Quantum-SAR extension of the spectral-SAR algorithm: application to polyphenolic anticancer bioactivity

Natural Compounds Therapeutic Features in Brain Disorders by Experimental, Bioinformatics and Cheminformatics Methods

BACKGROUND

Synthetic compounds with pharmaceutical applications in brain disorders are daily designed and synthesized, with well first effects but also seldom severe side effects. This imposes the search for alternative therapies based on the pharmaceutical potentials of natural compounds. The natural compounds isolated from various plants and arthropods venom are well known for their antimicrobial (antibacterial, antiviral) and antiinflammatory activities, but more studies are needed for a better understanding of their structural and pharmacological features with new therapeutic applications.

OBJECTIVES

Here we present some structural and pharmaceutical features of natural compounds isolated from plants and arthropods venom relevant for their efficiency and potency in brain disorders. We present the polytherapeutic effects of natural compounds belonging to terpenes (limonene), monoterpenoids (1,8-cineole) and stilbenes (resveratrol), as well as natural peptides (apamin, mastoparan and melittin).

METHODS

Various experimental and in silico methods are presented with special attention on bioinformatics (natural compounds database, artificial neural network) and cheminformatics (QSAR, drug design, computational mutagenesis, molecular docking).

RESULTS

In the present paper we reviewed: (i) recent studies regarding the pharmacological potential of natural compounds in the brain; (ii) the most useful databases containing molecular and functional features of natural compounds; and (iii) the most important molecular descriptors of natural compounds in comparison with a few synthetic compounds.

CONCLUSION

Our paper indicates that natural compounds are a real alternative for nervous system therapy and represents a helpful tool for the future papers focused on the study of the natural compounds.

QSAR by Minimal Topological Difference[s]: Post-Modern Perspectives

In the context of reconsidering the Quantitative Structure-Activity Relationship (QSAR) methods at the economical level, namely the optimization rules of OECD, the present review unfolds the key features of Minimal Sterical, Monte-Carlo and Minimal Topological Difference (MTD) methods, developed for quantitative treatment of the relations between biological activity of organic chemical compounds (drugs, pesticides, and so on) and their structures. The initial Minimal Steric Difference (MSD) is completed by the three-dimensional variant of the MTD method, being the last one referred to here, while the main principles of validating and guiding a viable QSAR method verified by the analytical-automated MTD, thus enlarging the perspectives of understanding the chemical-biological interaction at the level of ligand-receptor sites, cavity, and walls, with a true service to the future adaptive molecular design.

Multi-Layer Identification of Highly-Potent ABCA1 Up-Regulators Targeting LXRβ Using Multiple QSAR Modeling, Structural Similarity Analysis, and Molecular Docking

In this study, in silico approaches, including multiple QSAR modeling, structural similarity analysis, and molecular docking, were applied to develop QSAR classification models as a fast screening tool for identifying highly-potent ABCA1 up-regulators targeting LXRβ based on a series of new flavonoids. Initially, four modeling approaches, including linear discriminant analysis, support vector machine, radial basis function neural network, and classification and regression trees, were applied to construct different QSAR classification models. The statistics results indicated that these four kinds of QSAR models were powerful tools for screening highly potent ABCA1 up-regulators. Then, a consensus QSAR model was developed by combining the predictions from these four models. To discover new ABCA1 up-regulators at maximum accuracy, the compounds in the ZINC database that fulfilled the requirement of structural similarity of 0.7 compared to known potent ABCA1 up-regulator were subjected to the consensus QSAR model, which led to the discovery of 50 compounds. Finally, they were docked into the LXRβ binding site to understand their role in up-regulating ABCA1 expression. The excellent binding modes and docking scores of 10 hit compounds suggested they were highly-potent ABCA1 up-regulators targeting LXRβ. Overall, this study provided an effective strategy to discover highly potent ABCA1 up-regulators.

