Anatoly G. Artemenko

Hierarchical QSAR technology based on the Simplex representation of molecular structure

This article is about the hierarchical quantitative structure–activity relationship technology (HiT QSAR) based on the Simplex representation of molecular structure (SiRMS) and its application for different QSAR/QSP(property)R tasks. The essence of this technology is a sequential solution (with the use of the information obtained on the previous steps) to the QSAR problem by the series of enhanced models of molecular structure description [from one dimensional (1D) to four dimensional (4D)]. It is a system of permanently improved solutions. In the SiRMS approach, every molecule is represented as a system of different simplexes (tetratomic fragments with fixed composition, structure, chirality and symmetry). The level of simplex descriptors detailing increases consecutively from the 1D to 4D representation of the molecular structure. The advantages of the approach reported here are the absence of “molecular alignment” problems, consideration of different physical–chemical properties of atoms (e.g. charge, lipophilicity, etc.), the high adequacy and good interpretability of obtained models and clear ways for molecular design. The efficiency of the HiT QSAR approach is demonstrated by comparing it with the most popular modern QSAR approaches on two representative examination sets. The examples of successful application of the HiT QSAR for various QSAR/QSPR investigations on the different levels (1D–4D) of the molecular structure description are also highlighted. The reliability of developed QSAR models as predictive virtual screening tools and their ability to serve as the base of directed drug design was validated by subsequent synthetic and biological experiments, among others. The HiT QSAR is realized as a complex of computer programs known as HiT QSAR software that also includes a powerful statistical block and a number of useful utilities.

Application of Random Forest Approach to QSAR Prediction of Aquatic Toxicity

This work is devoted to the application of the random forest approach to QSAR analysis of aquatic toxicity of chemical compounds tested on Tetrahymena pyriformis. The simplex representation of the molecular structure approach implemented in HiT QSAR Software was used for descriptors generation on a two-dimensional level. Adequate models based on simplex descriptors and the RF statistical approach were obtained on a modeling set of 644 compounds. Model predictivity was validated on two external test sets of 339 and 110 compounds. The high impact of lipophilicity and polarizability of investigated compounds on toxicity was determined. It was shown that RF models were tolerant for insertion of irrelevant descriptors as well as for randomization of some part of toxicity values that were representing a noise. The fast procedure of optimization of the number of trees in the random forest has been proposed. The discussed RF model had comparable or better statistical characteristics than the corresponding PLS or KNN models.

Investigation of anticancer activity of macrocyclic Schiff bases by means of 4D-QSAR based on simplex representation of molecular structure

Influence of the molecular structure of macrocyclic pyridinophanes, their analogues and some other compounds on anticancer activity (Leukemia, central nervous system (CNS) cancer, prostate cancer, breast cancer, melanoma, non-small cell lung cancer, colon cancer, ovarian cancer, renal cancer) was investigated by means of a new 4D-QSAR approach based on the simplex representation of molecular structures (SiRMS). The number of group (N) is a tuning parameter which can be changed. As a rule For all the investigated molecules, the 3D structural models were first created and the set of conformers (fourth dimension) was used. Each conformer was represented as a system of different simplexes (tetratomic fragments of fixed structure, chirality and symmetry). Statistic characteristics of the QSAR partial least squares (PLS) models were satisfactory (correlation coefficient cross-validation coefficient ). The molecular fragments increasing and decreasing anticancer activity were defined. This information may be useful for the design and direct synthesis of novel anticancer agents.

Existing and Developing Approaches for QSAR Analysis of Mixtures

This review is devoted to the critical analysis of advantages and disadvantages of existing mixture descriptors and their usage in various QSAR/QSPR tasks. We describe good practices for the QSAR modeling of mixtures, data sources for mixtures, a discussion of various mixture descriptors and their application, recommendations about proper external validation specific for mixture QSAR modeling, and future perspectives of this field. The biggest problem in QSAR of mixtures is the lack of reliable data about the mixtures’ properties. Various mixture descriptors are used for the modeling of different endpoints. However, these descriptors have certain disadvantages, such as applicability only to 1u2009:u20091 binary mixtures, and additive nature. The field of QSAR of mixtures is still under development, and existing efforts could be considered as a foundation for future approaches and studies. The usage of non‐additive mixture descriptors, which are sensitive to interaction effects, in combination with best practices of QSAR model development (e.g., thorough data collection and curation, rigorous external validation, etc.) will significantly improve the quality of QSAR studies of mixtures.

The effect of nitroaromatics' composition on their toxicity in vivo: Novel, efficient non-additive 1D QSAR analysis

Novel 1D QSAR approach that allows analysis of non-additive effects of molecular fragments on toxicity has been proposed. Twenty-eight nitroaromatic compounds including some well-known explosives have been chosen for this study. The 50% lethal dose concentration for rats (LD50) was used as the estimation of toxicity in vivo to develop 1D QSAR models on the framework of Simplex representation of molecular structure. The results of 1D QSAR analysis show that even the information about the composition of molecules provides the main trends of toxicity changes. The necessity of consideration of substituents mutual impacts for the development of adequate QSAR models of nitroaromatics toxicity was demonstrated. Statistic characteristics for all the developed partial least squares QSAR models, except the additive ones are quite satisfactory (R2=0.81-0.92; Q2=0.64-0.83; R2 test=0.84-0.87). A successful performance of such models is due to their non-additivity i.e. possibility of taking into account the mutual influence of substituents in benzene ring which plays the governing role for toxicity change and could be mediated through the different C-H fragments of the ring. The correspondence between observed and predicted by these models toxicity values is good. This allowing combine advantages of such approaches and develop adequate consensus model that can be used as a toxicity virtual screening tool.

