Simona Funar-Timofei

Quantitative estimation of pesticide-likeness for agrochemical discovery

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.

A tentative quantitative structure-toxicity relationship study of benzodiazepine drugs.

Benzodiazepines belong to a large family of drugs, being used as hypnotics, anxiolytics, tranquillizers, anticonvulsants, in pre-medication and intravenous sedation. Several quantitative structure-toxicity (lethal oral dose for mouse) relationship (QSTR) models for 54 benzodiazepine derivatives have been developed. The molecular structure of these compounds was energetically optimized by molecular mechanics calculations. To the lowest energy conformations thus obtained, quantum chemical calculations (RM1 approach) were applied to finally optimize the structures. Several structural descriptors, volumes, molecular surface area, hydrophobicities and quantum chemical descriptors were calculated from the minimized structures. Multiple linear regression (MLR) combined with genetic algorithm for variable selection, artificial neural networks (ANNs), support vector machines (SVMs) and partial least squares (PLS) have been employed. Few satisfactory MLR models with predictive power were obtained. Nonlinear modelling methods of ANNs and SVMs gave somewhat better models than those obtained by MLR using same set of descriptors. Additional information on the factors which influence the benzodiazepine toxicity was given by PLS. The obtained models can be used for a rough evaluation of benzodiazepine toxicity.

Correlations of limiting oxygen index with structural polyphosphoester features by QSPR approaches

Fire retardant materials diminish the hazard to life from fire. Polyphosphonates and polyphosphates display good flame retardancy and attractive plasticizing properties, being an important class of organophosphorus based polymer additives. Properties of previously synthesized polyphosphoesters are presented here, being simulated as monomers. In this quantitative structure–property relationship work, the flammability was expressed by limiting oxygen index (LOI) values, which were determined experimentally. Two types of chiral structures were found by molecular mechanics calculations using the MMFF94s force field for half of the monomers, consequently, two datasets were built. Structural parameters were calculated for the minimum energy structures and were related to the LOI values by multiple linear regression (MLR), artificial neural networks (ANNs), and support vector machines (SVMs). MLR calculations were combined with a genetic algorithm for variable selection. Stable and predictive MLR models in terms of 2D autocorrelation parameters weighed by atomic polarizabilities and of 3D-Morse descriptors were obtained. Somewhat inferior fits were found in the nonlinear modeling by ANNs and SVMs with the same set of descriptors. Monomers including R chiral structures gave more stable and predictive models compared to the S isomers in all approaches. Monomer geometry influences the flame retardancy, being favorable for R isomers.

Combined molecular docking and QSAR study of fused heterocyclic herbicide inhibitors of D1 protein in photosystem II of plants

Cinnoline, pyridine, pyrimidine, and triazine herbicides were found be inhibitors of the D1 protein in photosystem II (D1 PSII) electron transport of plants. The photosystem II inhibitory activity of these herbicides, expressed by experimental

Modeling of 2-Pyridin-3-yl-Benzo(d)(1,3)Oxazin-4-one Derivatives by Several Conformational Searching Tools and Molecular Docking

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Structure-flammability relationship study of phosphoester dimers by MLR and PLS

pIC50 values, was modeled by a docking and quantitative structure-activity relationships study. A conformer ensemble for each of the herbicide structure was generated using the MMFF94s force field. These conformers were further employed in a docking approach, which provided new information about the rational “active conformations” and various interaction patterns of the herbicide derivatives with D1 PSII. The most “active conformers” from the docking study were used to calculate structural descriptors, which were further related to the inhibitory experimental

Modelling heterocyclic azo dye affinities for cellulose fibres by computational approaches

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