ArticleGenes2019
SXGBsite: Prediction of Protein-Ligand Binding Sites Using Sequence Information and Extreme Gradient Boosting.
Article in Genes, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed.
- DeepIR-Pred: identification of insulin receptors using metaheuristic optimization of biologically informed multi-view features with deep recurrent learning.Briefings in bioinformatics · 2026Article
- Article
- MGF-DTA: A Multi-Granularity Fusion Model for Drug-Target Binding Affinity Prediction.International journal of molecular sciences · 2026Article
- Computational approaches to druggable site identification: Current status and future perspective.Acta pharmaceutica Sinica. B · 2026Review
- Predicting the binding residues of four small molecule ligands by utilizing ensemble algorithms with additional correlation features.Scientific reports · 2025Article
- Hybrid protein-ligand binding residue prediction with protein language models: does the structure matter?Bioinformatics (Oxford, England) · 2025Article
- Improving Identification of Drug-Target Binding Sites Based on Structures of Targets Using Residual Graph Transformer Network.Biomolecules · 2025Article
- Construction and validation of a perioperative blood transfusion model for patients undergoing total hip arthroplasty with osteonecrosis of the femoral head based on machine learning.Frontiers in medicine · 2025Article
- TargetCLP: clathrin proteins prediction combining transformed and evolutionary scale modeling-based multi-view features via weighted feature integration approach.Briefings in bioinformatics · 2024Article
- EPDRNA: A Model for Identifying DNA-RNA Binding Sites in Disease-Related Proteins.The protein journal · 2024Article
- S-DCNN: prediction of ATP binding residues by deep convolutional neural network based on SMOTE.Frontiers in genetics · 2024Article
- In Silico Methods for Identification of Potential Active Sites of Therapeutic Targets.Molecules (Basel, Switzerland) · 2022Review
- Prediction of DNA-Binding Protein-Drug-Binding Sites Using Residue Interaction Networks and Sequence Feature.Frontiers in bioengineering and biotechnology · 2022Article
- A Comprehensive Review of Computation-Based Metal-Binding Prediction Approaches at the Residue Level.BioMed research international · 2022Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The prediction of protein-ligand binding sites is important in drug discovery and drug design. Protein-ligand binding site prediction computational methods are inexpensive and fast compared with experimental methods. This paper proposes a new computational method, SXGBsite, which includes the synthetic minority over-sampling technique (SMOTE) and the Extreme Gradient Boosting (XGBoost). SXGBsite uses the position-specific scoring matrix discrete cosine transform (PSSM-DCT) and predicted solvent accessibility (PSA) to extract features containing sequence information. A new balanced dataset was generated by SMOTE to improve classifier performance, and a prediction model was constructed using XGBoost. The parallel computing and regularization techniques enabled high-quality and fast predictions and mitigated overfitting caused by SMOTE. An evaluation using 12 different types of ligand binding site independent test sets showed that SXGBsite performs similarly to the existing methods on eight of the independent test sets with a faster computation time. SXGBsite may be applied as a complement to biological experiments.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.