Structural Investigation for Optimization of Anthranilic Acid Derivatives as Partial FXR Agonists by in Silico Approaches

In this paper, a three level in silico approach was applied to investigate some important structural and physicochemical aspects of a series of anthranilic acid derivatives (AAD) newly identified as potent partial farnesoid X receptor (FXR) agonists. Initially, both two and three-dimensional quantitative structure activity relationship (2D- and 3D-QSAR) studies were performed based on such AAD by a stepwise technology combined with multiple linear regression and comparative molecular field analysis. The obtained 2D-QSAR model gave a high predictive ability (R²(train) = 0.935, R²(test) = 0.902, Q²(LOO) = 0.899). It also uncovered that number of rotatable single bonds (b_rotN), relative negative partial charges (RPC(-)), oprea's lead-like (opr_leadlike), subdivided van der Waal's surface area (SlogP_VSA2) and accessible surface area (ASA) were important features in defining activity. Additionally, the derived3D-QSAR model presented a higher predictive ability (R²(train) = 0.944, R²(test) = 0.892, Q²(LOO) = 0.802). Meanwhile, the derived contour maps from the 3D-QSAR model revealed the significant structural features (steric and electronic effects) required for improving FXR agonist activity. Finally, nine newly designed AAD with higher predicted EC50 values than the known template compound were docked into the FXR active site. The excellent molecular binding patterns of these molecules also suggested that they can be robust and potent partial FXR agonists in agreement with the QSAR results. Overall, these derived models may help to identify and design novel AAD with better FXR agonist activity.

Design, synthesis, antifungal activities and 3D-QSAR of new N,N'-diacylhydrazines containing 2,4-dichlorophenoxy moiety

A series of new N,N'-diacylhydrazine derivatives were designed and synthesized. Their structures were verified by 1H-NMR, mass spectra (MS) and elemental analysis. The antifungal activities of these N,N'-diacylhydrazines were evaluated. The bioassay results showed that most of these N,N'-diacylhydrazines showed excellent antifungal activities against Cladosporium cucumerinum, Corynespora cassiicola, Sclerotinia sclerotiorum, Erysiphe cichoracearum, and Colletotrichum orbiculare in vivo. The half maximal effective concentration (EC50) of one of the compounds was also determined, and found to be comparable with a commercial drug. To further investigate the structure-activity relationship, comparative molecular field analysis (CoMFA) was performed on the basis of antifungal activity data. Both the steric and electronic field distributions of CoMFA are in good agreement in this study.

Determining chemical reactivity driving biological activity from SMILES transformations: the bonding mechanism of anti-HIV pyrimidines

Assessing the molecular mechanism of a chemical-biological interaction and bonding stands as the ultimate goal of any modern quantitative structure-activity relationship (QSAR) study. To this end the present work employs the main chemical reactivity structural descriptors (electronegativity, chemical hardness, chemical power, electrophilicity) to unfold the variational QSAR though their min-max correspondence principles as applied to the Simplified Molecular Input Line Entry System (SMILES) transformation of selected uracil derivatives with anti-HIV potential with the aim of establishing the main stages whereby the given compounds may inhibit HIV infection. The bonding can be completely described by explicitly considering by means of basic indices and chemical reactivity principles two forms of SMILES structures of the pyrimidines, the Longest SMILES Molecular Chain (LoSMoC) and the Branching SMILES (BraS), respectively, as the effective forms involved in the anti-HIV activity mechanism and according to the present work, also necessary intermediates in molecular pathways targeting/docking biological sites of interest.

High-Dimensional descriptor selection and computational QSAR modeling for antitumor activity of ARC-111 analogues Based on Support Vector Regression (SVR)

To design ARC-111 analogues with improved efficiency, we constructed the QSAR of 22 ARC-111 analogues with RPMI8402 tumor cells. First, the optimized support vector regression (SVR) model based on the literature descriptors and the worst descriptor elimination multi-roundly (WDEM) method had similar generalization as the artificial neural network (ANN) model for the test set. Secondly, seven and 11 more effective descriptors out of 2,923 features were selected by the high-dimensional descriptor selection nonlinearly (HDSN) and WDEM method, and the SVR models (SVR3 and SVR4) with these selected descriptors resulted in better evaluation measures and a more precise predictive power for the test set. The interpretability system of better SVR models was further established. Our analysis offers some useful parameters for designing ARC-111 analogues with enhanced antitumor activity.

QUANTITATIVE STRUCTURE–PROPERTY RELATIONSHIPS ON DISSOLVABILITY OF PCDD/Fs USING QUANTUM CHEMICAL DESCRIPTORS AND PARTIAL LEAST SQUARES

