Activity and toxicity modelling of some NCI selected compounds against leukemia P388ADR cell line using genetic algorithm-multiple linear regressions

被引:11
作者
Arthur, David Ebuka [1 ]
Uzairu, Adamu [1 ]
Mamza, Paul [1 ]
Abechi, Stephen Eyije [1 ]
Shallangwa, Gideon [1 ]
机构
[1] Ahmadu Bello Univ, Dept Chem, Zaria, Kaduna State, Nigeria
关键词
QSAR; Multiple linear regression; Drugs; Genetic algorithm; Validation; Molecular descriptors; IN-SILICO DISCOVERY; QSAR MODEL; DIFFERENTIAL INHIBITION; ALDOSE REDUCTASE; CHEMOTHERAPY; DESIGN; DESCRIPTORS; VALIDATION; COMPONENT; 3D-QSAR;
D O I
10.1016/j.jksus.2018.05.023
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Cancer-causing nature is one of the toxicological endpoints bringing about the most elevated concern. Likewise, the standard bioassays in rodents used to survey the cancer-mitigating capability of chemicals and medications are expensive and require the sacrifice of animals. Thus, we have endeavored the development of a worldwide QSAR model utilizing an information set of 85 compounds, including drugs for their anti-leukemia potential. Considering expansive number of information focuses with different structural elements utilized for model development (ntraining = 68) and model validation (ntest = 17), the model developed in this study has an encouraging statistical quality (leave-one-out Q2 = 0.833, R2pred = 0.716) for pLC50 and (leave-one-out Q2 = 0.744, R2pred = 0.614) for pGI50. Our developed model suggests that the absence of methanal fragments, low dipole moment and presence of some 2D autocorrelated molecular descriptors reduces the carcinogenicity. Branching, size and shape are found to be crucial factors for drug-mitigating carcinogenicity. (C) 2018 Production and hosting by Elsevier B.V.
引用
收藏
页码:324 / 331
页数:8
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