Predicting zebrafish (Danio rerio) embryo developmental toxicity through a non-conformational QSAR approach.

For many years, the frequent use of synthetic chemicals in the manufacture of veterinary drugs and plague control products has raised negative effects on human health and other non-target organisms, promoting the need to employ a practical and suitable methodology for early risk identification of several thousand commercial compounds. The zebrafish (Danio rerio) embryo has been emerged as one sustainable animal model for measuring developmental toxicity, an endpoint that is included in the regulatory procedures to approve chemicals, avoiding conventional and costly toxicity assays based on animal testing. In this context, the Quantitative Structure-Activity Relationships (QSAR) theory is applied to develop a predictive model based on a well-defined zebrafish embryo developmental toxicity database reported by the ToxCast™ Phase I chemical library of the Environmental Protection Agency (U.S. EPA). By means of four freely available softwares, a set with 28,038 non-conformational descriptors that encode the largest amount of permanent structural features are readily calculated. The Replacement Method (RM) variable subset selection technique provided the best regression models. Thereby, a linear QSAR model with proper statistical quality (Rtrain2 = 0.64, RMSEtrain = 0.49) is established in agreement with the Organization for Economic Co-operation and Development principles, accomplishing each internal (loo, l15 % o, VIF and Y-randomization) and external (Rtest2,Rm2, QF12, QF22, QF32 and CCC) validation criterion. The present QSAR approach provides a useful computational tool to estimate zebrafish developmental toxicity of new, untasted or hypothetical compounds, and it can contribute to the general lack of QSAR models in the literature to predict this endpoint.

The Use of the Index of Ideality of Correlation to Build Up Models for Bioconcentration Factor.

We establish a QSPR analysis for the bioconcentration factor of 851 heterogeneous structural compounds. Linear models are proposed via two different approaches: i. the optimal descriptor method implemented in CORAL, and ii. multivariable linear regressions on the best molecular descriptors found with the Replacement Method on 44,216 structural descriptors. Such variables are derived with different freely available softwares, such as PaDEL, PyDescriptor, Mold2 , QuBiLs-MAS and ISIDA/Fragmentor. The same validation set is employed in order to compare the predictive performance between the so-obtained CORAL and RM based models. Finally, the comparison of several models for the bioconcentration factor confirms the ability of the so-called index of ideality of correlation to be a criterion of predictive potential in Quantitative Structure-Property Relationships.

A non-conformational QSAR study for plant-derived larvicides against Zika Aedes aegypti L. vector.

A set of 263 plant-derived compounds with larvicidal activity against Aedes aegypti L. (Diptera: Culicidae) vector is collected from the literature, and is studied by means of a non-conformational quantitative structure-activity relationships (QSAR) approach. The balanced subsets method (BSM) is employed to split the complete dataset into training, validation and test sets. From 26,775 freely available molecular descriptors, the most relevant structural features of compounds affecting the bioactivity are taken. The molecular descriptors are calculated through four different freewares, such as PaDEL, Mold2, EPI Suite and QuBiLs-MAS. The replacement method (RM) variable subset selection technique leads to the best linear regression models. A successful QSAR equation involves 7-conformation-independent molecular descriptors, fulfiling the evaluated internal (loo, l30%o, VIF and Y-randomization) and external (test set with Ntest = 65 compounds) validation criteria. The practical application of this QSAR model reveals promising predicted values for some natural compounds with unknown experimental larvicidal activity. Therefore, the present model constitutes the first one based on a large molecular set, being a useful computational tool for identifying and guiding the synthesis of new active molecules inspired by natural products.

Quantitative structure-activity relationship (QSAR) analysis of plant-derived compounds with larvicidal activity against Zika Aedes aegypti (Diptera: Culicidae) vector using freely available descriptors.

BACKGROUND: We have developed a quantitative structure-activity relationship (QSAR) model for predicting the larvicidal activity of 60 plant-derived molecules against Aedes aegypti L. (Diptera: Culicidae), a vector of several diseases such as dengue, yellow fever, chikungunya and Zika. The balanced subsets method (BSM) based on k-means cluster analysis (k-MCA) was employed to split the data set. The replacement method (RM) variable subset selection technique coupled with multivariable linear regression (MLR) proved to be successful for exploring 18 326 molecular descriptors and fingerprints calculated with PaDEL, Mold2 and EPI Suite open-source softwares. RESULTS: A robust QSAR model (Rtrain2=0.84, Strain = 0.20 and Rtest2=0.92, Stest = 0.23) involving five non-conformational descriptors was established. The model was validated and tested through the use of an external test set of compounds, the leave-one-out (LOO) and leave-more-out (LMO) cross-validation methods, Y-randomization and applicability domain (AD) analysis. CONCLUSION: The QSAR model surpasses previously published models based on geometrical descriptors, thereby representing a suitable tool for predicting larvicidal activity against the vector A. aegypti using a conformation-independent approach. © 2018 Society of Chemical Industry.

The quantitative structure-insecticidal activity relationships from plant derived compounds against chikungunya and zika Aedes aegypti (Diptera:Culicidae) vector.

The insecticidal activity of a series of 62 plant derived molecules against the chikungunya, dengue and zika vector, the Aedes aegypti (Diptera:Culicidae) mosquito, is subjected to a Quantitative Structure-Activity Relationships (QSAR) analysis. The Replacement Method (RM) variable subset selection technique based on Multivariable Linear Regression (MLR) proves to be successful for exploring 4885 molecular descriptors calculated with Dragon 6. The predictive capability of the obtained models is confirmed through an external test set of compounds, Leave-One-Out (LOO) cross-validation and Y-Randomization. The present study constitutes a first necessary computational step for designing less toxic insecticides.

Quantitative Structure-Antifungal Activity Relationships for cinnamate derivatives.

Quantitative Structure-Activity Relationships (QSAR) are established with the aim of analyzing the fungicidal activities of a set of 27 active cinnamate derivatives. The exploration of more than a thousand of constitutional, topological, geometrical and electronic molecular descriptors, which are calculated with Dragon software, leads to predictions of the growth inhibition on Pythium sp and Corticium rolfsii fungi species, in close agreement to the experimental values extracted from the literature. A set containing 21 new structurally related cinnamate compounds is prepared. The developed QSAR models are applied to predict the unknown fungicidal activity of this set, showing that cinnamates like 38, 28 and 42 are expected to be highly active for Pythium sp, while this is also predicted for 28 and 34 in C. rolfsii.

QSAR analysis on tacrine-related acetylcholinesterase inhibitors.

BACKGROUND: The evaluation of the clinical effects of Tacrine has shown efficacy in delaying the deterioration of the symptoms of Alzheimer's disease, while confirming the adverse events consisting mainly in the elevated liver transaminase levels. The study of tacrine analogs presents a continuous interest, and for this reason we establish Quantitative Structure-Activity Relationships on their Acetylcholinesterase inhibitory activity. RESULTS: Ten groups of new developed Tacrine-related inhibitors are explored, which have been experimentally measured in different biochemical conditions and AChE sources. The number of included descriptors in the structure-activity relationship is characterized by 'Rule of Thumb'. The 1502 applied molecular descriptors could provide the best linear models for the selected Alzheimer's data base and the best QSAR model is reported for the considered data sets. CONCLUSION: The QSAR models developed in this work have a satisfactory predictive ability, and are obtained by selecting the most representative molecular descriptors of the chemical structure, represented through more than a thousand of constitutional, topological, geometrical, quantum-mechanical and electronic descriptor types.