PLA-SGCN: Protein-Ligand Binding Affinity Prediction by Integrating Similar Pairs and Semi-supervised Graph Convolutional Network
<|reference_start|>PLA-SGCN: Protein-Ligand Binding Affinity Prediction by Integrating Similar Pairs and Semi-supervised Graph Convolutional Network: The protein-ligand binding affinity (PLA) prediction goal is to predict whether or not the ligand could bind to a protein sequence. Recently, in PLA prediction, deep learning has received much attention. Two steps are involved in deep learning-based approaches: feature extraction and task prediction step. Many deep learning-based approaches concentrate on introducing new feature extraction networks or integrating auxiliary knowledge like protein-protein interaction networks or gene ontology knowledge. Then, a task prediction network is designed simply using some fully connected layers. This paper aims to integrate retrieved similar hard protein-ligand pairs in PLA prediction (i.e., task prediction step) using a semi-supervised graph convolutional network (GCN). Hard protein-ligand pairs are retrieved for each input query sample based on the manifold smoothness constraint. Then, a graph is learned automatically in which each node is a protein-ligand pair, and each edge represents the similarity between pairs. In other words, an end-to-end framework is proposed that simultaneously retrieves hard similar samples, learns protein-ligand descriptor, learns the graph topology of the input sample with retrieved similar hard samples (learn adjacency matrix), and learns a semi-supervised GCN to predict the binding affinity (as task predictor). The training step adjusts the parameter values, and in the inference step, the learned model is fine-tuned for each input sample. To evaluate the proposed approach, it is applied to the four well-known PDBbind, Davis, KIBA, and BindingDB datasets. The results show that the proposed method significantly performs better than the comparable approaches.<|reference_end|>
@article{abbasi2024pla-sgcn:,
    title={PLA-SGCN: Protein-Ligand Binding Affinity Prediction by Integrating
  Similar Pairs and Semi-supervised Graph Convolutional Network},
    author={Karim Abbasi, Parvin Razzaghi, Amin Ghareyazi, Hamid R. Rabiee},
    journal={arXiv preprint arXiv:2405.07452},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.07452},
    primaryClass={q-bio.QM cs.LG}
}