ClusterX

A Novel Representation Learning-Based Deep Clustering Framework for Accurate Visual Inspection in Virtual Screening

Introduction

Molecular clustering analysis has been developed to facilitate visual inspection in the process of structure-based virtual screening. However, traditional methods based on molecular fingerprints or molecular descriptors limit the accuracy of selecting active hit compounds, which may be attributed to the lack of representations of receptor structural and protein–ligand interaction during the clustering. Here, a novel deep clustering framework named ClusterX is proposed to learn molecular representations of protein-ligand complexes and clustering the ligands. In ClusterX, the graph was used to represent the protein–ligand complex, and the joint optimization can be used efficiently for learning the cluster-friendly features. This framework can provide a unique tool for cluster analysis and we hope that it will help computational medicinal chemists to make visual decisions.

Requirements

The project mainly uses three python packages, RDKit, dgllife and openselfsup. RDKit is used to read the structure of the protein-ligand complex, dgllife for building molecular graphs and openselfsup is used to quickly build a deep clustering model. For more details on the specific version numbers of required packages, see requirements.txt.

How to use

After installing the required dependencies, you can quickly start with the following command：

git clone https://github.com/ChenSikang/ClusterX.git

1. graph construct

The training data (ClusterX dataset) needs to be constructed through Featurizer.py, and the molecular graph is output as a numpy file. The same steps are used for the data to be clusteredBefore running, please make sure that the dataset exists in the data_dir path.

python Featurizer.py --data_dir 'your_data_path'

2. pre-training

We provide the pre-trained network parameters (pretrain_ClusterX.pkl), you can also use ClusterX dataset or your own training set to retrain a pre-trained model

python pretrain.py --data_dir 'your_train_data_path'

3.clustering(training)

After training, the clustering information will be output

python train.py --data_dir 'your_clustering_analysis_data_path' --pretrain_path 'your_pretrain_model_path'

Running following command will use model to predict the example_klifs_data.npy protein-ligand complex file in KLIFS database.

Before running, please make sure that the model file exists in the current path.

unzip ./example_klifs_data.zip
python train.py --data_dir './example_klifs_data.npy' --pretrain_path './pretrain_ClusterX.pkl'

4.visual inspection

ClusterX does not have the function of visualizing the binding conformation of the protein-ligand complex. You can import the initial protein-ligand complex file into Schrödinger, discovery studio，MOE and other visualization software combined with the clustering information provided by ClusterX for visual inspection.

Citation

If you use ClusterX in your work, please cite the following paper:

Sikang Chen, Jian Gao, Jiexuan Chen, Yufeng Xie, Zheyuan Shen, Lei Xu, Jinxin Che*, Jian Wu*, Xiaowu Dong* (2023) ClusterX: A Novel Representation Learning-Based Deep Clustering Framework for Accurate Visual Inspection in Virtual Screening. Briefings in Bioinformatics.

BibTeX entry:

@article{ClusterX,
  title={ClusterX: A Novel Representation Learning-Based Deep Clustering Framework for Accurate Visual Inspection in Virtual Screening.},
  author={Sikang Chen, Jian Gao, Jiexuan Chen, Yufeng Xie, Zheyuan Shen, Lei Xu, Jinxin Che, Jian Wu, Xiaowu Dong},
  journal={Briefings in Bioinformatics},
  year={2023}
}