Simplex Representation Of Molecular Structure Research Articles

Quantitative Structure Activity Relationship Analysis of Antiviral Activity of PF74 Type HIV-1 Capsid Protein Inhibitors by Simplex Representation of Molecular Structure

Human immunodeficiency virus type 1 (HIV-1) capsid protein (CA) plays essential roles in both early and late stages of HIV-1 replication and it is an important target for developing new therapeutic drugs. In the present study, quantitative structure–activity relationships (QSARs) models were developed for a series of 46 derivatives of PF74 CA inhibitors binding to the HIV1 capsid. The Partial least square (PLS) method was applied to obtain QSAR models based on simplex representation of molecular structure (SiRMS). Molecular docking studies were performed to find the optimal conformation of molecules within the binding site, and to compute 3D SiRMS descriptors. The study produced several statistically significant and acceptable QSAR models (R2 = 0.98, Q2 = 0.76–0.78, R2 predict = 0.84). A consensus model developed from the five excellent PLS QSAR models which was statistically validated for its predictive power using cross-validation, Y-randomization, and applicability domain evaluation procedures. The obtained results have satisfactory statistical parameters within acceptable range. The model interpreted by a reverse analysis identified the molecular fragments that promote and interfere with antiviral activity and the degree to which they influence activity. The influence of the physical–chemical properties of the molecules on antiviral activity was determined. It has been observed that antiviral activity is mainly governed by electrostatic characteristics of molecular structure and hydrophobic factors. The results obtained may further assist in the design of new CA inhibitor compounds and prediction of their activity.

Quantitative structure–activity relationship study on prolonged anticonvulsant activity of terpene derivatives in pentylenetetrazole test

Abstract Quantitative structure–activity relationship (QSAR) study has been conducted on 36 terpene derivatives with anticonvulsant activity in timed pentylenetetrazole (PTZ) infusion test. QSAR models for anticonvulsant activity prediction of hydrazones and esters of some monocyclic/bicyclic terpenoids were developed using simplex representation of molecular structure (SiRMS; informational field [IF]) approach based on the SiRMS and the IF of molecule. Four 2D partial least squares QSAR consensus models were developed with the coefficient of determination for test sets R test 2 > 0.62 {R}_{\text{test}}^{2}\gt 0.62 . Based on the established QSAR models, we found that carvone and verbenone cores possess the most significant contribution to antiseizure action examined on the model of PTZ-induced convulsions at 3 and 24 h after oral administration of terpene derivatives. Moreover, carbonyl and hydroxy group substitution in terpenoid molecules followed by hydrazones and esters formation leads to enhancement and prolongation of antiseizure action due to the contribution of additional molecular fragments. The presented QSAR models might be utilized to predict anticonvulsant effect among terpene derivatives for their oral administration against onset seizures.

Simplex representation of molecular structure as universal QSAR/QSPR tool.

We review the development and application of the Simplex approach for the solution of various QSAR/QSPR problems. The general concept of the simplex method and its varieties are described. The advantages of utilizing this methodology, especially for the interpretation of QSAR/QSPR models, are presented in comparison to other fragmentary methods of molecular structure representation. The utility of SiRMS is demonstrated not only in the standard QSAR/QSPR applications, but also for mixtures, polymers, materials, and other complex systems. In addition to many different types of biological activity (antiviral, antimicrobial, antitumor, psychotropic, analgesic, etc.), toxicity and bioavailability, the review examines the simulation of important properties, such as water solubility, lipophilicity, as well as luminescence, and thermodynamic properties (melting and boiling temperatures, critical parameters, etc.). This review focuses on the stereochemical description of molecules within the simplex approach and details the possibilities of universal molecular stereo-analysis and stereochemical configuration description, along with stereo-isomerization mechanism and molecular fragment “topography” identification.

Quantitative structure-activity relationship modelling of influenza M2 ion channels inhibitors

A series of adamantane derivatives (rimantadine and amantadine) incorporating amino-acid residues are investigated by simplex representation of molecular structure (SiRMS) approach in order to found correlation between chemical structures of investigated compounds and obtained data for antiviral activity and cytotoxicity. The obtained data from QSAR analysis show that adamantane derivatives containing amino acids with short aliphatic non-polar residues in the lateral chain will have good antiviral activity against the tested virus A/H3N2, strain Hong Kong/68 with low cytotoxicity. QSAR experiments and in vitro data also show good correlation and reveal that modified adamantine derivatives including guanidated in the lateral chain amino acid and ?-amino acids as substituents show low to none activity.

