ArticleComputational and mathematical methods in medicine2019
SmoPSI: Analysis and Prediction of Small Molecule Binding Sites Based on Protein Sequence Information.
Article in Computational and mathematical methods in medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- MGF-DTA: A Multi-Granularity Fusion Model for Drug-Target Binding Affinity Prediction.International journal of molecular sciences · 2026Article
- Improving Identification of Drug-Target Binding Sites Based on Structures of Targets Using Residual Graph Transformer Network.Biomolecules · 2025Article
- Predicting CTCF cell type active binding sites in human genome.Scientific reports · 2024Article
- Drug-Online: an online platform for drug-target interaction, affinity, and binding sites identification using deep learning.BMC bioinformatics · 2024Article
- Prediction of DNA-Binding Protein-Drug-Binding Sites Using Residue Interaction Networks and Sequence Feature.Frontiers in bioengineering and biotechnology · 2022Article
- A deep learning based framework for the classification of multi- class capsule gastroscope image in gastroenterologic diagnosis.Frontiers in physiology · 2022Article
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Authors and funding
6 authors.
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Abstract
The analysis and prediction of small molecule binding sites is very important for drug discovery and drug design. The traditional experimental methods for detecting small molecule binding sites are usually expensive and time consuming, and the tools for single species small molecule research are equally inefficient. In recent years, some algorithms for predicting binding sites of protein-small molecules have been developed based on the geometric and sequence characteristics of proteins. In this paper, we have proposed SmoPSI, a classification model based on the XGBoost algorithm for predicting the binding sites of small molecules, using protein sequence information. The model achieved better results with an AUC of 0.918 and an ACC of 0.913. The experimental results demonstrate that our method achieves high performances and outperforms many existing predictors. In addition, we also analyzed the binding residues and nonbinding residues and finally found the PSSM; hydrophilicity, hydrophobicity, charge, and hydrogen bonding have obviously different effects on the binding-site predictions.
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