Search

A series of 60 nitrobenzonitrile analogues of the anti-viral agent MDL-860 were synthesized (50 of which are new) and evaluated for their activity against three types of enteroviruses (coxsackievirus B1, coxsackievirus B3 and poliovirus 1). Among them, six diaryl ethers (20e, 27e, 28e, 29e, 33e and 35e) demonstrated high in vitro activity (SI > 50) towards at least one of the tested viruses and very low cytotoxicity against human cells. Compound 27e possesses the broadest spectrum of activity towards all tested viruses in the same way as MDL-860 does. The most active derivatives (27e, 29e and 35e) against coxsackievirus B1 were tested in vivo in newborn mice experimentally infected with 20 MLD 50 of coxsackievirus B1. Compound 29e showed promising in vivo activity (protection index 26% and 4 days lengthening of mean survival time). QSAR analysis of the substituent effects on the in vitro cytotoxicity (CC 50 ) and anti-viral activity of the nitrobenzonitrile derivatives was carried out and adequate QSAR models for the anti-viral activity of the compounds against poliovirus 1 and coxsackievirus B1 were constructed., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Severe adverse drug reactions (ADRs) are the fourth leading cause of fatality in the U.S. with more than 100,000 deaths per year. As up to 30% of all ADRs are believed to be caused by drug-drug interactions (DDIs), typically mediated by cytochrome P450s, possibilities to predict DDIs from existing knowledge are important. We collected data from public sources on 1485, 2628, 4371, and 27,966 possible DDIs mediated by four cytochrome P450 isoforms 1A2, 2C9, 2D6, and 3A4 for 55, 73, 94, and 237 drugs, respectively. For each of these data sets, we developed and validated QSAR models for the prediction of DDIs. As a unique feature of our approach, the interacting drug pairs were represented as binary chemical mixtures in a 1:1 ratio. We used two types of chemical descriptors: quantitative neighborhoods of atoms (QNA) and simplex descriptors. Radial basis functions with self-consistent regression (RBF-SCR) and random forest (RF) were utilized to build QSAR models predicting the likelihood of DDIs for any pair of drug molecules. Our models showed balanced accuracy of 72-79% for the external test sets with a coverage of 81.36-100% when a conservative threshold for the model's applicability domain was applied. We generated virtually all possible binary combinations of marketed drugs and employed our models to identify drug pairs predicted to be instances of DDI. More than 4500 of these predicted DDIs that were not found in our training sets were confirmed by data from the DrugBank database.

This article describes design, virtual screening, synthesis, and biological tests of novel αIIbβ3 antagonists, which inhibit platelet aggregation. Two types of αIIbβ3 antagonists were developed: those binding either closed or open form of the protein. At the first step, available experimental data were used to build QSAR models and ligand- and structure-based pharmacophore models and to select the most appropriate tool for ligand-to-protein docking. Virtual screening of publicly available databases (BioinfoDB, ZINC, Enamine data sets) with developed models resulted in no hits. Therefore, small focused libraries for two types of ligands were prepared on the basis of pharmacophore models. Their screening resulted in four potential ligands for open form of αIIbβ3 and four ligands for its closed form followed by their synthesis and in vitro tests. Experimental measurements of affinity for αIIbβ3 and ability to inhibit ADP-induced platelet aggregation (IC50) showed that two designed ligands for the open form 4c and 4d (IC50 = 6.2 nM and 25 nM, respectively) and one for the closed form 12b (IC50 = 11 nM) were more potent than commercial antithrombotic Tirofiban (IC50 = 32 nM).

Quantitative structure-activity relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists toward collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR studies, as well as encourage the use of high quality, validated QSARs for regulatory decision making.

In this paper we offer a novel approach for the structural interpretation of QSAR models. The major advantage of our developed methodology is its universality, i.e., it can be applied to any QSAR/QSPR model irrespective of chemical descriptors and machine learning methods applied. This universality was achieved by using only the information obtained from substructures of the compounds of interest to interpret model outcomes. Reliability of the offered approach was confirmed by the results of three case studies, including end-points of different types (continuous and binary classification) and nature (solubility, mutagenicity, and inhibition of Transglutaminase 2), various fragment and whole-molecule descriptors (Simplex and Dragon), and multiple modeling techniques (partial least squares, random forest, and support vector machines). We compared the global contributions of molecular fragments obtained using our methodology with known SAR rules derived experimentally. In all cases high concordance between our interpretation and results published by others was observed. Although the proposed interpretation approach could be easily extended to any type of descriptors, we would recommend using Simplex descriptors to achieve a larger variety of investigated molecular fragments. The developed approach is a good tool for interpretation of such "black box" models like random forest, neural networks, etc. Analysis of fragment global contributions and their deviation across a dataset could be useful for the identification of key fragments and structural alerts. This information could be helpful to maximize the positive influence of structural surroundings on the given fragment and to decrease the negative effects., (Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

A series of novel RGD mimetics containing phthalimidine fragment was designed and synthesized. Their antiaggregative activity determined by Born's method was shown to be due to inhibition of fibrinogen binding to αIIbβ₃. Molecular docking of RGD mimetics to αIIbβ₃ receptor showed the key interactions in this complex, and also some correlations have been observed between values of biological activity and docking scores. The single crystal X-ray data were obtained for five mimetics., (Copyright © 2013 The Authors. Published by Elsevier Ltd.. All rights reserved.)

