New Hybrid Genetic Based Support Vector Regression as QSAR Approach for Analyzing Flavonoids-GABA(A) Complexes

Resonance structure contributions, flexibility, and frontier molecular orbitals (HOMO-LUMO) of pelargonidin, cyanidin, and delphinidin throughout the conformational space: application to antioxidant and antimutagenic activities

This research refers to the study and understanding of the conformational space of the positive-charged anthocyanidin structures in relation with the known chemical reactivities and bioactivities of these compounds. Therefore, the planar (P) and nonplanar (Z) conformers of the three hydroxylated anthocyanidins pelargonidin, cyanidin, and delphinidin were analyzed throughout the conformational space at the B3LYP/6-311 ++ G** level of theory. The outcome displayed eleven new conformers for pelargonidin, fifty-four for cyanidin, and thirty-one for delphinidin. Positive-charged quinoidal structures showed a significant statistical weight in the conformational space, thus coexisting simultaneously with other resonance structures, such that under certain reaction conditions, the anthocyanidins behave as positive-charged quinoidal structures instead of oxonium salts. The calculations of the permanent dipole moment and the polarizability showed relationships with the quantity and arrangement of hydroxyls in the structure. In addition, theoretical calculations were used to analyze the frontier molecular orbitals (HOMO-LUMO) of the three anthocyanidins. The novel conception of this work lies in the fact that dipole moment, polarizability, and HOMO-LUMO values were related to the reactivity/bioactivity of these three anthocyanidins. HOMO-LUMO energy gaps were useful to explain the antioxidant activity, while the percent atom contributions to HOMO were appropriate to demonstrate the antimutagenic activity as enzyme inhibitors, as well as the steric and electrostatic requirements to form the pharmacophore. Delphinidin was the strongest antioxidant anthocyanidin, and pelargonidin the best anthocyanidin with antimutagenic activity.

A joint optimization QSAR model of fathead minnow acute toxicity based on a radial basis function neural network and its consensus modeling

Acute toxicity of the fathead minnow (Pimephales promelas) is an important indicator to evaluate the hazards and risks of compounds in aquatic environments. The aim of our study is to explore the predictive power of the quantitative structure-activity relationship (QSAR) model based on a radial basis function (RBF) neural network with the joint optimization method to study the acute toxicity mechanism, and to develop a potential acute toxicity prediction model, for fathead minnow. To ensure the symmetry and fairness of the data splitting and to generate multiple chemically diverse training and validation sets, we used a self-organizing mapping (SOM) neural network to split the modeling dataset (containing 955 compounds) characterized by PaDEL-descriptors. After preliminary selection of descriptors via the mean decrease impurity method, a hybrid quantum particle swarm optimization (HQPSO) algorithm was used to jointly optimize the parameters of RBF and select the key descriptors. We established 20 RBF-based QSAR models, and the statistical results showed that the 10-fold cross-validation results (R cv10 2) and the adjusted coefficients of determination (R adj 2) were all great than 0.7 and 0.8, respectively. The Q ext 2 of these models was between 0.6480 and 0.7317, and the R ext 2 was between 0.6563 and 0.7318. Combined with the frequency and importance of the descriptors used in RBF-based models, and the correlation between the descriptors and acute toxicity, we concluded that the water distribution coefficient, molar refractivity, and first ionization potential are important factors affecting the acute toxicity of fathead minnow. A consensus QSAR model with RBF-based models was established; this model showed good performance with R 2 = 0.9118, R cv10 2 = 0.7632, and Q ext 2 = 0.7430. A frequency weighted and distance (FWD)-based application domain (AD) definition method was proposed, and the outliers were analyzed carefully. Compared with previous studies the method proposed in this paper has obvious advantages and its robustness and external predictive power are also better than Xgboost-based model. It is an effective QSAR modeling method.

Awareness and current knowledge of epilepsy

Epilepsy is a severe neural disorder that affects approximately fifty million individuals globally. Despite the fact that for most of the people with epilepsy, convulsions are better controlled by current accessible antiepileptic medicines, yet there are more than 30% of individuals affected with medically intractable epilepsy and around 30-40% of all patients with epilepsy affected by many adverse reactions and convulsion resistance to the present antiepileptic drugs. Consequently, various scientists attempt to develop new strategies to treat epilepsy, for instance, to find out novel antiepileptic ingredients from traditional medicines. This work aims to present a complete summary of natural medicines prescribed as antiepileptic agents all over the world by ethnic groups and different tribes. We undertook an extensive bibliographic analysis by searching peer reviewed papers and classical textbooks and further consulting well accepted worldwide scientific databases. We carried out PubMed, EMbase and CENTRAL searches by means of terms such as "antiepileptic" and "anti-convulsant" activity of plants. Medicinal plants have been prescribed to treat epilepsy and have been recognized as antiepileptic medicines. In this review, a variety of herbs have been reviewed for thorough studies such as Cuminum cyminum, Butea monosperma, Solanum americanum, Anacyclus pyrethrum, Leonotis leonurus, Elaeocarpus ganitrus and Angelica archangelica. This paper shows that it was high time experimental studies are increased to obtain novel potential active principles from medicinal plants. Plant extracts and their chemical constituents should be further evaluated to clarify their mechanisms of action. This paper provides a solid base upon which to further investigate the clinical efficacy of medicinal plants that are both currently prescribed by physicians as traditional antiepileptic agents, but also could be effective as an antiepileptic drug with further research and study.

