ArticleFrontiers in pharmacology2023
Quantitative structure-activity relationship study of amide derivatives as xanthine oxidase inhibitors using machine learning.
Article in Frontiers in pharmacology, 2023. 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.
- Hybrid APSO-Bayesian optimized triple-kernel SVR models for QSAR and rational design of 1H-pyrrolo[2,3-c]pyridine-based LSD1 Inhibitors.Molecular diversity · 2026Article
- Quantitative Structure-Activity Relationship Study of Cathepsin L Inhibitors as SARS-CoV-2 Therapeutics Using Enhanced SVR with Multiple Kernel Function and PSO.International journal of molecular sciences · 2025Article
- Predicting EGFRPharmaceuticals (Basel, Switzerland) · 2025Article
- Predicting anti-trypanosome effect of carbazole-derived compounds by powerful SVM with novel kernel function and comprehensive learning PSO.Antimicrobial agents and chemotherapy · 2024Article
- Study of PARP inhibitors for breast cancer based on enhanced multiple kernel function SVR with PSO.Frontiers in pharmacology · 2024Article
- Prediction of histone deacetylase inhibition by triazole compounds based on artificial intelligence.Frontiers in pharmacology · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The target of the study is to predict the inhibitory effect of amide derivatives on xanthine oxidase (XO) by building several models, which are based on the theory of the quantitative structure-activity relationship (QSAR). The heuristic method (HM) was used to linearly select descriptors and build a linear model. XGBoost was used to non-linearly select descriptors, and radial basis kernel function support vector regression (RBF SVR), polynomial kernel function SVR (poly SVR), linear kernel function SVR (linear SVR), mix-kernel function SVR (MIX SVR), and random forest (RF) were adopted to establish non-linear models, in which the MIX-SVR method gives the best result. The kernel function of MIX SVR has strong abilities of learning and generalization of established models simultaneously, which is because it is a combination of the linear kernel function, the radial basis kernel function, and the polynomial kernel function. In order to test the robustness of the models, leave-one-out cross validation (LOOCV) was adopted. In a training set,
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