ArticleFrontiers in pharmacology2023
Prediction of histone deacetylase inhibition by triazole compounds based on artificial intelligence.
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.
- Epigenetic Reprogramming of Erythroid Progenitor Cells: Insights for Enhancing Cancer Immunotherapy.International journal of biological sciences · 2026Review
- Staphylococcal nuclease and tudor domain-containing protein 1: An emerging therapeutic target in cancer (Review).Molecular and clinical oncology · 2025Review
- 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
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Authors and funding
2 authors.
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Abstract
A quantitative structure-activity relationship (QSAR) study was conducted to predict the anti-colon cancer and HDAC inhibition of triazole-containing compounds. Four descriptors were selected from 579 descriptors which have the most obvious effect on the inhibition of histone deacetylase (HDAC). Four QSAR models were constructed using heuristic algorithm (HM), random forest (RF), radial basis kernel function support vector machine (RBF-SVM) and support vector machine optimized by particle swarm optimization (PSO-SVM). Furthermore, the robustness of four QSAR models were verified by K-fold cross-validation method, which was described by
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