QSRR study of psychiatric drugs using Classification and Regression Trees combined with adaptive Neuro-Fuzzy Inference System

被引:5
|
作者
Jalali-Heravi, Mehdi [1 ]
Shahbazikhah, Parvis [1 ]
Ghadiri-Bidhendi, Atieh [1 ]
机构
[1] Sharif Univ Technol, Dept Chem, Tehran, Iran
来源
QSAR & COMBINATORIAL SCIENCE | 2008年 / 27卷 / 06期
关键词
adaptive neuro-fuzzy inference system; classification and regression tree; gas chromatography; psychiatric drugs; quantitative structure-retention relationship;
D O I
10.1002/qsar.200710111
中图分类号
R914 [药物化学];
学科分类号
100701 ;
摘要
A new Quantitative Structure-Retention Relationship (QSRR) approach was carried out for prediction of gas-liquid retention times of 124 psychiatric drugs in whole blood on fused-silica capillary column coated with crosslinked methylsilicone with nitrogen-phosphorus detection. After screening the descriptors, a total of 699 topological, geometric, and electronic descriptors (zero- to three-dimensional) representing various structural characteristics were calculated for each molecule in the dataset. Combined method of Classification and Regression Tree (CART) as a feature selection method for the extraction of four relevant descriptors and Adaptive Neuro-Fuzzy Inference System (ANFIS) as a modeling technique was used for the prediction of retention times of diverse set of psychiatric drugs. The Root Mean Square Errors (RMSEs) for the calibration and prediction sets are 0.457 and 0.514, respectively.
引用
收藏
页码:729 / 739
页数:11
相关论文
共 50 条
  • [1] Combining classification and regression trees and the neuro-fuzzy inference system for environmental data modeling
    Burrows, WR
    18TH INTERNATIONAL CONFERENCE OF THE NORTH AMERICAN FUZZY INFORMATION PROCESSING SOCIETY - NAFIPS, 1999, : 695 - 699
  • [2] Adaptive Neuro-Fuzzy Inference System for Classification of Texts
    Kamil, Aida-zade
    Rustamov, Samir
    Clements, Mark A.
    Mustafayev, Elshan
    RECENT DEVELOPMENTS AND THE NEW DIRECTION IN SOFT-COMPUTING FOUNDATIONS AND APPLICATIONS, 2018, 361 : 63 - 70
  • [3] A study on the application of regression trees and Adaptive Neuro-Fuzzy Inference System in glass manufacturing process for packaging
    Costa, Herbert R. do N.
    La Neve, Alessandro
    2016 ANNUAL CONFERENCE OF THE NORTH AMERICAN FUZZY INFORMATION PROCESSING SOCIETY (NAFIPS), 2016,
  • [4] Fuzzy nonparametric regression based on an adaptive neuro-fuzzy inference system
    Danesh, Sedigheh
    Farnoosh, Rahman
    Razzaghnia, Tahereh
    NEUROCOMPUTING, 2016, 173 : 1450 - 1460
  • [5] Bayesian inference using an adaptive neuro-fuzzy inference system
    Knaiber, Mohammed
    Alawieh, Leen
    FUZZY SETS AND SYSTEMS, 2023, 459 : 43 - 66
  • [7] Adaptive Neuro-Fuzzy Inference System for Classification of ECG Signal
    Muthuvel, K.
    Suresh, L. Padma
    PROCEEDINGS OF 2013 INTERNATIONAL CONFERENCE ON CIRCUITS, POWER AND COMPUTING TECHNOLOGIES (ICCPCT 2013), 2013, : 1162 - 1166
  • [8] Adaptive Neuro-Fuzzy Inference System for Texture Image Classification
    Kuncoro, B. Ari
    Suharjito
    PROCEEDINGS OF THE 2015 INTERNATIONAL CONFERENCE ON AUTOMATION, COGNITIVE SCIENCE, OPTICS, MICRO ELECTRO-MECHANICAL SYSTEM, AND INFORMATION TECHNOLOGY (ICACOMIT), 2015, : 196 - 200
  • [9] GPS Signal Reception Classification Using Adaptive Neuro-Fuzzy Inference System
    Sun, Rui
    Hsu, Li-Ta
    Xue, Dabin
    Zhang, Guohao
    Ochieng, Washington Yotto
    JOURNAL OF NAVIGATION, 2019, 72 (03): : 685 - 701
  • [10] Automatic Classification of Antepartum Cardiotocography Using Fuzzy Clustering and Adaptive Neuro-Fuzzy Inference System
    Fei, Yue
    Huang, Xiaoqian
    Chen, Qinqun
    Chen, Jiamin
    Li, Li
    Hong, Jiaming
    Hao, Zhifeng
    Wei, Hang
    2020 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE, 2020, : 1938 - 1942