QSAR analysis of the toxicity of nitroaromatics in Tetrahymena pyriformis: structural factors and possible modes of action.

The Hierarchical Technology for Quantitative Structure–Activity Relationships (HiT QSAR) was applied to 95 diverse nitroaromatic compounds (including some widely known explosives) tested for their toxicity (50% inhibition growth concentration, IGC50) against the ciliate Tetrahymena pyriformis. The dataset was divided into subsets according to putative mechanisms of toxicity. The Classification and Regression Trees (CART) approach implemented within HiT QSAR has been used for prediction of mechanism of toxicity for new compounds. The resulting models were shown to have ∼80% accuracy for external datasets indicating that the mechanistic dataset division was sensible. The Partial Least Squares (PLS) statistical approach was then used to develop 2D QSAR models. Validated PLS models were explored to: (1) elucidate the effects of different substituents in nitroaromatic compounds on toxicity; (2) differentiate compounds by probable mechanisms of toxicity based on their structural descriptors; and (3) analyse the role of various physical–chemical factors responsible for compounds’ toxicity. Models were interpreted in terms of molecular fragments promoting or interfering with toxicity. It was also shown that mutual influence of substituents in benzene ring plays the determining role in toxicity variation. Although chemical mechanism based models were statistically significant and externally predictive ( u2009=u20090.64 for the external set of 63 nitroaromatics identified after all calculations have been completed), they were also shown to have limited coverage (57% for modelling and 76% for external set).

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.

The effects of characteristics of substituents on toxicity of the nitroaromatics: HiT QSAR study

The present study applies the Hierarchical Technology for Quantitative Structure–Activity Relationships (HiT QSAR) for (i) evaluation of the influence of the characteristics of 28 nitroaromatic compounds (some of which belong to a widely known class of explosives) as to their toxicity; (ii) prediction of toxicity for new nitroaromatic derivatives; (iii) analysis of the effects of substituents in nitroaromatic compounds on their toxicity in vivo. The 50% lethal dose concentration for rats (LD50) was used to develop the QSAR models based on simplex representation of molecular structure. The preliminary 1D QSAR results show that even the information on the composition of molecules reveals the main tendencies of changes in toxicity. The statistic characteristics for partial least squares 2D QSAR models are quite satisfactory (R2xa0=xa00.96–0.98; Q2xa0=xa00.91–0.93; R2testxa0=xa00.89–0.92), which allows us to carry out the prediction of activity for 41 novel compounds designed by the application of new combinations of substituents represented in the training set. The comprehensive analysis of toxicity changes as a function of substituent position and nature was carried out. Molecular fragments that promote and interfere with toxicity were defined on the basis of the obtained models. It was shown that the mutual influence of substituents in the benzene ring plays a crucial role regarding toxicity. The influence of different substituents on toxicity can be mediated via different C–H fragments of the aromatic ring.

Interpretation of QSAR Models Based on Random Forest Methods.

A new algorithm for the interpretation of Random Forest models has been developed. It allows to calculate the contribution of each descriptor to the calculated property value. In case of the simplex representation of a molecular structure, contributions of individual atoms can be calculated, and thus it becomes possible to estimate the influence of separate molecular fragments on the investigated property. Such information can be used for the design of new compounds with a predefined property value. The proposed measure of descriptor contributions is not an alternative to the importance of Breiman’s variable, but it characterizes the contribution of a particular explanatory variable to the calculated response value.

QSPR Approach to Predict Nonadditive Properties of Mixtures. Application to Bubble Point Temperatures of Binary Mixtures of Liquids

This paper is devoted to the development of methodology for QSPR modeling of mixtures and its application to vapor/liquid equilibrium diagrams for bubble point temperatures of binary liquid mixtures. Two types of special mixture descriptors based on SiRMS and ISIDA approaches were developed. SiRMS‐based fragment descriptors involve atoms belonging to both components of the mixture, whereas the ISIDA fragments belong only to one of these components. The models were built on the data set containing the phase diagrams for 167 mixtures represented by different combinations of 67 pure liquids. Consensus models were developed using nonlinear Support Vector Machine (SVM), Associative Neural Networks (ASNN), and Random Forest (RF) approaches. For SVM and ASNN calculations, the ISIDA fragment descriptors were used, whereas Simplex descriptors were employed in RF models. The models have been validated using three different protocols: “Points out”, “Mixtures out” and “Compounds out”, based on the specific rules to form training/test sets in each fold of cross‐validation. A final validation of the models has been performed on an additional set of 94 mixtures represented by combinations of novel 34 compounds and modeling set chemicals with each other. The root mean squared error of predictions for new mixtures of already known liquids does not exceed 5.7u2005K, which outperforms COSMO‐RS models. Developed QSAR methodology can be applied to the modeling of any nonadditive property of binary mixtures (antiviral activities, drug formulation, etc.)