<< Modelizacion de la estructura-toxicidad de diveros pesticidas utilizando metodos QSAR
, >> de Memorias del II Congreso Binacional de Investigacion, Ciencia y Tecnologia de las Universidades.
equation, regression coefficient is negative, indicating that higher values correspond to lowest pI[C.sub.50].
The next morning the gates shattered, and Qsar
as-Saghir fell on the third day." (64) There is no evidence, however, that any of the main gates were shattered by artillery, a rather spectacular event the chronicles presumably would not have failed to highlight--only the fausse-braye was laboriously penetrated by the Portuguese forces by the evening of Tuesday, 17 October 1458.
Guo et al., "QSAR
models for oxidative degradation of organic pollutants in the Fenton process," Journal of the Taiwan Institute of Chemical Engineers, vol.
SAR and QSAR
in Environmental Research, 15(3), 207-216.
A classic Hansch approach to QSAR
modeling (Hansch and Lien, 1971; Hansch and Clayton, 1973) identified key structural components that correlate with antifungal efficacy of alkamides tested, and can be used to direct studies of synthetic analogs.
Among the several possible QSAR
models, the best QSAR
could be determined from several statistical criteria such as [R.sup.2] and the F value.
Computational studies including QSAR
and molecular structural similarity studies were also performed to establish a relationship between biological activities in terms of minimum inhibitory concentration with various physicochemical parameters using multilinear regression.
and docking studies of novel [beta]-carboline derivatives as anticancer," Medicinal Chemistry Research, vol.
In this study, we applied virtual screen and QSAR
models based on structure-based and ligand-based methods in order to identify the potential TCM compounds.
The Dirani family, who own the land in the Bekaa Valley village of Qsar
Naba where the tents had been set up by Syrian families fleeing their home country, accused four refugees of raping a 29-year-old member of the family.
In this study, we compare the performance of semi-supervised and supervised machine learning methods applied to various problems of modeling Quantitative Structure Activity Relationship (QSAR
) in sets of chemical compounds.