ArticleInternational journal of molecular sciences2023
A Random Forest Model for Peptide Classification Based on Virtual Docking Data.
Article in International journal of molecular sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 16 citations in OpenAlex.
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- Lipopeptide Engineering: From Natural Origins to Rational Design Against Antimicrobial Resistance.Antibiotics (Basel, Switzerland) · 2026Review
- The Application of artificial intelligence in periprosthetic joint infection.Journal of advanced research · 2026Review
- Prediction of clinical stages of cervical cancer via machine learning integrated with clinical features and ultrasound-based radiomics.Scientific reports · 2025Article
- Application of machine learning models to explore prognosis and cause of death in advanced intrahepatic cholangiocarcinoma patients undergoing chemotherapy.Discover oncology · 2025Article
- Constructing a screening model to identify patients at high risk of hospital-acquired influenza on admission to hospital.Frontiers in public health · 2025Article
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- Therapeutic peptide development revolutionized: Harnessing the power of artificial intelligence for drug discovery.Heliyon · 2024Review
- Innovation through Artificial Intelligence in Triage Systems for Resource Optimization in Future Pandemics.Biomimetics (Basel, Switzerland) · 2024Article
- A Computational Predictor for Accurate Identification of Tumor Homing Peptides by Integrating Sequential and Deep BiLSTM Features.Interdisciplinary sciences, computational life sciences · 2024Article
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- Identifying hub genes in response to ustekinumab and the impact of ustekinumab treatment on fibrosis in Crohn's disease.Frontiers in immunology · 2024Article
- On the Use of Machine Learning Techniques and Non-Invasive Indicators for Classifying and Predicting Cardiac Disorders.Biomedicines · 2023Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
Abstract
The affinity of peptides is a crucial factor in studying peptide-protein interactions. Despite the development of various techniques to evaluate peptide-receptor affinity, the results may not always reflect the actual affinity of the peptides accurately. The current study provides a free tool to assess the actual peptide affinity based on virtual docking data. This study employed a dataset that combined actual peptide affinity information (active and inactive) and virtual peptide-receptor docking data, and different machine learning algorithms were utilized. Compared with the other algorithms, the random forest (RF) algorithm showed the best performance and was used in building three RF models using different numbers of significant features (four, three, and two). Further analysis revealed that the four-feature RF model achieved the highest Accuracy of 0.714 in classifying an independent unknown peptide dataset designed with the PEDV spike protein, and it also revealed overfitting problems in the other models. This four-feature RF model was used to evaluate peptide affinity by constructing the relationship between the actual affinity and the virtual docking scores of peptides to their receptors.
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