Machine learning prediction of the potential pesticide applicability of three dihydroquinoline derivatives: Syntheses, crystal structures and physical properties

Systematic approaches to machine learning models for predicting pesticide toxicity

Pesticides play an important role in modern agriculture by protecting crops from pests and diseases. However, the negative consequences of pesticides, such as environmental contamination and adverse effects on human and ecological health, underscore the importance of accurate toxicity predictions. To address this issue, artificial intelligence models have emerged as valuable methods for predicting the toxicity of organic compounds. In this review article, we explore the application of machine learning (ML) for pesticide toxicity prediction. This review provides a detailed summary of recent developments, prediction models, and datasets used for pesticide toxicity prediction. In this analysis, we compared the results of several algorithms that predict the harmfulness of various classes of pesticides. Furthermore, this review article identified emerging trends and areas for future direction, showcasing the transformative potential of machine learning in promoting safer pesticide usage and sustainable agriculture.

Application and Progress of Machine Learning in Pesticide Hazard and Risk Assessment

Long-term exposure to pesticides is associated with the incidence of cancer. With the exponential increase in the number of new pesticides being synthesized, it becomes more and more important to evaluate the toxicity of pesticides by means of simulated calculations. Based on existing data, machine learning methods can train and model the predictions of the effects of novel pesticides, which have limited available data. Combined with other technologies, this can aid the synthesis of new pesticides with specific active structures, detect pesticide residues, and identify their tolerable exposure levels. This article mainly discusses support vector machines, linear discriminant analysis, decision trees, partial least squares, and algorithms based on feedforward neural networks in machine learning. It is envisaged that this article will provide scientists and users with a better understanding of machine learning and its application prospects in pesticide toxicity assessment.

Complexation and enantioselectivity of novel bridge-like uranyl- 2-((1Z,9Z)-9-(2-Hydroxyphenyl)-3,5,6,8-tetrahydrobenzo[h][1,4,7,10] dioxadiazacyclododecin-2-yl)-5-methoxyphenol with chiral organophosphorus pesticide enantiomers of R/S-malathions

Designing new uranyl complexes with enantioselectivity is of great significance for the identification and separation of enantiomers of chiral pesticides. In this paper, a new asymmetric rigid uranyl-2-((1Z,9Z)-9-(2-Hydroxyphenyl)-3,5,6,8-tetrahydrobenzo[h][1,4,7,10] dioxadiaza-cyclododecin-2-yl)-5-methoxyphenol(Uranyl-HTDM) was designed, we used Uranyl-HTDM as a receptor to selectively coordinate with the guests of the chiral organophosphorus pesticide R/S-malathions(R/S-MLTs) to explore the receptor's enatioselectivity recognition of the chiral guests of R/S-MLTs. Density functional theory (DFT) method was used to comprehensively study the complexation mode of the receptor with enantiomers. The results showed that the U of Uranyl-HTDM could coordinate with both the thiophosphoryl sulfur and carbonyl oxygens of R/S-MLTs in different environments, respectively. The thermodynamics calculations further indicated that the receptor could selectively recognize the thiophosphoryl sulfur and carbonyl oxygen atoms of R/S-malathions, and the complexation abilities of Uranyl-HTDM to the R/S-malathions under different solvents were not the same. The smaller the polarity of solvents, the stronger the complexation ability of Uranyl-HTDM with R-malathion, toluene was an ideal solvent with large △G change and enatioselectivity coefficient of 99.55%. The study provides useful references for the design of new uranyl-salophens and for the experimental study on the molecular recognition of chiral organophosphorus pesticides.

A new quinolinone-chalcone hybrid with potential antibacterial and herbicidal properties using in silico approaches

Quinolinone-chalcones are hybrid compounds consisting of chalcone and quinolone moieties with biological activity related to their hybrid structure. This work seeks to describe the structural and theoretical parameters related to the physicochemical properties and biological activity of a new quinolinone-chalcone. The synthesis, structural characterization by X-ray diffraction, molecular topology by Hirshfeld surfaces and QTAIM, molecular electronic calculations, and pharmacophore analysis were described. The weak interactions C-H^…O, C-H^…π, and C-H^…Br were responsible for crystal growth and stabilized the crystalline state. The DFT analysis shows that the sulfonamide group region is susceptible to observed interactions, and the frontier molecular orbitals indicate high kinetic stability. Also, pharmacophore analysis revealed potential antibacterial and herbicidal activity; by docking within the active site of TtgR, a transcription regulator for the efflux pump TtgABC from the highly resistant Pseudomonas putida (P. putida) strain DOT-TIE, we showed that the activation of TtgR relies upon the binding of aromatic-harboring compounds, which plays a crucial role in bacterial evasion. In this context, a new quinolinone-chalcone has a higher binding affinity than tetracycline, which suggests it might be a better effector for TtgR.

A Review on Prediction Models for Pesticide Use, Transmission, and Its Impacts

The lure of increased productivity and crop yield has caused the imprudent use of pesticides in great quantity that has unfavorably affected environmental health. Pesticides are chemicals intended for avoiding, eliminating, and mitigating any pests that affect the crop. Lack of awareness, improper management, and negligent disposal of pesticide containers have led to the permeation of pesticide residues into the food chain and other environmental pathways, leading to environmental degradation. Sufficient steps must be undertaken at various levels to monitor and ensure judicious use of pesticides. Development of prediction models for optimum use of pesticides, pesticide management, and their impact would be of great help in monitoring and controlling the ill effects of excessive use of pesticides. This paper aims to present an exhaustive review of the prediction models developed and modeling strategies used to optimize the use of pesticides.

Structural comparison of five new halogenated dihydroquinoline-4(1H)-ones

Compounds with dihydroquinoline-4(1H)-one nuclei have been reported in the literature for being important in the development of medicines due to their broad spectrum of activities. In this way, the structural knowledge of this class becomes relevant for obtaining new materials with desired biological properties. This study presents the structural elucidation of five halogenated dihydroquinolines, as well as the discussion about the effect on the molecular conformation of the type and position of halogen atom on aromatic rings. Compounds I and IV differ in halogen substitution on 2-phenyl ring, while compounds III and V differ in halogen substitution on the benzylidene ring. Moreover, compound II has a para-substituted 2-phenyl ring in their molecular structure. The crystal packing of all five molecules is mainly ruled by C–H⋯O and C–H···halogen interactions that form dimers and chains. The shift in position and the kind of the halogen in ring C shows a starring role in the conformation of the studied compounds, and the packaging of these compounds is more susceptible to variations when the halogen position changes.

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Five dihydroquinoline-4(1H)-ones have been synthesized by varying the halogenated substituents.

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Different substituents lead to distinct number of molecules at asymmetric unit.

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The Molecular packing is stabilized by C–H⋯O and C–H···Halogen hydrogen bonds.

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The change in position and the halogen atom attached in ring C play a significant role in the conformation.

In silico-driven identification of novel molluscicides effective against Biomphalaria glabrata (Say, 1818)

Schistosomiasis control in endemic areas depends on several factors, including mass drug delivery programs and interrupting the transmission of disease by controlling the intermediate host snails in the freshwater ecosystem using molluscicides.