The Influence of Structural Patterns on Acute Aquatic Toxicity of Organic Compounds.

Investigation of the influence of molecular structure of different organic compounds on acute toxicity towards Fathead minnow, Daphnia magna, and Tetrahymena pyriformis has been carried out using 2D simplex representation of molecular structure and two modelling methods: Random Forest (RF) and Gradient Boosting Machine (GBM). Suitable QSAR (Quantitative Structure - Activity Relationships) models were obtained. The study was focused on QSAR models interpretation. The aim of the study was to develop a set of structural fragments that simultaneously consistently increase toxicity toward Fathead minnow, Daphnia magna, Tetrahymena pyriformis. The interpretation allowed to gain more details about known toxicophores and to propose new fragments. The results obtained made it possible to rank the contributions of molecular fragments to various types of toxicity to aquatic organisms. This information can be used for molecular optimization of chemicals. According to the results of structural interpretation, the most significant common mechanisms of the toxic effect of organic compounds on Fathead minnow, Daphnia magna and Tetrahymena pyriformis are reactions of nucleophilic substitution and inhibition of oxidative phosphorylation in mitochondria. In addition acetylcholinesterase and voltage-gated ion channel of Fathead minnow and Daphnia magna are important targets for toxicants. The on-line version of the OCHEM expert system (https://ochem.eu) were used for a comparative QSAR investigation. The proposed QSAR models comply with the OECD principles and can be used to reliably predict acute toxicity of organic compounds towards Fathead minnow, Daphnia magna and Tetrahymena pyriformis with allowance for applicability domain estimation.

Effect of the structural factors of organic compounds on the acute toxicity toward Daphnia magna

ABSTRACT The acute toxicity of organic compounds towards Daphina magna was subjected to QSAR analysis. The two-dimensional simplex representation of molecular structure (2D SiRMS) and the support vector machine (SVM), gradient boosting (GBM) methods were used to develop QSAR models. Adequate regression QSAR models were developed for incubation of 24 h. Their interpretation allowed us to quantitatively describe and rank the well-known toxicophores, to refine their molecular surroundings, and to distinguish the structural derivatives of the fragments that significantly contribute to the acute toxicity (LC50) of organic compounds towards D. magna. Based on the results of the interpretation of the regression models, a molecular design (modification) of highly toxic compounds was performed in order to reduce their hazard. In addition, acceptable classification QSAR models were developed to reliably predict the following mode of action (MOA): specific and non-specific toxicity of organic compounds towards D. magna. When interpreting these models, we were able to determine the structural fragments and the physicochemical characteristics of molecules that are responsible for the manifestation of one of the modes of action. The on-line version of the OCHEM expert system (https://ochem.eu), HYBOT descriptors, and the random forest and SVM methods were used for a comparative QSAR investigation.

Prediction of the flash points of binary biodiesel mixtures from molecular structures

Biodiesel has already been widely used as an essential renewable and alternative energy. Great attention also has been attracted to its safety issues at the same time. In this paper, a novel mixture Quantitative Structure-Property Relationship model (M-QSPR) was developed to predict the flash points of binary biodiesel mixtures only from their molecular structures. The Simplex Representation of Molecular Structure (SiRMS) descriptors were employed to represent the molecular characteristics of biodiesel mixtures. The genetic algorithm combined with multiple linear regression (GA-MLR) was used to select the most relevant SiRMS descriptors and develop the corresponding MLR model. The resulted model was a two-parameter linear equation, with the root mean square error and average absolute error of the external test set being of 4.777 and 3.768 K, respectively. The presented model was then rigorously verified by multiple validation methods, and the results showed satisfactory fitness, robustness and predictivity of the model. This study could be reasonably considered to provide a reliable method for predicting the flash points of binary biodiesel mixtures for engineering.