The environmental fate of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) has become a major issue in recent decades. Quantitative structure–property relationship (QSPR) modeling is a powerful approach for predicting the properties of environmental organic pollutants from their structure descriptors. In this study, QSPR models were established for estimating water solubility (- log S W ) and n-octanol/water partition coefficient ( log KOW) of PCDD/Fs. Quantum chemical descriptors computed with density functional theory at the B3LYP/6-31G(d) level and partial least squares (PLS) analysis with an optimizing procedure were used to generate QSPR models for - log S W and log K OW of PCDD/Fs. Optimized models with high correlation coefficients (R2 > 0.983) were obtained for estimating - log S W and log K OW of PCDD/Fs. Both the internal cross validation test [Formula: see text] and external validation test (R2 > 0.965) results showed that the obtained models had high-precision and good prediction capability. The - log S W } and log K OW values predicted by the obtained models are very close to those observed. The PLS analysis indicated that PCDD/Fs with larger electronic spatial extent (R e ), lower molecular total energy (E T ), and smaller energy gap between the lowest unoccupied and the highest occupied molecular orbitals (E LUMO -E HOMO ) tend to be less soluble in water but more lipophilic.

QSAR AND PHARMACOPHORE MODELING OF 4-ARYLTHIENO [3, 2-d] PYRIMIDINE DERIVATIVES AGAINST ADENOSINE RECEPTOR OF PARKINSON'S DISEASE

A series of 47, 4-arylthieno[3, 2-d] pyrimidine derivatives was subjected to quantitative structure-antiparkinson activity relationships (QSAR) studies to evaluate the antagonist activity towards both adenosine A1 and adenosine A2A targets in Parkinson's drug discovery. QSAR models were derived with the aid of genetic function approximation (GFA) technique using descriptors to make connections between structural parameters and antiparkinson's activity followed by ADMET analysis and pharmacophore model generation. QSAR model was assessed using a test set of 12 compounds for A1 (r2 pred = 0.961), (q2 = 0.912) and 12 compounds for A2a (r2 pred = 0.914), (q2 = 0.781) receptor. The results revealed the significant role of DIPOLE MAG, CHI-V-3-P, WIENER, AREA, SC-2 and PHI-MAG descriptors in the antiparkinson activity of the studied compounds against adenosine A1 and adenosine A2A receptors. Subsequent, ADMET analysis shows 28 compounds can be the better candidates of drug and execution of pharmacophore model, explores the hydrogen bond donor, aromatic ring and hydrophobic groups are the key structural features for the antagonist activity.

Alert-QSAR. Implications for electrophilic theory of chemical carcinogenesis

Given the modeling and predictive abilities of quantitative structure activity relationships (QSARs) for genotoxic carcinogens or mutagens that directly affect DNA, the present research investigates structural alert (SA) intermediate-predicted correlations A(SA) of electrophilic molecular structures with observed carcinogenic potencies in rats (observed activity, A = Log[1/TD(50)], i.e., [Formula: see text]). The present method includes calculation of the recently developed residual correlation of the structural alert models, i.e., [Formula: see text]. We propose a specific electrophilic ligand-receptor mechanism that combines electronegativity with chemical hardness-associated frontier principles, equality of ligand-reagent electronegativities and ligand maximum chemical hardness for highly diverse toxic molecules against specific receptors in rats. The observed carcinogenic activity is influenced by the induced SA-mutagenic intermediate effect, alongside Hansch indices such as hydrophobicity (LogP), polarizability (POL) and total energy (Etot), which account for molecular membrane diffusion, ionic deformation, and stericity, respectively. A possible QSAR mechanistic interpretation of mutagenicity as the first step in genotoxic carcinogenesis development is discussed using the structural alert chemoinformation and in full accordance with the Organization for Economic Co-operation and Development QSAR guidance principles.

Residual-QSAR. Implications for genotoxic carcinogenesis

INTRODUCTION

Both main types of carcinogenesis, genotoxic and epigenetic, were examined in the context of non-congenericity and similarity, respectively, for the structure of ligand molecules, emphasizing the role of quantitative structure-activity relationship ((Q)SAR) studies in accordance with OECD (Organization for Economic and Cooperation Development) regulations. The main purpose of this report involves electrophilic theory and the need for meaningful physicochemical parameters to describe genotoxicity by a general mechanism. RESIDUAL-QSAR METHOD: The double or looping multiple linear correlation was examined by comparing the direct and residual structural information against the observed activity. A self-consistent equation of observed-computed activity was assumed to give maximum correlation efficiency for those situations in which the direct correlations gave non-significant statistical information. Alternatively, it was also suited to describe slow and apparently non-noticeable cancer phenomenology, with special application to non-congeneric molecules involved in genotoxic carcinogenesis.

