Probing the structural requirements for thyroid hormone receptor inhibitory activity of sulfonylnitrophenylthiazoles (SNPTs) using 2D-QSAR and 3D-QSAR approaches View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2017-02

AUTHORS

Fang-Fang Wang, Wei Yang, Yong-Hui Shi, Guo-Wei Le

ABSTRACT

A series of sulfonylnitrophenylthiazoles derivatives were identified as effective targeting agents that block the interaction of the thyroid hormone receptor with its coactivators. In this work, in order to analyze the structure-activity relationship of these inhibitors and investigate the structural requirements for thyroid hormone receptor inhibitory activity, new statistically validated in silico models adopting different molecular descriptors were established. The two-dimensional quantitative structure-activity relationship models were developed using multiple linear regression method, which show both significant statistical quality and predictive ability (R2 = 0.939, Q2 = 0.622 for thyroid hormone receptor β; R2 = 0.862, Q2 = 0.763 for thyroid hormone receptor α), and different molecular descriptors were included, namely R2e, H5U, EEigo4r and Ram for thyroid hormone receptor β, MATS1P, IC2 and R5e+ for thyroid hormone receptor α. The optimum comparative molecular field analysis models were established using the template ligand-based alignment, which show satisfactory linear correlations (thyroid hormone receptor β: R2cv = 0.577, R2pred = 0.8013; thyroid hormone receptor α: R2cv = 0.549, R2pred = 0.8639). In addition, the R2cv of 0.543, R2pred of 0.8523 for thyroid hormone receptor β and R2cv of 0.560, R2pred of 0.8695 for thyroid hormone receptor α have been observed when comparative molecular similarity analysis fields were applied. All the developed statistical models give satisfactory results with accurate fitting and strong predictive abilities. Moreover, the contour maps provide an intuitive understanding of the structural requirements for the inhibitors. In conclusion, these data can provide some meaningful theoretical references to understand the factors influencing the inhibitory activity and direct the molecular design of novel inhibitors with increased activity. More... »

PAGES

344-360

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00044-016-1751-3

DOI

http://dx.doi.org/10.1007/s00044-016-1751-3

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50 schema:description A series of sulfonylnitrophenylthiazoles derivatives were identified as effective targeting agents that block the interaction of the thyroid hormone receptor with its coactivators. In this work, in order to analyze the structure-activity relationship of these inhibitors and investigate the structural requirements for thyroid hormone receptor inhibitory activity, new statistically validated in silico models adopting different molecular descriptors were established. The two-dimensional quantitative structure-activity relationship models were developed using multiple linear regression method, which show both significant statistical quality and predictive ability (R2 = 0.939, Q2 = 0.622 for thyroid hormone receptor β; R2 = 0.862, Q2 = 0.763 for thyroid hormone receptor α), and different molecular descriptors were included, namely R2e, H5U, EEigo4r and Ram for thyroid hormone receptor β, MATS1P, IC2 and R5e+ for thyroid hormone receptor α. The optimum comparative molecular field analysis models were established using the template ligand-based alignment, which show satisfactory linear correlations (thyroid hormone receptor β: R2cv = 0.577, R2pred = 0.8013; thyroid hormone receptor α: R2cv = 0.549, R2pred = 0.8639). In addition, the R2cv of 0.543, R2pred of 0.8523 for thyroid hormone receptor β and R2cv of 0.560, R2pred of 0.8695 for thyroid hormone receptor α have been observed when comparative molecular similarity analysis fields were applied. All the developed statistical models give satisfactory results with accurate fitting and strong predictive abilities. Moreover, the contour maps provide an intuitive understanding of the structural requirements for the inhibitors. In conclusion, these data can provide some meaningful theoretical references to understand the factors influencing the inhibitory activity and direct the molecular design of novel inhibitors with increased activity.
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