Modeling of the Acute Toxicity of Benzene Derivatives by Complementary QSAR Methods

A data set containing acute toxicity values (96-h LC50) of 69 substituted benzenes for fathead minnow (Pimephales promelas) was investigated with two Quantitative Structure- Activity Relationship (QSAR) models, either using or not using molecular descriptors, respectively. Recursive Neural Networks (RNN) derive a QSAR by direct treatment of the molecular structure, described through an appropriate graphical tool (variable-size labeled rooted ordered trees) by defining suitable representation rules. The input trees are encoded by an adaptive process able to learn, by tuning its free parameters, from a given set of structureactivity training examples. Owing to the use of a flexible encoding approach, the model is target invariant and does not need a priori definition of molecular descriptors. The results obtained in this study were analyzed together with those of a model based on molecular descriptors, i.e. a Multiple Linear Regression (MLR) model using CROatian MultiRegression selection of descriptors (CROMRsel). The comparison revealed interesting similarities that could lead to the development of a combined approach, exploiting the complementary characteristics of the two approaches

MI-NODES multiscale models of metabolic reactions, brain connectome, ecological, epidemic, world trade, and legal-social networks

[Abstract] Complex systems and networks appear in almost all areas of reality. We find then from proteins residue networks to Protein Interaction Networks (PINs). Chemical reactions form Metabolic Reactions Networks (MRNs) in living beings or Atmospheric reaction networks in planets and moons. Network of neurons appear in the worm C. elegans, in Human brain connectome, or in Artificial Neural Networks (ANNs). Infection spreading networks exist for contagious outbreaks networks in humans and in malware epidemiology for infection with viral software in internet or wireless networks. Social-legal networks with different rules evolved from swarm intelligence, to hunter-gathered societies, or citation networks of U.S. Supreme Court. In all these cases, we can see the same question. Can we predict the links based on structural information? We propose to solve the problem using Quantitative Structure-Property Relationship (QSPR) techniques commonly used in chemo-informatics. In so doing, we need software able to transform all types of networks/graphs like drug structure, drug-target interactions, protein structure, protein interactions, metabolic reactions, brain connectome, or social networks into numerical parameters. Consequently, we need to process in alignment-free mode multitarget, multiscale, and multiplexing, information. Later, we have to seek the QSPR model with Machine Learning techniques. MI-NODES is this type of software. Here we review the evolution of the software from chemoinformatics to bioinformatics and systems biology. This is an effort to develop a universal tool to study structure-property relationships in complex systems

Some Graphs Are More Strongly-Isospectral than Others

Abstract Let A be the adjacency matrix of a graph G, let D be its distance matrix and let V be the diagonal matrix with elements that indicate the valence of corresponding vertices. We explore possibility of discriminating the degree of similarity between isospectral graphs (having the same eigenvalues of the adjacency matrix A) by examining their spectral properties with respect to additional graph matrices: A -V matrix, which is essentially the Laplace matrix multiplied by -1; AA T -V matrix, which is obtained from AA T where elements on the main diagonal are replaced by zeros; natural distance matrix N DD, constructed from distances between columns of the adjacency matrix viewed as vectors in N-dimensional space; terminal matrix, which is really the distance matrix between the vertices of degree 1, also called terminal vertices. We found that matrices of form A m -V, the elements of which count non-returning walk of length m in a graph, discriminate some isospectral mates, but not others. We refer to pair of graphs which agree in eigenvalues of several matrices as stronglyisospectral, or S-isospectral graphs, as opposed to those less strongly similar. Hence, in other words, some graphs are more S-isospectral than other

Modeling complex metabolic reactions, ecological systems, and financial and legal networks with MIANN models based on Markov-Wiener node descriptors

[Abstract] The use of numerical parameters in Complex Network analysis is expanding to new fields of application. At a molecular level, we can use them to describe the molecular structure of chemical entities, protein interactions, or metabolic networks. However, the applications are not restricted to the world of molecules and can be extended to the study of macroscopic nonliving systems, organisms, or even legal or social networks. On the other hand, the development of the field of Artificial Intelligence has led to the formulation of computational algorithms whose design is based on the structure and functioning of networks of biological neurons. These algorithms, called Artificial Neural Networks (ANNs), can be useful for the study of complex networks, since the numerical parameters that encode information of the network (for example centralities/node descriptors) can be used as inputs for the ANNs. The Wiener index (W) is a graph invariant widely used in chemoinformatics to quantify the molecular structure of drugs and to study complex networks. In this work, we explore for the first time the possibility of using Markov chains to calculate analogues of node distance numbers/W to describe complex networks from the point of view of their nodes. These parameters are called Markov-Wiener node descriptors of order kth (Wk). Please, note that these descriptors are not related to Markov-Wiener stochastic processes. Here, we calculated the Wk(i) values for a very high number of nodes (>100,000) in more than 100 different complex networks using the software MI-NODES. These networks were grouped according to the field of application. Molecular networks include the Metabolic Reaction Networks (MRNs) of 40 different organisms. In addition, we analyzed other biological and legal and social networks. These include the Interaction Web Database Biological Networks (IWDBNs), with 75 food webs or ecological systems and the Spanish Financial Law Network (SFLN). The calculated Wk(i) values were used as inputs for different ANNs in order to discriminate correct node connectivity patterns from incorrect random patterns. The MIANN models obtained present good values of Sensitivity/Specificity (%): MRNs (78/78), IWDBNs (90/88), and SFLN (86/84). These preliminary results are very promising from the point of view of a first exploratory study and suggest that the use of these models could be extended to the high-throughput re-evaluation of connectivity in known complex networks (collation)

