A Comparative Study on the Constitutive Properties of Marketed Pesticides

Hao, Ge‐Fei; Dong, Qingjian; Yang, Guang‐Fu

doi:10.1002/minf.201100020

Cited by 94 publications

(111 citation statements)

References 38 publications

(31 reference statements)

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“…In our approach, we limit the number of descriptors to six basic physicochemical, independent, properties, correlated with pesticide bioavailability, solubility and stability [3,9,20,21]. These descriptors are included also in the formulation of QED [8] to define drug-likeness, and moreover, with a slight variation, i.e., count of aromatic rings – arR – replaced by count of aromatic bonds, the same properties were are encountered in Hao’s [14] approach to identity pesticides (see Table 1). …”

Section: Resultsmentioning

confidence: 99%

“…The three main classes of pesticides are: herbicides (against weeds), insecticides (against harmful insect pests), and fungicides (against harmful diseases) [12,14,22]. In this section, we will describe the way the above established pesticide class-specific desirability functions relate to each other.…”

Section: Resultsmentioning

confidence: 99%

“…In a more recent work Clarke [13] established upper limits of Abraham descriptors McGowan volume, hydrogen bond acidity and the hydrogen bond basicity. Investigating the constitutive properties of a representative library of marketed pesticides, from different periods of registration, Hao et al [14] defined simple and easy to implement rules for pesticide-likeness, by including molecular weight (MW), hydrophobicity (LogP), number of H-bond acceptors (HBA) and donors (HBD), number of rotatable bonds (RB) and number of aromatic bounds.…”

Section: Introductionmentioning

confidence: 99%

“…*MLogP [15] values were computed for Lipiniski’s [3] and Tice’s [11] rules and CLogP [16] values for Hao’s [14] according to the original publications.…”

Section: Introductionmentioning

confidence: 99%

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Quantitative estimation of pesticide-likeness for agrochemical discovery

et al. 2014

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BackgroundThe design of chemical libraries, an early step in agrochemical discovery programs, is frequently addressed by means of qualitative physicochemical and/or topological rule-based methods. The aim of this study is to develop quantitative estimates of herbicide- (QEH), insecticide- (QEI), fungicide- (QEF), and, finally, pesticide-likeness (QEP).In the assessment of these definitions, we relied on the concept of desirability functions.ResultsWe found a simple function, shared by the three classes of pesticides, parameterized particularly, for six, easy to compute, independent and interpretable, molecular properties: molecular weight, logP, number of hydrogen bond acceptors, number of hydrogen bond donors, number of rotatable bounds and number of aromatic rings. Subsequently, we describe the scoring of each pesticide class by the corresponding quantitative estimate. In a comparative study, we assessed the performance of the scoring functions using extensive datasets of patented pesticides.ConclusionsThe hereby-established quantitative assessment has the ability to rank compounds whether they fail well-established pesticide-likeness rules or not, and offer an efficient way to prioritize (class-specific) pesticides. These findings are valuable for the efficient estimation of pesticide-likeness of vast chemical libraries in the field of agrochemical discovery.Graphical AbstractQuantitative models for pesticide-likeness were derived using the concept of desirability functions parameterized for six, easy to compute, independent and interpretable, molecular properties: molecular weight, logP, number of hydrogen bond acceptors, number of hydrogen bond donors, number of rotatable bounds and number of aromatic rings.Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-014-0042-6) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…*MLogP [15] values were computed for Lipiniski’s [3] and Tice’s [11] rules and CLogP [16] values for Hao’s [14] according to the original publications.…”

Section: Introductionmentioning

confidence: 99%

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Quantitative estimation of pesticide-likeness for agrochemical discovery

et al. 2014

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“…Hao et al [42] published recently a new comparative study on the constitutive properties of newly marketed pesticides. He defined simple and easy to implement rules for pesticide-likeness, by including molecular weight (MW), lipophilicity (expressed as log P), number of H-bond acceptors (HBA) and donors (HBD), number of rotatable bonds (RB) and number of aromatic bounds.…”

Section: Similarity Of Drugs and Agrochemicalsmentioning

confidence: 99%

Potential of agricultural fungicides for antifungal drug discovery

Jampílek

2015

Expert Opinion on Drug Discovery

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While it is true that only a small fraction of fungal species are responsible for human mycoses, the increasing prevalence of fungal diseases has highlighted an urgent need to develop new antifungal drugs, especially for systemic administration. This contribution focuses on the similarities between agricultural fungicides and drugs. Inorganic, organometallic and organic compounds can be found amongst agricultural fungicides. Furthermore, fungicides are designed and developed in a similar fashion to drugs based on similar rules and guidelines, with fungicides also having to meet similar criteria of lead-likeness and/or drug-likeness. Modern approved specific-target fungicides are well-characterized entities with a proposed structureactivity relationships hypothesis and a defined mode of action. Extensive toxicological evaluation, including mammalian toxicology assays, is performed during the whole discovery and development process. Thus modern agrochemical research (design of modern agrochemicals) comes close to drug design, discovery and development. Therefore, modern specific-target fungicides represent excellent lead-like structures/models for novel drug design and development.

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Photo‐Stimuli Smart Supramolecular Self‐Assembly of Azobenzene/β‐Cyclodextrin Inclusion Complex for Controlling Plant Bacterial Diseases

Yang

Xiang

et al. 2023

Adv Funct Materials

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Controllable and on‐demand delivery of supramolecular systems have received considerable attention in modern agricultural management, especially for managing intractable plant diseases. Here, an intelligent photoresponsive pesticide delivery system is reported based on β‐cyclodextrin (β‐CD) and azobenzene, which overcomes the resistance of phytopathogens caused by the irrational use of conventional pesticides. Antibacterial bioassays illustrated that designed azobenzene derivative 3a possesses the most efficient bioactivity with EC50 values of 0.52–25.31 µg mL−1 toward three typical phytopathogens. Moreover, the assembly of the supramolecular binary complex 3a@β‐CD is successfully elucidated and displays exceptional inhibitory activity on biofilm formation. Of note, this supramolecular binary complex significantly improves the water solubility, foliar surface wettability, and shows marked light‐responsive properties. In vivo anti‐Xoo assays reveal that 3a@β‐CD has excellent control efficiency (protective activity: 51.22%, curative activity: 48.37%) against rice bacterial blight pathogens, and their control efficiency can be elevated to values of 55.84% (protective activity) and 52.05% (curative activity) by UV–vis exposure. In addition, the 3a@β‐CD are non‐toxic toward various non‐target organisms. This study therefore offers new insights into the potential of host‐guest complexes as a feasible pesticide discovery strategy characterized by a safe, biocompatible, light‐responsive release, and antibiofilm properties for overcoming intractable plant bacterial diseases.

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A Comparative Study on the Constitutive Properties of Marketed Pesticides

Cited by 94 publications

References 38 publications

Quantitative estimation of pesticide-likeness for agrochemical discovery

Quantitative estimation of pesticide-likeness for agrochemical discovery

Potential of agricultural fungicides for antifungal drug discovery

Photo‐Stimuli Smart Supramolecular Self‐Assembly of Azobenzene/β‐Cyclodextrin Inclusion Complex for Controlling Plant Bacterial Diseases

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