The Rücker–Markov invariants of complex bio-systems: applications in parasitology and neuroinformatics

[Abstract] Rücker's walk count (WC) indices are well-known topological indices (TIs) used in Chemoinformatics to quantify the molecular structure of drugs represented by a graph in Quantitative structure–activity/property relationship (QSAR/QSPR) studies. In this work, we introduce for the first time the higher-order (kth order) analogues (WCk) of these indices using Markov chains. In addition, we report new QSPR models for large complex networks of different Bio-Systems useful in Parasitology and Neuroinformatics. The new type of QSPR models can be used for model checking to calculate numerical scores S(Lij) for links Lij (checking or re-evaluation of network connectivity) in large networks of all these fields. The method may be summarized as follows: (i) first, the WCk(j) values are calculated for all jth nodes in a complex network already created; (ii) A linear discriminant analysis (LDA) is used to seek a linear equation that discriminates connected or linked (Lij = 1) pairs of nodes experimentally confirmed from non-linked ones (Lij = 0); (iii) The new model is validated with external series of pairs of nodes; (iv) The equation obtained is used to re-evaluate the connectivity quality of the network, connecting/disconnecting nodes based on the quality scores calculated with the new connectivity function. The linear QSPR models obtained yielded the following results in terms of overall test accuracy for re-construction of complex networks of different Bio-Systems: parasite–host networks (93.14%), NW Spain fasciolosis spreading networks (71.42/70.18%) and CoCoMac Brain Cortex co-activation network (86.40%). Thus, this work can contribute to the computational re-evaluation or model checking of connectivity (collation) in complex systems of any science field.Programa Iberoamericano de Ciencia y Tecnología para el Desarrollo; Ibero-NBIC, 209RT-0366Ministerio de Ciencia e Innovación; TIN2009-0770

Extending Graph (Discrete) Derivative Descriptors to N-Tuple Atom-Relations

In the present manuscript, an extension of the previously defined Graph Derivative Indices (GDIs) is discussed. To achieve this objective, the concept of a hypermatrix, conceived from the calculation of the frequencies of triple and quadruple atom relations in a set of connected sub-graphs, is introduced. This set of subgraphs is generated following a predefined criterion, known as the event (S), being in this particular case the connectivity among atoms. The triple and quadruple relations frequency matrices serve as a basis for the computation of triple and quadruple discrete derivative indices, respectively. The GDIs are implemented in a computational program denominated DIVATI (acronym for DIscrete DeriVAtive Type Indices), a module of TOMOCOMD-CARDD program. Shannon‟s entropy-based variability analysis demonstrates that the GDIs show major variability than others indices used in QSAR/QSPR researches. In addition, it can be appreciated when the indices are extended over n-elements from the graph, its quality increases, principally when they are used in a combined way. QSPR modeling of the physicochemical properties Log P and Log K of the 2-furylethylenes derivatives reveals that the GDIs obtained using the tripleand quadruple matrix approaches yield superior performance to the duplex matrix approach. Moreover, the statistical parameters for models obtained with the GDI method are superior to those reported in the literature by using other methods. It can therefore be suggested that the GDI method, seem to be a promissory tool to reckon on in QSAR/QSPR studies, virtual screening of compound datasets and similarity/dissimilarity evaluations

QSAR study for carcinogenicity in a large set of organic compounds

In our continuing efforts to find out acceptable Absorption, Distribution, Metabolization, Elimination and Toxicity (ADMET) properties of organic compounds, we establish linear QSAR models for the carcinogenic potential prediction of 1464 compounds taken from the "Galvez data set", that include many marketed drugs. More than a thousand of geometry-independent molecular descriptors are simultaneously analyzed, obtained with the softwares E-Dragon and Recon. The variable subset selection method employed is the Replacement Method, and also the improved version Enhanced Replacement Method. The established models are properly validated through an external test set of compounds, and by means of the Leave-Group-Out Cross Validation method. In addition, we apply the Y-Randomization strategy and analyze the Applicability Domain of the developed model. Finally, we compare the results obtained in present study with the previous ones from the literature. The novelty of present work relies on the development of an alternative predictive structure-carcinogenicity relationship in a large heterogeneous set of organic compounds, by only using a reduced number of geometry independent molecular descriptors.Fil: Duchowicz, Pablo Román. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; ArgentinaFil: Comelli, Nieves Carolina. Universidad Nacional de Catamarca. Facultad de Ciencias Agrarias; ArgentinaFil: Ortiz, Erlinda del Valle. Universidad Nacional de Catamarca. Facultad de Tecnología y Ciencias Aplicadas; ArgentinaFil: Castro, Eduardo Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; Argentin

