QSAR-Guided Proposition of N-(4-methanesulfonyl)Benzoyl-N'-(Pyrimidin-2-yl)Thioureas as Effective and Safer Herbicides.
Natânia E RodriguesAdriana C de FariaIngrid V PereiraElaine F F da CunhaMatheus P FreitasPublished in: Bulletin of environmental contamination and toxicology (2022)
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 r 2 pred 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 R 1 and R 2 positions modulate the agrochemical (R 1 = Cl increases and R 2 = OR decreases bioactivity) and environmental friendship (particularly with R 2 = OH) performances of this class of compounds.
Keyphrases
- molecular docking
- molecular dynamics
- molecular dynamics simulations
- polycyclic aromatic hydrocarbons
- structure activity relationship
- gas chromatography
- data analysis
- oxidative stress
- mass spectrometry
- big data
- electronic health record
- electron transfer
- fluorescent probe
- living cells
- sensitive detection
- oxide nanoparticles
- protein protein
- machine learning
- risk assessment
- case control
- deep learning
- genome wide identification
- quantum dots