RESUMO
In this work, the vapor pressure of pesticides is employed as an indicator of their volatility potential. Quantitative Structure-Property Relationship models are established to predict the classification of compounds according to their volatility, into the high and low binary classes separated by the 1-mPa limit. A large dataset of 1005 structurally diverse pesticides with known experimental vapor pressure data at 20 °C is compiled from the publicly available Pesticide Properties DataBase (PPDB) and used for model development. The freely available PaDEL-Descriptor and ISIDA/Fragmentor molecular descriptor programs provide a large number of 19,947 non-conformational molecular descriptors that are analyzed through multivariable linear regressions and the Replacement Method technique. Through the selection of appropriate molecular descriptors of the substructure fragment type and the use of different standard classification metrics of model's quality, the classification of the structure-property relationship achieves acceptable results for discerning between the high and low volatility classes. Finally, an application of the obtained QSPR model is performed to predict the classes for 504 pesticides not having experimentally measured vapor pressures.
Assuntos
Praguicidas , Pressão de Vapor , Praguicidas/química , Relação Quantitativa Estrutura-Atividade , Modelos LinearesRESUMO
In advanced water treatment processes, the degradation efficiency of contaminants depends on the reactivity of the hydroxyl radical toward a target micropollutant. The present study predicts the hydroxyl radical rate constant in water (k OH) for 118 emerging micropollutants, by means of quantitative structure-property relationships (QSPR). The conformation-independent QSPR approach is employed, together with a large number of 15,251 molecular descriptors derived with the PaDEL, Epi Suite, and Mold2 freewares. The best multivariable linear regression (MLR) models are found with the replacement method variable subset selection technique. The proposed five-descriptor model has the following statistics for the training set: [Formula: see text], RMS train = 0.21, while for the test set is [Formula: see text], RMS test = 0.11. This QSPR serves as a rational guide for predicting oxidation processes of micropollutants.
Assuntos
Radical Hidroxila/química , Modelos Teóricos , Poluentes Químicos da Água/química , Purificação da Água/métodos , Modelos Lineares , Conformação Molecular , Oxirredução , Relação Quantitativa Estrutura-AtividadeRESUMO
The ANTARES dataset is a large collection of known and verified experimental bioconcentration factor data, involving 851 highly heterogeneous compounds from which 159 are pesticides. The BCF ANTARES data were used to derive a conformation-independent QSPR model. A large set of 27,017 molecular descriptors was explored, with the main intention of capturing the most relevant structural characteristics affecting the studied property. The structural descriptors were derived with different freeware tools, such as PaDEL, Epi Suite, CORAL, Mold2, RECON, and QuBiLs-MAS, and so it was interesting to find out the way that the different descriptor tools complemented each other in order to improve the statistical quality of the established QSPR. The best multivariable linear regression models were found with the Replacement Method variable sub-set selection technique. The proposed QSPR model improves previous reported models of the bioconcentration factor in the present dataset.
Assuntos
Biodegradação Ambiental , Compostos Orgânicos/química , Relação Quantitativa Estrutura-Atividade , Modelos Lineares , Modelos Químicos , Conformação Molecular , Medição de RiscoRESUMO
We predict the soil sorption coefficient for a heterogeneous set of 643 organic non-ionic compounds by means of Quantitative Structure-Property Relationships (QSPR). A conformation-independent representation of the chemical structure is established. The 17,538 molecular descriptors derived with PaDEL and EPI Suite softwares are simultaneously analyzed through linear regressions obtained with the Replacement Method variable subset selection technique. The best predictive three-descriptors QSPR is developed on a reduced training set of 93 chemicals, having an acceptable predictive capability on 550 test set compounds. We also establish a model with a single optimal descriptor derived from CORAL freeware. The present approach compares fairly well with a previously reported one that uses Dragon descriptors.