Modeling of the acute toxicity of benzene derivatives by complementary QSAR methods

A data set containing acute toxicity values (96-h LC50) of 69 substituted benzenes for fathead minnow (Pimephales promelas) was investigated with two Quantitative Structure- Activity Relationship (QSAR) models, either using or not using molecular descriptors, respectively. Recursive Neural Networks (RNN) derive a QSAR by direct treatment of the molecular structure, described through an appropriate graphical tool (variable-size labeled rooted ordered trees) by defining suitable representation rules. The input trees are encoded by an adaptive process able to learn, by tuning its free parameters, from a given set of structureactivity training examples. Owing to the use of a flexible encoding approach, the model is target invariant and does not need a priori definition of molecular descriptors. The results obtained in this study were analyzed together with those of a model based on molecular descriptors, i.e. a Multiple Linear Regression (MLR) model using CROatian MultiRegression selection of descriptors (CROMRsel). The comparison revealed interesting similarities that could lead to the development of a combined approach, exploiting the complementary characteristics of the two approaches

Character of Graphs with Extremal Balaban Index

NSFC [10831001]The Balaban index (also called J index) of a connected graph G is defined as J = J(G) = (vertical bar E(G)vertical bar)(mu+1) Sigma(u nu is an element of E(G)) (1)(root sigma G(u)sigma G(nu)), where sigma(G)(u) = Sigma(w is an element of V(G)) d(G)(u, w) and mu is the cyclomatic number. Balaban index has been used in various QSAR. and QSPR studies. In this paper, we characterize the graphs with both some parameters (such as the number of vertices, connectivity, diameter) and extreme Balaban indices

Nenad Trinajstić – Pioneer of Chemical Graph Theory

We present a brief overview of many contributions of Nenad Trinajstić to Chemical Graph Theory, an important and fast developing branch of Theoretical Chemistry. In addition, we outline briefly the various activities of Trinajstić within the chemical community of Croatia. As can be seen, his scientific work has been very productive and has not abated despite the hostilities towards the Chemical Graph Theory in certain chemical circles over the past 30 years. On the contrary, Trinajstić continued, widened the areas of his research interest, which started with investigating the close relationship between Graph Theory and HMO, and demonstrated the importance of Chemical Graph theory for chemistry. In more than one way he has proven the opponents of Chemical Graph Theory wrong, though some continue to fail to recognize the importance of Graph Theory in Chemistry

Designing low viscosity furan epoxy polymers of the materials for construction industry

The materials are designed and the properties are studied of the low viscosity furan­epoxy reactive oligomers, structured by amine complex curing agents for the use as injection systems during the repairing and recovery construction work. A wide range of the amine containing structuring agents that have industrial potential and ensure a high degree of conversion was analyzed. This makes it possible to form the rational structure of furan epoxy polymers, which is due to the application of optimal parameters of structuring (temperature, concentration of ingredients, time and others) during the formation of the polymeric composition systems for construction purposes. The structure topological parameters were studied (traditional topological criterion of Wiener and others) and certain parameters of reactivity (structural functionality, formal unlimitedness, the index of distribution of electron density on the atoms of molecule, etc.) parent substances (monomers) during obtaining furan epoxy materials. The knowledge of this set of characteristics makes it possible to purposefully regulate structure and properties of the designed low viscosity injection furan­epoxy polymeric materials. The deformation strength, adhesive, sorption, technological properties of the proposed low viscosity injection furan epoxy polymers for construction purposes were explored. As a result of the optimal combination of the set of structural topological parameters and technological factors, the high level of physical mechanical, adhesive properties, water resistance of the designed composite materials is ensured. The development of composite polymeric systems was accomplished with the use of the “green chemistry” principles.Розроблено склади і досліджені властивості низьков'язких фурано-епоксидних олігомерів, які структуровані амінними затверджувачами для використання при ремонтно-відновлювальних будівельних роботах. Визначено діапазон оптимальних параметрів структурування полімерних систем. Досліджені структурно-топологічні параметри і реакційна здатність вихідних речовин при отриманні фурано-епоксидних матеріалів. Проведено дослідження міцностних, адгезійних, сорбційних властивостей розроблених низьков'язких фурано-епоксидних полімерів