A novel method for predicting the flash points of binary mixtures from molecular structures

Flash point (FP) is one of the most important parameters used to characterize the potential fire and explosion hazards for flammable liquids. A quantitative structure–property relationship (QSPR) study is conducted to develop mathematical models for predicting the flash points of binary mixtures from only their molecular structures. The modified Simplex Representation of Molecular Structure (m-SiRMS) descriptors were proposed to characterize the molecular features of a total of 288 binary organic mixtures. A set of optimal descriptor subsets that significantly contribute to the FP property were determined by combining the genetic algorithm with multiple linear regression (GA-MLR) and then develop the prediction model. The resulted model is a multiple linear equation with seven SiRMS descriptors. The average absolute error and the root mean square error of the external test set was 3.48 K and 4.73, respectively. The developed model was rigorously validated using multiple strategies, and further extensively compared to the corresponding model developed based on traditional SiRMS descriptors as well as other previously published models. The results demonstrated the robustness, validity and satisfactory predictivity of the proposed model, and the superiority of the proposed m-SiRMS descriptors as well. The proposed method could be reasonably expected to reliably predict the FP of the binary mixtures for engineering.

Prediction of the Auto-Ignition Temperatures of Binary Miscible Liquid Mixtures from Molecular Structures.

A quantitative structure-property relationship (QSPR) study is performed to predict the auto-ignition temperatures (AITs) of binary liquid mixtures based on their molecular structures. The Simplex Representation of Molecular Structure (SiRMS) methodology was employed to describe the structure characteristics of a series of 132 binary miscible liquid mixtures. The most rigorous “compounds out” strategy was employed to divide the dataset into the training set and test set. The genetic algorithm (GA) combined with multiple linear regression (MLR) was used to select the best subset of SiRMS descriptors, which significantly contributes to the AITs of binary liquid mixtures. The result is a multilinear model with six parameters. Various strategies were employed to validate the developed model, and the results showed that the model has satisfactory robustness and predictivity. Furthermore, the applicability domain (AD) of the model was defined. The developed model could be considered as a new way to reliably predict the AITs of existing or new binary miscible liquid mixtures, belonging to its AD.

Consensus QSAR modelling of SIRT1 activators using simplex representation of molecular structure

Hierarchical QSAR technology (HiT QSAR) was used for consensus QSAR modelling of 65 SIRT1 activators. Simplex representation of molecular structure (SiRMS) has been used for descriptor generation. The predictive QSAR models were developed using the partial least squares (PLS) method. The QSAR models were built up according to OECD principles. One hundred rounds of Y-scrambling were performed for each selected model to exclude chance correlations. A successful consensus model (r2 = 0.830, = 0.754) was obtained from the five best QSAR models. Leverage, ellipsoid and local tree domain of applicability (DA) approaches have been used for evaluation of the quality of predictions. Molecular fragments responsible for an increase and decrease of the activation properties have been determined by mechanistic interpretation of the developed QSAR model.

Ранжированный ряд фрагментов молекул лекарственных средств, определяющих их нейродоступность

Использование методов компьютерного моделирования на основе зависимости «структура — свойство» (QSPR) позволяет рационально планировать как проведение экспериментальных исследований, так и осуществлять эффективное генерирование структур, обладающих способностью проникать через ГЭБ. Целью работы было создание моделей «структура — свойство» на основе симплексного представления молекул для внеэкспериментальной оценки их способности проникать через ГЭБ, и проведение структурной интерпретации построенных моделей, учитывающей взаимное влияние атомов в молекуле. С использованием метода симплексного представления молекулярной структуры был оценен вклад различных молекулярных фрагментов и функциональных групп в величину нейродоступности диффундирующих через ГЭБ соединений. Показано, что наличие сильно полярных и ионогенных групп (карбоксильная, карбонильная, фенольный гидроксил) негативно сказывается на способности веществ проникать в мозг. Наличие атомов галогена и ароматических фрагментов положительно влияет на эффективность переноса молекул через ГЭБ. Построенная QSPR модель характеризуется удовлетворительной прогнозирующей способностью, оцененной согласно внутренней процедуре валидации моделей RandomForest (R2 = 0,54, RMSE = 0,47).

QSPR modeling of critical properties of organic binary mixtures

QSPR models for the critical temperatures, critical volumes, and critical pressures of binary organic mixtures are given. The binary organic mixtures have been described in terms of the mixture modification of simplex representation of molecular structure. The accuracy of the obtained models is comparable to the recommended one, the mean error ranging from 6.8 to 14.6%. The models imply that electronic polarizability is the most important factor for the critical volume and that the critical temperature and critical pressure are determined primarily by van der Waals and electrostatic interactions.