Assuntos
Praguicidas/química , Poluentes do Solo/química , Solo/química , Adsorção , Biodegradação Ambiental , Formaldeído/química , Modelos Químicos , Conformação Molecular , Relação Quantitativa Estrutura-Atividade , Medição de Risco , SolubilidadeRESUMO
The pharmacokinetic properties of flavonoids with differing degrees of lipophilicity were investigated using immobilized artificial membranes (IAMs) as the stationary phase in high performance liquid chromatography (HPLC). For each flavonoid compound, we investigated whether the type of column used affected the correlation between the retention factors and the calculated octanol/water partition (log Poct). Three-dimensional (3D) molecular descriptors were calculated from the molecular structure of each compound using i) VolSurf software, ii) the GRID method (computational procedure for determining energetically favorable binding sites in molecules of known structure using a probe for calculating the 3D molecular interaction fields, between the probe and the molecule), and iii) the relationship between partition and molecular structure, analyzed in terms of physicochemical descriptors. The VolSurf built-in Caco-2 model was used to estimate compound permeability. The extent to which the datasets obtained from different columns differ both from each other and from both the calculated log Poct and the predicted permeability in Caco-2 cells was examined by principal component analysis (PCA). The immobilized membrane partition coefficients (kIAM) were analyzed using molecular descriptors in partial least square regression (PLS) and a quantitative structure-retention relationship was generated for the chromatographic retention in the cholesterol column. The cholesterol column provided the best correlation with the permeability predicted by the Caco-2 cell model and a good fit model with great prediction power was obtained for its retention data (R(2)=0.96 and Q(2)=0.85 with four latent variables).
Assuntos
Antineoplásicos/química , Antineoplásicos/farmacologia , Cromatografia/métodos , Flavonoides/química , Flavonoides/farmacologia , Membranas Artificiais , Células CACO-2 , Humanos , Estrutura Molecular , Permeabilidade , Relação Quantitativa Estrutura-AtividadeRESUMO
Drugs acting on the central nervous system (CNS) have to cross the blood-brain barrier (BBB) in order to perform their pharmacological actions. Passive BBB diffusion can be partially expressed by the blood/brain partition coefficient (logBB). As the experimental evaluation of logBB is time and cost consuming, theoretical methods such as quantitative structure-property relationships (QSPR) can be useful to predict logBB values. In this study, a 2D-QSPR approach was applied to a set of 28 drugs acting on the CNS, using the logBB property as biological data. The best QSPR model [n = 21, r = 0.94 (r² = 0.88), s = 0.28, and Q² = 0.82] presented three molecular descriptors: calculated n-octanol/water partition coefficient (ClogP), polar surface area (PSA), and polarizability (α). Six out of the seven compounds from the test set were well predicted, which corresponds to good external predictability (85.7%). These findings can be helpful to guide future approaches regarding those molecular descriptors which must be considered for estimating the logBB property, and also for predicting the BBB crossing ability for molecules structurally related to the investigated set.
Fármacos que atuam no sistema nervoso central (SNC) devem atravessar a barreira hematoencefálica (BHE) para exercerem suas ações farmacológicas. A difusão passiva através da BHE pode ser parcialmente expressa pelo coeficiente de partição entre os compartimentos encefálico e sanguíneo (logBB, brain/blood partition coefficient). Considerando-se que a avaliação experimental de logBB é dispendiosa e demorada, métodos teóricos como estudos das relações entre estrutura química e propriedade (QSPR, Quantitative Structure-Property Relationships) podem ser utilizados na previsão dos valores de logBB. Neste estudo, uma abordagem de QSPR-2D foi aplicada a um conjunto de 28 moléculas com ação central, usando logBB como propriedade biológica. O melhor modelo de QSPR [n = 21, r = 0,94 (r² = 0,88), s = 0,28 e Q² = 0,82] apresentou três descritores moleculares: o coeficiente calculado de partição n-octanol/água (ClogP), área de superfície polar (PSA) e polarizabilidade (α). Seis dos sete compostos do conjunto de avaliação foram bem previstos pelo modelo, o que corresponde a um bom poder de previsão externa (85,7%). Os resultados obtidos podem auxiliar de forma relevante em estudos futuros, orientando quais descritores moleculares devem ser considerados para estimar logBB e prever a passagem através da BHE de moléculas estruturalmente relacionadas às do conjunto investigado.