Fluorinated benzoxazinones designed via MIA-QSAR, docking and molecular dynamics as protoporphyrinogen IX oxidase inhibitors.

BACKGROUND: Fluorine plays a significant role in agrochemical science because approximately 25% of herbicides licensed worldwide contain this element. In a pool of previously synthesized benzoxazinones, some compounds contained fluorine and demonstrated inhibitory activities against protoporphyrinogen IX oxidase (PPO). Therefore, three data sets of benzoxazinone derivatives with known inhibitory activity against PPO were employed to build a multivariate image analysis applied to a quantitative structure-activity relationships (MIA-QSAR) model to identify improved analogs with at least one fluorine substituent. RESULTS: The QSAR model was vigorously validated and demonstrated to be highly predictive (r2 = 0.85, q2 = 0.71, and r2 pred = 0.88); thus, the model can provide reliable estimations for the PPO inhibitory activity of unknown derivatives. From these compounds, a couple of N-substituted benzoxazinones that contained the -CH2CHF2 group were found with predicted pKi values larger than 8 (Ki in mol L-1) and higher lipophilicity than the most active data set compounds. In addition, we carried out a systematic investigation of the binding mode of PPO by performing computational docking followed by molecular dynamics simulations. The proposed binding mode was consistent with experimental studies, and several potential key residues were identified. CONCLUSION: Two new proposed benzoxazinones exhibited better performance than compounds of the data set, and fluorine substituents played pivotal roles in describing the biological activities. © 2024 Society of Chemical Industry.

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.

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.

QSAR-Guided Proposition of N-(4-methanesulfonyl)Benzoyl-N'-(Pyrimidin-2-yl)Thioureas as Effective and Safer Herbicides.

Chlorinated agrochemicals play a major role in toxicity due especially to the labile C - Cl bond and high lipophilicity of organochlorines. In turn, urea and thiourea herbicides are widely used for weed control. A series of substituted N-benzoyl-N'-pyrimidin-2-yl thioureas has been recently synthesized and tested against Brassica napus L., demonstrating promising herbicidal activities, particularly for chlorinated derivatives. We have therefore modeled these activities using multivariate image analysis applied to quantitative structure-activity relationships (MIA-QSAR) to find out a significant and reliable correlation between measured and predicted inhibition of B. napus L. root growth (%) and, ultimately, to propose effective, non-chlorinated and/or less lipophilic N-(4-methanesulfonyl)benzoyl-N'-(pyrimidin-2-yl)thiourea candidates. The model was found to be predictive, giving an average r2pred in the external validation of 0.833. The predicted data for the proposed herbicides, interpreted in terms of MIA-plots of the chemical moieties responsible for bioactivity and supported by docking studies towards the photosystem II enzyme, suggest that substituents at both R1 and R2 positions modulate the agrochemical (R1 = Cl increases and R2 = OR decreases bioactivity) and environmental friendship (particularly with R2 = OH) performances of this class of compounds.