Amino substituted nitrogen heterocycle ureas as kinase insert domain containing receptor (KDR) inhibitors: Performance of structure–activity relationship approaches

A quantitative structure–activity relationship (QSAR) study was performed on a set of amino-substituted nitrogen heterocyclic urea derivatives. Two novel approaches were applied: (1) the simplified molecular input-line entry systems (SMILES) based optimal descriptors approach; and (2) the fragment-based simplex representation of molecular structure (SiRMS) approach. Comparison with the classic scheme of building up the model and balance of correlation (BC) for optimal descriptors approach shows that the BC scheme provides more robust predictions than the classic scheme for the considered pIC50 of the heterocyclic urea derivatives. Comparison of the SMILES-based optimal descriptors and SiRMS approaches has confirmed good performance of both techniques in prediction of kinase insert domain containing receptor (KDR) inhibitory activity, expressed as a logarithm of inhibitory concentration (pIC50) of studied compounds.

'Quasi-Mixture' Descriptors for QSPR Analysis of Molecular Macroscopic Properties. The Critical Properties of Organic Compounds.

Rational approach towards the QSAR/QSPR modeling requires the descriptors to be computationally efficient, yet physically and chemically meaningful. On the basis of existing simplex representation of molecular structure (SiRMS) the novel 'quasi-mixture' descriptors were developed in order to accomplish the goal of characterization molecules on 2D level (i.e. without explicit generation of 3D structure and exhaustive conformational search) with account for potential intermolecular interactions. The critical properties of organic compounds were chosen as target properties for the estimation of descriptors' efficacy because of their well-known physical nature, rigorously estimated experimental errors and large quantity of experimental data. Among described properties are critical temperature, pressure and volume. Obtained models have high statistical characteristics, therefore showing the efficacy of suggested 'quasi-mixture' approach. Moreover, 'quasi-mixture' approach, as a branch of the SiRMS, allows to interpret results in terms of simple basic molecular properties. The obtained picture of influences corresponds to the accepted theoretical views.

QSPR Modeling of critical parameters of organic compounds belonging to different classes in terms of the simplex representation of molecular structure

A set of QSPR models has been proposed for the description and prediction of critical temperatures, volumes, and pressures and Pitzer’s acentric factors for a large number of organic compounds on the basis of the simplex representation of molecular structure (SiRMS). The models have been designed with the aid of the nonlinear random forest learning algorithm. The SiRMS approach turned out to be an efficient tool for studying and predicting thermodynamic parameters of organic compounds. Taking into account the accuracy of the obtained results, obvious interpretability of simplex descriptors, and their effect on critical properties, the SiRMS approach may be recommended for studying and predicting critical properties.

QSAR analysis of poliovirus inhibition by dual combinations of antivirals

We have applied Hierarchical QSAR Technology (HiT QSAR) to the prediction of antiviral effects of paired combinations of picornavirus replication inhibitors against poliovirus 1 (Mahoney) in vitro. The inhibition from all binary combinations of eight antivirals were investigated. Simplex representation of molecular structure (SiRMS) was used for the generation of molecular descriptors of both pure compounds and all dual mixture combinations. Predictive QSAR models were obtained using the partial least squares (PLS) method. Predictive power of the developed models was validated using eightfold external cross-validation (CV, $$Q_{\text{ext}}^{ 2}$$ = 0.67–0.93). Adequate models ( $$Q_{\text{ext}}^{ 2}$$ = 0.53–0.97) were obtained in the same way for predicting measured inhibitory concentrations at other levels (i.e., IC30, IC40, IC60, IC70). The usage of predicted values of these concentrations in the framework of the feature net (FN) approach led to an insignificant increase in the quality of the obtained QSAR models ( $$Q_{\text{ext}}^{ 2}$$ = 0.71–0.94). Developed QSAR models were analyzed and interpreted so that structural fragments and components of the combination promoting the antiviral activity were determined (e.g., 2-(4-methoxyphenyl)-4,5-dihydrooxazole or the combination of N-hydroxybenzimidoyl and 3-methylisoxasole). Then the resulting consensus model was used to predict novel potent combinations of drugs. Combinations of enviroxime with pleconaril, WIN52084, and rupintrivir and the mixture of rupintrivir with disoxaril were predicted to cause the most inhibition of poliovirus 1 replication. HiT QSAR proved itself as an adequate tool for QSAR analysis of mixtures and, although the method described here is suitable only for binary mixtures, it can be easily extended for more complex combinations.

