Role of physico-chemical parameters in Quantitative Structure-Activity Relationship based modeling of CYP26A1 inhibitory activity

被引:0
作者
Srivastava, A. K. [1 ]
Srivastava, Akanchha [1 ]
Archana [1 ]
Jaiswal, Meetu [1 ]
机构
[1] Univ Allahabad, Dept Chem, QSAR Res Lab, Allahabad 211002, Uttar Pradesh, India
关键词
QSAR; CYP26A1; physico-chemical parameters;
D O I
暂无
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Quantitative Structure-Activity Relationship (QSAR) studies were performed on CYP26A1 inhibitory activity using physico-chemical parameters like Balaban centric index (BAC), mean Weiner index (WA), Balaban distance connectivity index (J), information theoretic index (Id), zero order ((0)chi), first order ((1)chi) and second order ((2)chi) Of molecular connectivity, polarizibility (Pz) and partition coefficient (log P) along with appropriate dummy parameter. The QSAR models were tested for their statistical significance and reliability by using leave one out cross validation method. Some significant models have been reported.
引用
收藏
页码:721 / 727
页数:7
相关论文
共 50 条
[41]   Development of quantitative structure-activity relationship for a set of carbonic anhydrase inhibitors: Use of quantum and chemical descriptors [J].
Khadikar, Padmakar V. ;
Deeb, Omar ;
Jaber, Amal ;
Singh, Jyoti ;
Agrawal, Vijay K. ;
Singh, Shalini ;
Lakhwani, Meenakshi .
LETTERS IN DRUG DESIGN & DISCOVERY, 2006, 3 (09) :622-635
[42]   Structural Similarity Based Kriging for Quantitative Structure Activity and Property Relationship Modeling [J].
Teixeira, Ana L. ;
Falcao, Andre O. .
JOURNAL OF CHEMICAL INFORMATION AND MODELING, 2014, 54 (07) :1833-1849
[43]   Modeling the Dispersibility of Single Walled Carbon Nanotubes in Organic Solvents by Quantitative Structure-Activity Relationship Approach [J].
Yilmaz, Hayriye ;
Rasulev, Bakhtiyor ;
Leszczynski, Jerzy .
NANOMATERIALS, 2015, 5 (02) :778-791
[44]   AutoQSAR: an automated machine learning tool for best-practice quantitative structure-activity relationship modeling [J].
Dixon, Steven L. ;
Duan, Jianxin ;
Smith, Ethan ;
Von Bargen, Christopher D. ;
Sherman, Woody ;
Repasky, Matthew P. .
FUTURE MEDICINAL CHEMISTRY, 2016, 8 (15) :1825-1839
[45]   Systematic review on the application of machine learning to quantitative structure-activity relationship modeling against Plasmodium falciparum [J].
Oguike, Osondu Everestus ;
Ugwuishiwu, Chikodili Helen ;
Asogwa, Caroline Ngozi ;
Nnadi, Charles Okeke ;
Obonga, Wilfred Ofem ;
Attama, Anthony Amaechi .
MOLECULAR DIVERSITY, 2022, 26 (06) :3447-3462
[46]   Using quantitative structure-activity relationship modeling to quantitatively predict the developmental toxicity of halogenated azole compounds [J].
Craig, Evisabel A. ;
Wang, Nina Ching ;
Zhao, Q. Jay .
JOURNAL OF APPLIED TOXICOLOGY, 2014, 34 (07) :787-794
[47]   Quantitative structure-activity relationship modeling for predication of inhibition potencies of imatinib derivatives using SMILES attributes [J].
Hamzehali, Hamideh ;
Lotfi, Shahram ;
Ahmadi, Shahin ;
Kumar, Parvin .
SCIENTIFIC REPORTS, 2022, 12 (01)
[48]   Quantitative structure-activity relationship modeling and docking of some synthesized bioactive oxopyrolidines against Staphylococcus aureus [J].
Albratty, Mohammed .
JOURNAL OF SAUDI CHEMICAL SOCIETY, 2022, 26 (04)
[49]   Zerovalent Iron/Cu Combined Degradation of Halogenated Disinfection Byproducts and Quantitative Structure-Activity Relationship Modeling [J].
Liu, Yan ;
Gao, Jianfa ;
Zhu, Qingyao ;
Zhou, Xi ;
Chu, Wenhai ;
Huang, Jingxiong ;
Liu, Changkun ;
Yang, Bo ;
Yang, Mengting .
ENVIRONMENTAL SCIENCE & TECHNOLOGY, 2023, 57 (30) :11241-11250
[50]   Data-Driven Quantitative Structure-Activity Relationship Modeling for Human Carcinogenicity by Chronic Oral Exposure [J].
Chung, Elena ;
Russo, Daniel P. ;
Ciallella, Heather L. ;
Wang, Yu-Tang ;
Wu, Min ;
Aleksunes, Lauren M. ;
Zhu, Hao .
ENVIRONMENTAL SCIENCE & TECHNOLOGY, 2023, 57 (16) :6573-6588