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 1 : 1 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., (Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

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., (Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

The novel RGD mimetics with phthalimidine central fragment were synthesized with the use of 4-piperidine-4-yl-butyric, 4-piperidine-4-yl-benzoic, 4-piperazine-4-yl-benzoic and 1,2,3,4-tetrahydroisoquinoline-7-carboxylic acids as surrogates of Arg motif. The synthesized compounds potently inhibited platelet aggregation in vitro and blocked FITC-Fg binding to α(IIb)β(3) integrin in a suspension of washed human platelets. The key α(IIb)β(3) protein-ligand interactions were determined in docking experiments., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

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 (r²(ext) = 0.64 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).

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., (Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Background: 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., Discussion: 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., Conclusion: 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.

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 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., (Copyright © 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

The development of a new quantitative structure-property relationship (QSPR) model to predict aqueous solubility (S(w)) accurately for compounds of military interest is presented. The ability of the new model to predict solubility is assessed and compared to available experimental data. A large set of structurally diverse organic compounds was used in this analysis. SiRMS methodology was employed to develop PLS models based on 135 training compounds and predictive accuracy was tested for 155 compounds selected for that purpose. The use of descriptors calculated only from the 2D level of representation of molecular structure produces a well-fitted and robust QSPR model (R(2)=0.90; Q(2)=0.87). Predictive ability for the model produced in this study on external test set (R(test)(2)=0.81) is comparable to the predictive ability of EPI Suite 4.0. Consensus solubility predictions using SiRMS and EPI models for 25 compounds of military interest (not included into the training set) have been completed.

Water solubility values for 27 nitro compounds with experimentally measured values were computed using the conductor-like screening model for real solvent (COSMO-RS) based on the density functional theory and COSMO technique. We have found that the accuracy of the COSMO-RS approach for prediction of water solubility of liquid nitro compounds is impressively high (the errors are lower than 0.1 LU). However, for some solid nitro compounds, especially nitramines, there is sufficient disagreement between calculated and experimental values. In order to increase the accuracy of predictions the quantitative structure-property relationship (QSPR) part of the COSMO-RS approach has been modified. The solubility values calculated by the modified COSMO-RS method have shown much better agreement with the experimental values (the mean absolute errors are lower than 0.5 LU). Furthermore, this technique has been used for prediction of water solubility for an expanded set of 23 nitro compounds including nitroaromatic, nitramines, nitroanisoles, nitrogen rich compounds, and some their nitroso and amino derivatives with unknown experimental values. The solubility values predicted using the proposed computational technique could be useful for the determination of the environmental fate of military and industrial wastes and the development of remediation strategies for contaminated soils and waters. This predictive capability is especially important for unstable compounds and for compounds that have yet to be synthesized.

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.

A stimulating effect of Azotobacter vinelandii IMB B-7076 bacteria on germination of seeds and formation of germs of different plants has been shown. One of the factors, which determines such phenomenon is the accumulation of biologically active substances in culture medium. The researched bacteria of Azotobacter genus accumulate indoleacetic acid (IAA) in the culture medium. Other substances of indol nature have been identified in the culture medium of these bacteria. It has been shown, using the computer's program PASS that these substances cannot take part in the regulation of plants growth. However 1H-indole-3carboxaldehyde can be characterized by antagonistic activity.

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 (R2 = 0.96-0.98; Q2 = 0.91-0.93; R2 (test) = 0.89-0.92), 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.

The dependence of hydrolytic activity of artificial ribonucleases toward an HIV-I RNA fragment, a 21-mer oligonucleotide, and tRNA Asp on the structure of the RNase mimetic was analyzed. The quantitative structure-activity relationship (QSAR task) was determined by the method of simplex representation of the molecular structure where the amounts of four-atom fragments (simplexes) of fixed structure, symmetry, and chirality served as descriptors. Not only the types of atoms participating in simplexes but also their physicochemical properties (e.g., partial charges, lipophilicities, etc.) were taken into account. This allowed the estimation of the relative role of various factors affecting the interaction of molecules under study with the corresponding biological target. The 2D QSAR models obtained by the method of projection to latent structures have quite satisfactory statistical indices (R2 = 0.82-0.96; Q2 = 0.73-0.89), which help predict the activities of new compounds. The electrostatic properties of ribonuclease atoms were shown to contribute significantly to the manifestation of the hydrolytic activity of ribonucleases in the case of the 21-mer oligonucleotide and tRNA. In addition, the structural fragments that most greatly contribute to the alteration of the hydrolytic activity of RNases were identified. The models obtained were used for the virtual screening and molecular design of new highly efficient RNase mimetics.

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.

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.

The influence of molecular structure of 346 ligands on their affinity for 5-HT1A receptors was investigated. It was shown that the effectiveness of the proposed novel approach for interpretation of decision tree models compared favourably with the PLS method. In the context of the proposed approach, molecular fragments and their values of the relative influence on the affinity for 5-HT1A receptors were defined.