An Improved QSPR Modeling of Hydrocarbon Dipole Moments

Dipole moments of hydrocarbons are not an easy property to model with conventional 2D descriptors. A comparison of the performance of the most commonly used sets of topological descriptors is presented, each set containing descriptors derived from the regular and Detour distance matrix, Electrotopological State Indices, and the basic number of atoms of each type and bonds. Data were taken on a representative set of 35 hydrocarbon dipole moments previously reported and the classical multivariable regression analysis for establishing the models is employed

More on Comparison Between First Geometric-Arithmetic Index and Atom-Bond Connectivity Index

The first geometric-arithmetic (GA) index and atom-bond connectivity (ABC) index are molecular structure descriptors which play a significant role in quantitative structure-property relationship (QSPR) and quantitative structure-activity relationship (QSAR) studies. Das and Trinajsti\'{c} [\textit{Chem. Phys. Lett.} \textbf{497} (2010) 149-151] showed that $GA$ index is greater than $ABC$ index for all those graphs (except $K_{1,4}$ and $T^{*}$, see Figure 1) in which the difference between maximum and minimum degree is less than or equal to 3. In this note, it is proved that $GA$ index is greater than $ABC$ index for line graphs of molecular graphs, for general graphs in which the difference between maximum and minimum degree is less than or equal to $(2\delta-1)^{2}$ (where $\delta$ is the minimum degree and $\delta\geq2$) and for some families of trees. Thereby, a partial solution to an open problem proposed by Das and Trinajsti\'{c} is given.Comment: 10 pages, 2 tables, 1 figure, revised versio

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

QSPR studije karbonilnih, hidroksilnih, polienskih indeksa i prosječne molekulske težine polimera pod fotostabilizacijom pristupom ANN i MLR

One of the main disadvantages of the use of synthetic or semi-synthetic polymeric materials is their degradation and aging. The purpose of this study was to use artificial neural networks (ANN) and multiple linear regressions (MLR) to predict the carbonyl, hydroxyl, and polyene indices (ICO, IOH, and IOP), and viscosity average molecular weight (MV) of poly(vinyl chloride), polystyrene, and poly(methyl methacrylate). These physicochemical properties are considered fundamental during the study of photostabilization of polymers. From the five repeating units of monomers, the structure of the polymer studied is shown. Quantitative structure-property relationship (QSPR) models obtained by using relevant descriptors showed good predictability. Internal validation {R2, RMSE, and Q2LOO}, external validation {R2, RMSE, Q2pred, rm2, Δrm2, k, and k’}, and applicability domain were used to validate these models. The comparison of the results shows that the ANN models are more efficient than those of the MLR models. Accordingly, the QSPR model developed in this study provides excellent predictions, and can be used to predict ICO, IOH, IOP, and MV of polymers, particularly for those that have not been tested. This work is licensed under a Creative Commons Attribution 4.0 International License.Jedan od glavnih nedostataka upotrebe sintetičkih ili polusintetičkih polimernih materijala je njihova razgradnja i starenje. Svrha ove studije je primjena umjetnih neuronskih mreža (ANN) i višestrukih linearnih regresija (MLR) za predviđanje karbonilnih, hidroksilnih i polienskih indeksa (ICO, IOH i IOP) i prosječne molekulske mase viskoznosti (MV) poli(vinil-klorida), polistirena i poli(metil metakrilata). Ta fizikalno-kemijska svojstva smatraju se važnim tijekom proučavanja fotostabilizacije polimera. Iz pet ponavljajućih jedinica monomera prikazana je struktura ispitivanog polimera. Kvantitativni modeli odnosa strukture-svojstava (QSPR) dobiveni primjenom relevantnih deskriptora pokazali su dobru predvidljivost. Za potvrdu tih modela provedene su: interna provjera {R2, RMSE i Q2LOO}, vanjska provjera {R2, RMSE, Q2pred, rm2, Δrm2, k i k’} i domena primjenjivosti. Usporedba rezultata pokazuje da su modeli ANN učinkovitiji od modela MLR. Prema tome, model QSPR razvijen u ovoj studiji pruža izvrsna predviđanja i može se primjenjivati za predviđanje ICO, IOH, IOP i MV polimera, posebno za one koji nisu testirani. Ovo djelo je dano na korištenje pod licencom Creative Commons Imenovanje 4.0 međunarodna

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