Conformation-Independent QSPR Approach for the Soil Sorption Coefficient of Heterogeneous Compounds

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.

Application of quantitative structure-property relationship analysis to estimate the vapor pressure of pesticides

The application of molecular descriptors in describing Quantitative Structure Property Relationships (QSPR) for the estimation of vapor pressure (VP) of pesticides is of ongoing interest. In this study, QSPR models were developed using multiple linear regression (MLR) methods to predict the vapor pressure values of 162 pesticides. Several feature selection methods, namely the replacement method (RM), genetic algorithms (GA), stepwise regression (SR) and forward selection (FS), were used to select the most relevant molecular descriptors from a pool of variables. The optimum subset of molecular descriptors was used to build a QSPR model to estimate the vapor pressures of the selected pesticides. The Replacement Method improved the predictive ability of vapor pressures and was more reliable for the feature selection of these selected pesticides. The results provided satisfactory MLR models that had a satisfactory predictive ability, and will be important for predicting vapor pressure values for compounds with unknown values. This study may open new opportunities for designing and developing new pesticide.

Free variable selection QSPR study to predict (19)F chemical shifts of some fluorinated organic compounds using Random Forest and RBF-PLS methods

In this work, two new and powerful chemometrics methods are applied for the modeling and prediction of the (19)F chemical shift values of some fluorinated organic compounds. The radial basis function-partial least square (RBF-PLS) and random forest (RF) are employed to construct the models to predict the (19)F chemical shifts. In this study, we didn't used from any variable selection method and RF method can be used as variable selection and modeling technique. Effects of the important parameters affecting the ability of the RF prediction power such as the number of trees (nt) and the number of randomly selected variables to split each node (m) were investigated. The root-mean-square errors of prediction (RMSEP) for the training set and the prediction set for the RBF-PLS and RF models were 44.70, 23.86, 29.77, and 23.69, respectively. Also, the correlation coefficients of the prediction set for the RBF-PLS and RF models were 0.8684 and 0.9313, respectively. The results obtained reveal that the RF model can be used as a powerful chemometrics tool for the quantitative structure-property relationship (QSPR) studies.

QSAR models for thiophene and imidazopyridine derivatives inhibitors of the Polo-Like Kinase 1

The inhibitory activity of 103 thiophene and 33 imidazopyridine derivatives against Polo-Like Kinase 1 (PLK1) expressed as pIC50 (-logIC50) was predicted by QSAR modeling. Multivariate linear regression (MLR) was employed to model the relationship between 0D and 3D molecular descriptors and biological activities of molecules using the replacement method (MR) as variable selection tool. The 136 compounds were separated into several training and test sets. Two splitting approaches, distribution of biological data and structural diversity, and the statistical experimental design procedure D-optimal distance were applied to the dataset. The significance of the training set models was confirmed by statistically higher values of the internal leave one out cross-validated coefficient of determination (Q2) and external predictive coefficient of determination for the test set (Rtest2). The model developed from a training set, obtained with the D-optimal distance protocol and using 3D descriptor space along with activity values, separated chemical features that allowed to distinguish high and low pIC50 values reasonably well. Then, we verified that such model was sufficient to reliably and accurately predict the activity of external diverse structures. The model robustness was properly characterized by means of standard procedures and their applicability domain (AD) was analyzed by leverage method.

Binary classification of chalcone derivatives with LDA or KNN based on their antileishmanial activity and molecular descriptors selected using the Successive Projections Algorithm feature-selection technique

Chalcones are naturally occurring aromatic ketones, which consist of an α-, β-unsaturated carbonyl system joining two aryl rings. These compounds are reported to exhibit several pharmacological activities, including antiparasitic, antibacterial, antifungal, anticancer, immunomodulatory, nitric oxide inhibition and anti-inflammatory effects. In the present work, a Quantitative Structure-Activity Relationship (QSAR) study is carried out to classify chalcone derivatives with respect to their antileishmanial activity (active/inactive) on the basis of molecular descriptors. For this purpose, two techniques to select descriptors are employed, the Successive Projections Algorithm (SPA) and the Genetic Algorithm (GA). The selected descriptors are initially employed to build Linear Discriminant Analysis (LDA) models. An additional investigation is then carried out to determine whether the results can be improved by using a non-parametric classification technique (One Nearest Neighbour, 1NN). In a case study involving 100 chalcone derivatives, the 1NN models were found to provide better rates of correct classification than LDA, both in the training and test sets. The best result was achieved by a SPA-1NN model with six molecular descriptors, which provided correct classification rates of 97% and 84% for the training and test sets, respectively.

