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 this study, these compounds were joined to form a single data set and the HPPD inhibition of this enlarged library of triketones was modeled using multivariate image analysis applied to quantitative structure-activity relationships (MIA-QSAR). Docking studies were also carried out to validate the MIA-QSAR findings and to aid the interpretation of ligand-enzyme interactions responsible for the bioactivity (pIC50 ). RESULTS: The MIA-QSAR models based on van der Waals radii (rvdW ), electronegativity (ε), and the rvdW /ε ratio as molecular descriptors were both predictive to an acceptable degree (r2 ≥ 0.80, q2 ≥ 0.68 and r2 pred ≥ 0.68). Subsequently, partial least squares (PLS) regression parameters were applied to predict the pIC50 values of newly proposed derivatives, yielding a few promising agrochemical candidates. The calculated log P for most of these derivatives was found to be higher than that of mesotrione and the library compounds, indicating that they should be less prone to leach out and contaminate groundwater. CONCLUSION: Multivariate image analysis descriptors corroborated by docking studies were capable of modeling the herbicidal activities of 68 triketones reliably. Due to the substituent effects at the triketone framework, particularly of a nitro group in R3 , promising analogs could be designed. The P9 proposal demonstrated higher calculated activity and log P than commercial mesotrione. © 2023 Society of Chemical Industry.

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. The robust and predictive models were used to estimate the pIC50 of new agrochemical candidates, which were proposed based on a chemical mixing of the substructures of the most active compounds present in the data set. The chemical features affecting the herbicidal activities were analyzed using MIA contour maps, whereas the ligand-enzyme interactions responsible for the binding affinities were rationalized through docking studies. The proposed compound possessing a thiobenzamide moiety and C-11 chain, H, NO2, OH, and OH as variable substituents was the most promising alternative.Communicated by Ramaswamy H. Sarma.

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. terebinthifolius. For cosmetic application, further studies with luteolin are especially recommended, for having presented a good performance both in theoretical inhibition (30.92 kJ mol-1 ) and skin permeability (LogKp=-6.62 cm-1 ).

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, CF3 in 4-position, and the 2-NO2 group in a phenyl ring appear to favor the HPPD inhibition. At the same time, Me and OMe substituents contribute to decreasing the pKi values. Accordingly, a few compounds were proposed and the candidate with 2,4,6-triBr substituents demonstrated an estimated pKi similar to those of the best library compounds. This finding was corroborated by the docking scores of the ligand-enzyme interactions. In addition, the high calculated lipophilicity of some proposed agrochemicals suggests that they should have low soil mobility and, therefore, are not prone to easily leach out and reach groundwater, despite causing other ecological issues.

In silico modeling of the AHAS inhibition of an augmented series of pyrimidine herbicides and design of novel derivatives.

Pyrimidine compounds comprise a class of acetohydroxyacid synthase (AHAS) inhibitors, thus possessing herbicidal activity. Due to the ongoing development of resistance by weeds to current herbicides, the design of new agrochemical candidates is often required. This work reports the proposition of unprecedented pyrimidines as herbicides guided by quantitative structure-activity relationship (QSAR) modeling. Multivariate image analysis (MIA) descriptors for 66 pyrimidine derivatives obtained from different sources were regressed against inhibitory activity data, and the resulting QSAR models were found to be reliable and predictive (r2 = 0.88 ± 0.07, q2 = 0.53 ± 0.06, and r2pred = 0.51 ± 0.10 in a bootstrapping experiment using electronegativity-based descriptors). The chemical features responsible for the herbicidal activities were analyzed through MIA contour maps that describe the substituent effects on the response variables, whereas the interaction between the proposed compounds and AHAS was analyzed through docking studies. From the proposed compounds, at least five pyrimidine derivatives exhibited promising performance as AHAS inhibitors compared to the known analogs.

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. As a result, the MIA-QSAR models were reliable, robust, and predictive; for antifungal activity, the averages of the main validation parameters were r2 = 0.936, q2 = 0.741, and r2pred = 0.720, and for herbicidal activity, the model was very predictive (r2pred = 0.981 and r2m = 0.944). From the interpretation of the MIA-plots, 46 novel sulfonylureas with likely improved performance were proposed, from which 9 presented promising calculated selectivity indexes. Docking studies were performed to validate the QSAR predictions and to understand the interaction mode of the proposed ligands with the acetohydroxyacid synthase enzyme.

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). As a result, the validation parameters indicate that the robustness of the models seems to be more related to the alignment aspect of the structures than to how well their tridimensional features are described.

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 n-octanol/water partition coefficients (log P), 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. In turn, organofluorine compounds have a widespread use in agriculture. Therefore, density functional calculations and docking studies showed that the bioisosteric replacement of chlorines in the above-mentioned compounds by fluorines improves some physicochemical parameters used to estimate the toxicity and environmental risk of these compounds, as well as the ligand-enzyme (GABAA receptor-chloride channel complex) interactions related to their insecticidal activity. This work is an effort to provide an improved new class of organofluorine pesticides.

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 information was included considering the optimised geometries and the docked structures of a series of disulfide compounds potentially useful as SARS-CoV protease inhibitors. The traditional analysis (based on flat-shape molecules) proved itself as the most effective technique, which means that, despite the undeniable importance of conformation for biomolecular behaviour, this type of information did not bring relevant contributions for MIA-QSAR modelling. Consequently, promising drug candidates were proposed on the basis of MIA-plot analyses, which account for PLS regression coefficients and variable importance in projection scores of the MIA-QSAR model.

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. Multivariate image analysis applied to QSAR (MIA-QSAR) is an example of 2D methodology that has presented a satisfactory performance in the generation of effective prediction models for biological/physicochemical properties. However, once this is a 2D method, conformational information is not explicitly considered, despite the well-known role of such type of information in explaining the biochemical behavior. Thus, the importance of conformation is undeniable, but the requirement of this information for QSAR analysis still needs to be studied. Therefore, this work aimed to provide a method for encoding 3D information into MIA-QSAR descriptors and analyze the consequences of this inclusion on this methodology. The strategy consisted in fully optimizing the molecular geometries of anti-HCV compounds and three-dimensionally align them before performing the MIA-QSAR procedure. As a result, it was possible to verify that this type of information does not improve the MIA-QSAR modeling performance; instead, the traditional procedure consisting of maximally congruent substructures generated a more reliable prediction model.

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. By means of contour maps obtained from the partial least squares regression coefficients and the variable importance in projection scores, key molecular moieties were targeted for possible structural modification, in order to obtain novel and more environmentally friendly insecticide candidates. The image-based descriptors applied encode molecular arrangement, atoms connectivity, groups size, and polarity; consequently, the findings in this work cannot be achieved by a simple relationship with hydrophobicity, usually described by the octanol-water partition coefficient.

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. The logBCF model was calibrated and vigorously validated (r(2) = 0.79, q(2) = 0.70 and rtest(2) = 0.81), providing a comprehensive three-parameter linear equation after variable selection (logBCF = 5.682 - 0.00233 × X9774 - 0.00070 × X813 - 0.00273 × X5144); the variables represent pixel coordinates in the multivariate image. Finally, chemical interpretation of the obtained models in terms of the structural characteristics responsible for the enhanced or reduced logBCF values was performed, providing key leads in the prospective development of more eco-friendly synthetic herbicides.