华中科技大学学报（自然科学版）2024，Vol.52Issue(11)：78-84,7.DOI:10.13245/j.hust.241111
子树权重等4种拓扑指标预测抗HIV病毒活性
Prediction of anti-HIV activity using four topological indices including subtree weights
摘要
Abstract
Aiming at the issue of poor model generalization in current anti-human immunodeficiency virus(HIV)prediction methods that predominantly relied on distance-based indices,an efficient approach for computing structural subtree weight indices was proposed by constructing a lossless row transformation rule for subtree weight information based on a generalized adjacency matrix.By integrating Wiener,Harary,and Schultz indices,and utilizing classical supervised learning algorithms in machine learning(support vector machines(SVM),K-nearest neighbors(KNN)algorithm,decision trees algorithm),models were built to predict the anti-HIV activity of compound molecules.Experimental results show that subtree weight indices exhibit excellent feature discrimination and accuracy,ranging from 91.03%to 99.61%.Therefore,this index can serve as an effective new metric in new drug discovery.关键词
子树权重指标/广义邻接矩阵/树与单双圈图/机器学习/新药研发Key words
subtree weight index/generalized adjacency matrix/tree,unicyclic and bicyclic graphs/machine learning/new drug discovery分类
信息技术与安全科学引用本文复制引用
杨雨,靳棒棒,李波,张修梅..子树权重等4种拓扑指标预测抗HIV病毒活性[J].华中科技大学学报（自然科学版）,2024,52(11):78-84,7.基金项目
河南省科技厅国际科技合作资助项目(242102521023,232102521002) （242102521023,232102521002）
河南省科技厅科技攻关资助项目(232102210011). （232102210011）
