Topological indices based on 2- or 3-eccentricity to predict anti-HIV activity

作者：

Highlights：

• Four topological indices based on k-eccentricity were introduced. The algorithms on these topological indices were devised. The time complexity was analyzed.

• we employ the given topological indices based on 2- and 3-eccentricity as the feature vectors to predict anti-HIV activity by devising machine learning predicting models with the help of Support Vector Machine, K Nearest Neighbor and Decision Tree, respectively.

• Through these experiments, we find that these topological indices based on the k-eccentricity (k=2,3) have good applications in predicting anti-HIV activity. The highest predicting accuracy is 99.7%, while the lowest is 97.7%.

摘要

•Four topological indices based on k-eccentricity were introduced. The algorithms on these topological indices were devised. The time complexity was analyzed.•we employ the given topological indices based on 2- and 3-eccentricity as the feature vectors to predict anti-HIV activity by devising machine learning predicting models with the help of Support Vector Machine, K Nearest Neighbor and Decision Tree, respectively.•Through these experiments, we find that these topological indices based on the k-eccentricity (k=2,3) have good applications in predicting anti-HIV activity. The highest predicting accuracy is 99.7%, while the lowest is 97.7%.

论文关键词：Topological index based on k-eccentricity,Predicting anti-HIV activity,Machine learning predicting models

论文评审过程：Received 26 June 2021, Revised 14 October 2021, Accepted 16 October 2021, Available online 3 November 2021, Version of Record 3 November 2021.

论文官网地址：https://doi.org/10.1016/j.amc.2021.126748