The 50% cytotoxic concentration (CC50) in HeLa cells, the 50% inhibitory concentration (IC50) against human rhinovirus 2 (HRV-2), and the selectivity index (SI = CC50/IC50) of [(biphenyloxy)propyl]isoxazole derivatives were used to develop quantitative structure-activity relationships (QSAR) based on simplex representation of molecular structure. Statistic characteristics for partial least-squares models are quite satisfactory (R2 = 0.838 - 0.918; Q2 = 0.695 - 0.87) for prediction of CC50, IC50, and SI values and permit the virtual screening and molecular design of new compounds with strong anti-HRV-2 activity. The quality of prognosis for designed compounds was additionally estimated by analysis of domain applicability for each QSAR model. A hypothesis to the effect that terminal benzene substituents must have negative electrostatic potential and definite length (approximately 5.5-5.6 A) to possess strong antiviral activity has been suggested. The quality of developed analysis, i.e., high level of antiviral action of three new designed compounds, has been confirmed experimentally.

Objectives: The objectives of this study were (i) to apply computer-based technologies to evaluate the structure of 48 N,N'-(bis-5-nitropyrimidyl)dispirotripiperazines which belong to a new class of highly active antiviral compounds binding to cell surface heparan sulphates, (ii) to understand the chemical- biological interactions governing their activities, and (iii) to design new compounds with strong antiviral activity., Methods: The logarithm of 50% cytotoxic concentration (CC(50)) in GMK cells, of 50% inhibitory concentration (IC(50)) against herpes simplex virus type 1, and of selectivity index (SI = CC(50)/IC(50)) was used to develop quantitative structure-activity relationships (QSARs) based on simplex representation of molecular structure. The QSAR model was applied to design new compounds. Two of these compounds were synthesized, physico-chemically characterized and tested for cytotoxicity and antiviral activity., Results: Statistic characteristics for partial least squares models allow the prediction of CC(50), IC(50) and SI values. The QSAR results demonstrate a high impact of individual structural fragments for antiviral activity. Molecular fragments that promote and interfere with antiviral activity were defined on the basis of the obtained models. Electrostatic factors (38%) and hydrophobicity (34%) were the most important determinants of antiherpetic activity. Using the established method, new potential dispirotripiperazine derivatives were computationally designed. Two of these computationally designed compounds were synthesized. The biological test results confirm the computationally predicted values of these compounds., Conclusions: The established QSAR model is suitable for the design of new antiherpetic compounds and prediction of their activity.

The search for the inhibitors of adenoviruses has been performed among the substances of new class NCM (nitrogen containing macroheterocycles) and their analogues that have high potential of pharmacological properties. We have found a number of NCM and their derivatives that inhibit the reproduction of adenoviruses to various degrees. For the prediction of NCM structure with antiadenoviral activity we have performed the computer modeling using QSAR approach on the basis of simplex representation of molecular structure (SiRMS).

In this work, a hierarchic system of QSAR models from 1D to 4D is considered on the basis of the simplex representation of molecular structure (SiRMS). The essence of this system is that the QSAR problem is solved sequentially in a series of the improved models of the description of molecular structure. Thus, at each subsequent stage of a hierarchic system, the QSAR problem is not solved ab ovo, but rather the information obtained from the previous step is used. Actually, we deal with a system of solutions defined more exactly. In the SiRMS approach, a molecule is represented as a system of different simplex descriptors (tetratomic fragments with fixed composition, structure, chirality and symmetry). The level of simplex-descriptor detail increases consecutively from 1D to 4D representations of molecular structure. It enables us to determine the fragments of structure that promote or interfere with the given biological activity easily. Molecular design of compounds with a given level of activity is possible on the basis of SiRMS. The efficiency of the method is demonstrated for the example of the analysis of substituted piperazines affinity for the 5-HT1A receptor.

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). 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 r=0.990-0.861; cross-validation coefficient CVR=0.914-0.633). 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.

A new 4D-QSAR approach has been considered. For all investigated molecules the 3D structural models have been created and the set of conformers (fourth dimension) have been used. Each conformer is represented as a system of different simplexes (tetratomic fragments of fixed structure, chirality and symmetry). The investigation of influence of molecular structure of macrocyclic pyridinophanes, their analogues and certain other compounds on anticancer and antiviral (anti-influenza, antiherpes and antiadenovirus) activity has been carried out by means of the 4D-QSAR. Statistic characteristics for QSAR of PLS (partial least squares) models are satisfactory (R = 0.92-0.97; CVR = 0.63-0.83). Molecular fragments increasing and decreasing biological activity were defined. This information may be useful for design, and direct synthesis of novel anticancer and antiviral agents.

Quantitative estimation of the structure-anticancer activity relationship in a series of macrocyclic Schiff bases of 2,6-bis(formylaryloxymethyl)pyridines was carried out by the topological approach. Correlation equations describing the relationship between the anticancer activity and structural parameters of the molecules studied and descriptors characterizing their structure were obtained on the basis of in vitro screening data. The influence of structure of the investigated substances as reflected by the parameters studied on the anticancer activity, was established.