APPLICATION AND DISCUSSIONS

The QSAR principles were systematically applied to a given pool of molecules with genotoxic activity in rats to elucidate their carcinogenic mechanisms. Once defined, the endpoint associated with ligand-DNA interaction was used to select variables that retained the main Hansch physicochemical parameters of hydrophobicity, polarizability and stericity, computed by the custom PM3 semiempirical quantum method. The trial and test sets of working molecules were established by implementing the normal Gaussian principle of activities that applies when the applicability domain is not restrained to the congeneric compounds, as in the present study. The application of the residual, self-consistent QSAR method and the factor (or average) method yielded results characterized by extremely high and low correlations, respectively, with the latter resembling the direct activity to parameter QSARs. Nevertheless, such contrasted correlations were further incorporated into the advanced statistical minimum paths principle, which selects the minimum hierarchy from Euclidean distances between all considered QSAR models for all combinations and considered molecular sets (i.e., school and validation). This ultimately led to a mechanistic picture based on the identified alpha, beta and gamma paths connecting structural indicators (i.e., the causes) to the global endpoint, with all included causes. The molecular mechanism preserved the self-consistent feature of the residual QSAR, with each descriptor appearing twice in the course of one cycle of ligand-DNA interaction through inter-and intra-cellular stages.

CONCLUSIONS

Both basal features of the residual-QSAR principle of self-consistency and suitability for non-congeneric molecules make it appropriate for conceptually assessing the mechanistic description of genotoxic carcinogenesis. Additionally, it could be extended to enriched physicochemical structural indices by considering the molecular fragments or structural alerts (or other molecular residues), providing more detailed maps of chemical-biological interactions and pathways.

Study on QSTR of benzoic acid compounds with MCI

Quantitative structure-toxicity relationship (QSTR) plays an important role in toxicity prediction. With the modified method, the quantum chemistry parameters of 57 benzoic acid compounds were calculated with modified molecular connectivity index (MCI) using Visual Basic Program Software, and the QSTR of benzoic acid compounds in mice via oral LD₅₀ (acute toxicity) was studied. A model was built to more accurately predict the toxicity of benzoic acid compounds in mice via oral LD₅₀: 39 benzoic acid compounds were used as a training dataset for building the regression model and 18 others as a forecasting dataset to test the prediction ability of the model using SAS 9.0 Program Software. The model is LogLD₅₀ = 1.2399 × ⁰J^A +2.6911 × ¹J^A – 0.4445 × J^B (R² = 0.9860), where ⁰J^A is zero order connectivity index, ¹J^A is the first order connectivity index and J_B = ⁰J^A × ¹J^A is the cross factor. The model was shown to have a good forecasting ability.

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.

Köln-Timişoara Molecular activity combined models toward interspecies toxicity assessment

Aiming to provide a unified picture of computed activity – quantitative structure activity relationships, the so called Köln (ESIP-ElementSpecificInfluenceParameter) model for activity and Timisoara (Spectral-SAR) formulation of QSAR were pooled in order to assess the toxicity modeling and inter-toxicity correlation maps for aquatic organisms against paradigmatic organic compounds. The Köln ESIP model for estimation of a compound toxicity is based on the experimental measurement expressing the direct action of chemicals on the organism Hydractinia echinata so that the structural influence parameters are reflected by the metamorphosis degree itself. As such, the calculation of the structural parameters is absolutely necessary for correct evaluation and interpretation of the evolution of M(easured) and the C(computed) values. On the other hand, the Timişoara Spectral-SAR analysis offers correlation models and paths for H.e. species as well as for four other different organisms with which the toxicity may be inter-changed by means of the same mechanism of action induced by certain common chemicals.

Classification of 5-HT(1A) receptor ligands on the basis of their binding affinities by using PSO-Adaboost-SVM

In the present work, the support vector machine (SVM) and Adaboost-SVM have been used to develop a classification model as a potential screening mechanism for a novel series of 5-HT_1A selective ligands. Each compound is represented by calculated structural descriptors that encode topological features. The particle swarm optimization (PSO) and the stepwise multiple linear regression (Stepwise-MLR) methods have been used to search descriptor space and select the descriptors which are responsible for the inhibitory activity of these compounds. The model containing seven descriptors found by Adaboost-SVM, has showed better predictive capability than the other models. The total accuracy in prediction for the training and test set is 100.0% and 95.0% for PSO-Adaboost-SVM, 99.1% and 92.5% for PSO-SVM, 99.1% and 82.5% for Stepwise-MLR-Adaboost-SVM, 99.1% and 77.5% for Stepwise-MLR-SVM, respectively. The results indicate that Adaboost-SVM can be used as a useful modeling tool for QSAR studies.