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QSAR investigations and structure-based virtual screening on a series of nitrobenzoxadiazole derivatives targeting human glutathione-S-transferases
- Source :
- Journal of Molecular Structure, Journal of Molecular Structure, 2020, 1211, 10p. ⟨10.1016/j.molstruc.2020.128015⟩
- Publication Year :
- 2020
- Publisher :
- Elsevier BV, 2020.
-
Abstract
- Quantitative structure-activity relationship (QSAR) models are useful tools for understanding the relation between biological activity and chemical structure, and for the design of new drugs. In this work, the performances of two QSAR approaches for modelling and predicting glutathione-S-transferases (GSTP1-1) inhibition are compared, namely Multiple Linear Regression (MLR) and Artificial Neural Network (ANN). These models are applied to 38 thiol substituted nitrobenzoxadiazole inhibitors. For these compounds, we found that the nonlinear model performs better than the linear one in terms of predictive ability, indicating a non-linear relation between the selected molecular descriptors and GSTP1-1 inhibition. The validity of the proposed models was established using the following techniques: separation of data into independent training and test sets, leave-one-out cross-validation, and Y-randomization. Otherwise, the domain of applicability indicating the area of reliable predictions was defined. Through the adopted QSAR model and in silico virtual screening, we identified 23 hits with good predictability. Our work should motivate future in vitro investigations on these compounds.
- Subjects :
- MECHANISM
Quantitative structure–activity relationship
PREDICTION
In silico
APPLICABILITY
Computational biology
010402 general chemistry
01 natural sciences
VALIDATION
Analytical Chemistry
Inorganic Chemistry
GSTP1-1 INHIBITOR
DESIGN
Molecular descriptor
Linear regression
NITROBENZOXADIAZOLE DERIVATIVE
VIRTUAL SCREENING
Spectroscopy
AGENT
Virtual screening
Artificial neural network
Series (mathematics)
010405 organic chemistry
Chemistry
Organic Chemistry
0104 chemical sciences
HIT PARTIAL LEAST-SQUARES
ACID
Structure based
NEURAL-NETWORKS
[PHYS.PHYS.PHYS-CHEM-PH]Physics [physics]/Physics [physics]/Chemical Physics [physics.chem-ph]
Subjects
Details
- ISSN :
- 00222860
- Volume :
- 1211
- Database :
- OpenAIRE
- Journal :
- Journal of Molecular Structure
- Accession number :
- edsair.doi.dedup.....87d3ef0b10a02bb02571758da65fa2ca