Pharmacophore models for GABA(A) modulators: implications in CNS drug discovery

IMPORTANCE OF THE FIELD

GABA(A) ion channel is a validated drug target, implicated in the pathophysiology of various neurological and psychiatric disorders. Structural investigations on GABA(A) are currently precluded in the absence of experimentally resolved structure. Pharmacophore modeling circumvents such issues and proves to be a powerful and successful method in drug discovery.

AREAS COVERED IN THIS REVIEW

The present reviews encompass pharmacophoric models available in the literature for the orthosteric GABA and the allosteric benzodiazepine binding site. Success stories from these simplistic pharmacophore models in scaffold hopping and strategic lead optimization have been highlighted. Recent advances in pharmacophore modeling that can leverage CNS drug discovery programs and deliver astounding results have been reviewed.

WHAT THE READER WILL GAIN

Readers are bound to gain a comprehensive insight on different computational techniques used by different groups to arrive at simple, yet sophisticated pharmacophore models. In the absence of experimentally unresolved active site geometry of GABA(A), these models will provide the reader an opportunity to translate these pharmacophoric features to the microscopic phenomenon of supramolecular ligand interaction.

TAKE HOME MESSAGE

Pharmacophore modeling has now evolved as a mainstay approach for lead generation and optimization in drug discovery programs. Of late, many advances in pharmacophore perception have emerged. Such advancements should be used to confront activity profiling and early stage risk assessment in a high-throughput fashion. Extending such technologies has the potential not only to reduce time and cost, but also to prevent late stage attrition in drug discovery.

Quantitative structure-activity relationships of mosquito larvicidal chalcone derivatives

The mosquito larvicidal activities of a series of chalcones and some derivatives were subjected to a quantitative structure-activity relationship (QSAR) study, using more than a thousand constitutional, topological, geometrical, and electronic molecular descriptors calculated with Dragon software. The larvicidal activity values for 28 active compounds of the series were predicted, showing in general a good approximation to the experimental values found in the literature. Chalcones having one or both electron-rich rings showed high toxicity. However, the activity of chalcones was reduced by electron-withdrawing groups, and this was roughly diminished by derivatization of the carbonyl group. A set of six chalcones being structurally similar to some of the active ones, with a still unknown larvicidal activity, were prepared. Their activity values were predicted by applying the developed QSAR models, showing that two chalcones of such set, both 32 and 34, were expected to be highly active.

A comparative QSAR on 1,2,5-thiadiazolidin-3-one 1,1-dioxide compounds as selective inhibitors of human serine proteinases

Selective inhibitors of target serine proteinases have a potential therapeutic role for the treatment of various inflammatory and related diseases. We develop a comparative quantitative structure-activity relationships based analysis on compounds embodying the 1,2,5-thiadiazolidin-3-one 1,1-dioxide scaffold. By means of classical Molecular Dynamics we obtain the conformation of each lowest-energy molecular structure from which we derive more than a thousand of structural descriptors necessary for building predictive QSAR models. We resort to two different modeling approaches with the purpose of testing the consistency of our results: (a) multivariable linear regressions based on the replacement method and forward stepwise regression, and (b) the calculation of flexible descriptors with the CORAL program. All the models are properly validated by means of standard procedures. The resulting QSAR models are supposed to be of great utility for the rational search and design (including synthesis and/or in vitro biochemical studies) of new effective non-peptidyl inhibitors of serine proteinases.

QSAR analysis on Spodoptera litura antifeedant activities for flavone derivatives

We establish useful models that relate experimentally measured biological activities of compounds to their molecular structure. The pED(50) feeding inhibition on Spodoptera litura species exhibited by aurones, chromones, 3-coumarones and flavones is analyzed in this work through the hypothesis encompassed in the Quantitative Structure-Activity Relationships (QSAR) Theory. This constitutes a first necessary computationally based step during the design of more bio-friendly repellents that could lead to insights for improving the insecticidal activities of the investigated compounds. After optimizing the molecular structure of each furane and pyrane benzoderivative with the semiempirical molecular orbitals method PM3, more than a thousand of constitutional, topological, geometrical and electronic descriptors are calculated and multiparametric linear regression models are established on the antifeedant potencies. The feature selection method employed in this study is the Replacement Method, which has proven to be successful in previous analyzes. We establish the QSAR both for the complete molecular set of compounds and also for each chemical class, so that acceptably describing the variation of the inhibitory activities from the knowledge of their structure and thus achieving useful predictive results. The main interest of developing trustful QSAR models is that these enable the prediction of compounds having no experimentally measured activities for any reason. Therefore, the structure-activity relationships are further employed for investigating the antifeedant activity on previously synthesized 2-,7-substituted benzopyranes, which do not pose any measured values on the biological expression. One of them, 2-(alpha-naphtyl)-4H-1-benzopyran-4-one, results in a promising structure to be experimentally analyzed as it has predicted pED(50)=1.162.