QSPR Prediction of Lipophilicity for Organic Compounds Using Random Forest Technique on the Basis of Simplex Representation of Molecular Structure

The relationship between the octanol-water partition coefficient for more than twelve thousand organic compounds and their structures was investigated using a QSPR approach based on Simplex Representation of Molecular Structure (SiRMS). The dataset used in our study included 10973 compounds with experimental values of lipophilicity (LogKow ) for different chemical compounds. Random Forest (RF) method was used for statistical modeling at the 2D level of representation of molecular structure. Developed models are adequate and successfully validated with external test sets. Proposed models have clear interpretation due to the use of simplex representation of molecular structure and predict the LogKow values with the accuracy of the best modern models. Thus QSPR models proposed in this study represent powerful and easy-to use virtual screening tool that can be recommended for prediction of octanol-water partition coefficient.

Qsar Analysis of [(Biphenyloxy)Propyl]Isoxazoles: Agents Against Coxsackievirus B3

Antiviral drugs are urgently needed for the treatment of acute and chronic diseases caused by enteroviruses such as coxsackievirus B3 (CVB3). The main goal of this study is quantitative structure-activity relationship (QSAR) analysis of anti-CVB3 activity (clinical CVB3 isolate 97927 [log IC50, µM]) and investigation of the selectivity of 25 ([biphenyloxy]propyl)isoxazoles, followed by computer-aided design and virtual screening of novel active compounds. The 2D QSAR obtained models are quite satisfactory (R(2) = 0.84-0.99, Q(2) = 0.76-0.92, R(2)(ext) = 0.62-0.79). Compounds with high antiviral activity and selectivity have to contain 5-trifluoromethyl-[1,2,4]oxadiazole or 2,4-difluorophenyl fragments. Insertion of 2,5-dimethylbenzene, napthyl and especially biphenyl substituents into investigated compounds substantially decreases both their antiviral activity and selectivity. Several compounds were proposed as a result of design and virtual screening. A high level of activity of 2-methoxy-1-phenyl-1H-imidazo[4,5-c]pyridine (sm428) was confirmed experimentally. Simplex representation of molecular structure allows successful QSAR analysis of anti-CVB3 activity of ([biphenyloxy]propyl)isoxazole derivatives. Two possible ways of battling CVB3 are considered as a future perspective.

Per Aspera Ad Astra : Application of Simplex Qsar Approach in Antiviral Research

This review explores the application of the Simplex representation of molecular structure (SiRMS) QSAR approach in antiviral research. We provide an introduction to and description of SiRMS, its application in antiviral research and future directions of development of the Simplex approach and the whole QSAR field. In the Simplex approach every molecule is represented as a system of different simplexes (tetratomic fragments with fixed composition, structure, chirality and symmetry). The main advantages of SiRMS are consideration of the different physical-chemical properties of atoms, high adequacy and good interpretability of models obtained and clear procedures for molecular design. The reliability of developed QSAR models as predictive virtual screening tools and their ability to serve as the basis of directed drug design was validated by subsequent synthetic and biological experiments. The SiRMS approach is realized as the complex of the computer program 'HiT QSAR', which is available on request.

Application of Random Forest and Multiple Linear Regression Techniques to QSPR Prediction of an Aqueous Solubility for Military Compounds.

The relationship between the aqueous solubility of more than two thousand eight hundred organic compounds and their structures was investigated using a QSPR approach based on Simplex Representation of Molecular Structure (SiRMS). The dataset consists of 2537 diverse organic compounds. Multiple Linear Regression (MLR) and Random Forest (RF) methods were used for statistical modeling at the 2D level of representation of molecular structure. Statistical characteristics of the best models are quite good (MLR method: R(2) =0.85, Q(2) =0.83; RF method: R(2) =0.99, R(2) oob =0.88). The external validation set of 301 compounds (including 47 nitro-, nitroso- and nitrogen-rich compounds of military interest) which were not included in the training set and modeling process, was used for evaluation of the models predictivity. Thus, well-fitted and robust (R(2) test (MLR)=0.76 and R(2) test (RF)=0.82) models were obtained for both statistical techniques using descriptors based on the topological structural information only. The predicted solubility values for military compounds are in good agreement with experimental ones. Developed QSPR models represent powerful and easy-to-use virtual screening tool that can be recommended for prediction of aqueous solubility.