Multivariate image analysis applied to quantitative structure-activity relationships and docking studies of recent hydroxyphenylpyruvate deoxygenase inhibitors.

Background: Mesotrione is a triketone widely used as an inhibitor of the hydroxyphenylpyruvate deoxygenase (HPPD) enzyme. However, new agrochemicals should be developed continuously to tackle the problem of herbicide resistance. Two sets of mesotrione analogs have been synthesized recently and they have demonstrated successful phytotoxicity against weeds.

In Silico Interactions of the Components from the Schinus terebinthifolius Extract with Human Tyrosinase.

The anti-tyrosinase activity of the leaf extract of Schinus terebinthifolius, also known as Brazilian peppertree, was evaluated using multiple in silico approaches, such as molecular homology, molecular docking, MM-GBSA, molecular dynamics, MM-PBSA, QSAR, and skin permeability predictions. With these computational tools, the compounds that downregulate tyrosinase enzyme activity could be evaluated, and more potent molecules could be identified. The results indicated that various compounds, especially luteolin, are accountable for the anti-tyrosinase activity of S.

Computer-Assisted Proposition of Promising Aryloxyacetic Acid Derivatives as HPPD Inhibitors.

A series of aryloxyacetic acid derivatives have demonstrated promising herbicidal performance by inhibition of the hydroxyphenylpyruvate deoxygenase (HPPD) enzyme. We hereby applied quantitative structure-activity relationship (QSAR) and docking strategies to model and chemically understand the bioactivities of these compounds and subsequently propose unprecedented analogues aiming at improving the herbicidal and environmental properties. Bulky halogens at the 2-, 3-, 4-, and 6-positions of an aromatic ring, CF in 4-position, and the 2-NO group in a phenyl ring appear to favor the HPPD inhibition.

MIA-QSAR study of the structural merging of (thio)benzamide herbicides with photosynthetic system II inhibitory activities.

Benzamide herbicides consist of a class of photosynthetic system II (PSII) inhibitors widely used for weed control. However, the development of resistance by these weeds to the known herbicides requires an ongoing search for new agrochemicals. We report the combination of two congeneric series of (thio)benzamide herbicides into a single data set and subsequent modeling of their herbicidal activities against PSII using MIA-QSAR.

Is conformation relevant for QSAR purposes? 2D Chemical representation in a 3D-QSAR perspective.

Conformation has a key role in the mechanism of interaction between small molecules and biological receptors. However, encoding this type of information in molecular descriptors for the construction of robust quantitative structure-activity relationships (QSAR) models is not an easy task and, so far, the dependence of these models on such feature has not been thoroughly investigated. In the present study, the authors explore the effects of conformational information on a 3D-QSAR technique by comparing models built with descriptors that encode fully described tridimensional aspects (structures docked inside a biological target), with descriptors in which this information is suppressed (flat structures) or not fully described (structures with quantum-chemically optimized geometries).

Computer-Assisted Improvement of Sulfonylureas with Antifungal Properties and Limited Herbicidal Activity: Potential Application in Forage Conservation.

This work reports studies at the molecular level of a series of modified sulfonylureas to determine the chemophoric sites responsible for their antifungal and herbicidal activities. For forage conservation, high antifungal potency and low phytotoxicity are required. A molecular modeling study based on multivariate image analysis applied to quantitative structure-activity relationship (MIA-QSAR) was performed to model these properties, as well as to guide the design of new agrochemical candidates.

Conformational fingerprints in the modelling performance of MIA-QSAR: a case for SARS-CoV protease inhibitors.

Multivariate image analysis applied to quantitative structure-activity relationships (MIA-QSAR) has proved to be a high-performance 2D tool for drug design purposes. Nonetheless, MIA-QSAR strategy does not efficiently incorporate conformational information. Therefore, understanding the implications of including this type of data into the MIA-QSAR model, in terms of predictability and interpretability, seems a crucial task.

Conformational preferences of fluorine-containing agrochemicals and their implications for lipophilicity prediction.

Molecular polarity governs lipophilicity, which in turn determines important agrochemical and environmental properties, such as soil sorption and bioconcentration of organic compounds. Since the C-F bond is the most polar in organic chemistry, the orientation of fluorine substituents originating from the rotation around C-C(F) bonds should affect the polarity and, consequently, the physicochemical and biological properties of fluorine-containing agrochemicals. Accordingly, this study aims to determine the most likely conformers of some fluorine-containing agrochemicals and to correlate their molecular dipole moments with the respective -octanol/water partition coefficients (log ), in order to investigate the dependence of the lipophilicity with the molecular conformation.

Theoretical study of fluorinated bioisosteres of organochlorine compounds as effective and eco-friendly pesticides.

Chlordane is a worldwide banned organochlorine insecticide because of its hazard to animal and human health. It is also a persistent organic pollutant, which can affect either the soil or the aquatic life. The same applies to other chlorinated cyclodiene insecticides, such as dieldrin and aldrin.

3D perspective into MIA-QSAR: A case for anti-HCV agents.

Quantitative structure-activity relationship (QSAR) is a molecular modeling technique widely used in the discovery of novel drugs. Currently, there are many approaches for performing such analysis, which are commonly classified from 1D to 6D. 2D and 3D techniques are among the most exploited ones.

Revealing chemophoric sites in organophosphorus insecticides through the MIA-QSPR modeling of soil sorption data.

Soil sorption of insecticides employed in agriculture is an important parameter to probe the environmental fate of organic chemicals. Therefore, methods for the prediction of soil sorption of new agrochemical candidates, as well as for the rationalization of the molecular characteristics responsible for a given sorption profile, are extremely beneficial for the environment. A quantitative structure-property relationship method based on chemical structure images as molecular descriptors provided a reliable model for the soil sorption prediction of 24 widely used organophosphorus insecticides.

Quantitative modeling of bioconcentration factors of carbonyl herbicides using multivariate image analysis.

The bioconcentration factor (BCF) is an important parameter used to estimate the propensity of chemicals to accumulate in aquatic organisms from the ambient environment. While simple regressions for estimating the BCF of chemical compounds from water solubility or the n-octanol/water partition coefficient have been proposed in the literature, these models do not always yield good correlations and more descriptive variables are required for better modeling of BCF data for a given series of organic pollutants, such as some herbicides. Thus, the logBCF values for a set of carbonyl herbicides comprising amide, urea, carbamate and thiocarbamate groups were quantitatively modeled using multivariate image analysis (MIA) descriptors, derived from colored image representations for chemical structures.