-l LIG_IDENTIFY] [-e SILENCE_MODE] [-t DEBUG]
                      [-m {gnn,lgbm}] [-d {cpu,cuda}]

Ligand explorer, version: v2.0

options:
  -h, --help            show this help message and exit
  -o OUTPUT_DIR, --output_dir OUTPUT_DIR
                        defined the output path
  -i INPUT_ZIP, --input_zip INPUT_ZIP
                        Path of the input zip file (rar, zip, tar, tar.gz, tar.bz2, bz2)
  -f FIX_PDB_FILE, --fix_pdb_file FIX_PDB_FILE
                        Fix missing main chain atom by PDBFixer (if PDBFixer is install)
  -b BOX_SIZE, --box_size BOX_SIZE
                        defined the docking grid box size (angstrom), default is 10
  -c CORE, --core CORE  defined the number of mult-process core, default is None <will use all core>
  -s STRICT_MODE, --strict_mode STRICT_MODE
                        If strict mode is on, workflow will remove identical ligands. default is True
  -l LIG_IDENTIFY, --lig_identify LIG_IDENTIFY
                        The workflow will determine the type of all ligand (ions, solvent, nucleic acid,
                        ligand, peptide, cyclic peptide). default is True
  -e SILENCE_MODE, --silence_mode SILENCE_MODE
                        Disable all output information. The workflow will run in silence. default is False
  -t DEBUG, --debug DEBUG
                        debug mode. output some debug information. default is False
  -m {gnn,lgbm}, --backend {gnn,lgbm}
                        Model backend for ligand identification: gnn (default) or lgbm
  -d {cpu,cuda}, --device {cpu,cuda}
                        Device for GNN inference: cpu (default) or cuda

How to Cite

If you use LigandExplorer in your research, please cite:

Li, Y.; Zou, R.; Yang, M.; Wang, Y.; Liu, Z.; Zheng, H. LigandExplorer: An Automated Tool for Ligand Extraction from PDB Structures. J. Chem. Inf. Model. 2026, 66 (6), 3026–3035. DOI: 10.1021/acs.jcim.5c02921

BibTeX

@article{Li2026LigandExplorer,
  author    = {Li, Yaqi and Zou, Rongfeng and Yang, Maohua and Wang, Ying and Liu, Zhonghua and Zheng, Hang},
  title     = {LigandExplorer: An Automated Tool for Ligand Extraction from PDB Structures},
  journal   = {Journal of Chemical Information and Modeling},
  year      = {2026},
  volume    = {66},
  number    = {6},
  pages     = {3026--3035},
  doi       = {10.1021/acs.jcim.5c02921},
  pmid      = {41762111},
}

RIS (EndNote, Zotero, Mendeley)

TY  - JOUR
AU  - Li, Yaqi
AU  - Zou, Rongfeng
AU  - Yang, Maohua
AU  - Wang, Ying
AU  - Liu, Zhonghua
AU  - Zheng, Hang
TI  - LigandExplorer: An Automated Tool for Ligand Extraction from PDB Structures
JO  - Journal of Chemical Information and Modeling
PY  - 2026
VL  - 66
IS  - 6
SP  - 3026
EP  - 3035
DO  - 10.1021/acs.jcim.5c02921
PM  - 41762111
ER  -