Event-based criteria in GT-STAF information indices: theory, exploratory diversity analysis and QSPR applications.

Versatile event-based approaches for the definition of novel information theory-based indices (IFIs) are presented. An event in this context is the criterion followed in the "discovery" of molecular substructures, which in turn serve as basis for the construction of the generalized incidence and relations frequency matrices, Q and F, respectively. From the resultant F, Shannon's, mutual, conditional and joint entropy-based IFIs are computed. In previous reports, an event named connected subgraphs was presented. The present study is an extension of this notion, in which we introduce other events, namely: terminal paths, vertex path incidence, quantum subgraphs, walks of length k, Sach's subgraphs, MACCs, E-state and substructure fingerprints and, finally, Ghose and Crippen atom-types for hydrophobicity and refractivity. Moreover, we define magnitude-based IFIs, introducing the use of the magnitude criterion in the definition of mutual, conditional and joint entropy-based IFIs. We also discuss the use of information-theoretic parameters as a measure of the dissimilarity of codified structural information of molecules. Finally, a comparison of the statistics for QSPR models obtained with the proposed IFIs and DRAGON's molecular descriptors for two physicochemical properties log P and log K of 34 derivatives of 2-furylethylenes demonstrates similar to better predictive ability than the latter.

On the contribution of molecular topology to drug design and discovery.

The role of molecular topology (MT) in the design and selection of new drugs is discussed. After an overview of the different in silico molecular design current technologies, the QSAR analysis is dealt in detail with particular emphasis in the use of topological indices as molecular descriptors. The results of the application of MT in drug design and discovery are described and finally a possible explanation is given about some of the key reasons explaining it's the extraordinary performance.

Application of molecular topology to the prediction of mosquito repellents of a group of terpenoid compounds.

A topological-mathematical model based on multilinear regression analysis has been built to search new terpenoid actives as mosquito repellents. The structural depiction was performed using topological indices, and a four-variable model for the prediction of corrected repellent ratio (r (2) = 0.9672, Q (2) = 0.9435) was selected. The model was checked by cross-validation, internal validation, and randomization test. The results confirm its capability to predict the property analyzed. After carrying out a virtual screening upon such a model, new structures with potential repellent activity against mosquitoes are proposed.

Aplicación de la Topología Molecular en la búsqueda de nuevos agentes activos frente a Leishmania / Application of the Molecular Topology in the search of new agents actives against Leishmania

Se ha desarrollado un modelo topológico-matemático encaminado a buscarnuevos compuestos derivados del 3,5 dinitrobenceno sulfonamida y/o benzamidaactivos frente a Leishmania. Utilizando el análisis lineal discriminante y el análisisde regresión multilineal se ha obtenido un modelo capaz de identificar la actividaddel 90% de los compuestos estudiados. Tras realizar un barrido virtual se proponennuevas estructuras potencialmente activas frente a L donovani

A topological-mathematical model has been arranged to search for newderivatives of 3,5-dinitrobenzene sulphonamide and/or benzamide, active againstL. donovani. By using linear discriminant analysis as well as multilinear regression,a model capable to identify the activity of 90% of the analyzed compounds hasbeen achieved. After carrying out a virtual screening based upon such a model, newstructures potentially active against the parasite are proposed

Aplicación de la topología molecular en la predicción de la inhibición de Trypanosoma cruzi Hexokinasa y un grupo de derivados bifosfonatos / Application of molecular topology to the prediction of inhibition of Trypanosoma cruzi Hexokinase by bisphosphonates

Se ha desarrollado un modelo topológico-matemático para la búsqueda de nuevos derivados bisfosfonatos activosfrente a la hexokinasa de Trypanosoma cruzi. Utilizando el análisis lineal discriminante se ha seleccionado una funcióncon cuatro variables capaz de predecir adecuadamente la CI50 para cada compuesto de las series de entrenamientoy test. El modelo propuesto se ha aplicado a una librería molecular y se han propuesto nuevas estructuraspotencialmente activas frente a T. cruzi

A topological-mathematical model has been arranged to search for new derivatives of bisphosphonate compounds actingas inhibitors against Trypanosoma cruzi hexokinase. By using linear discriminant analysis, a four-variable function wasachieved allowing an accurate prediction of the IC50 for each compound of the training and test series. After carryingout a virtual screening based upon such a model, new structures potentially actives against T. cruzi are proposed

Prediction of acute toxicity of organophosphorus pesticides using topological indices.

Topological indices were used in the prediction of the acute toxicity (intraperitoneal and oral LD(50)) of organophosphorus pesticides on rats. Models with six variables for the prediction of LD(50)-i.p. (r = 0.849, Q(2) = 0.613) and eight variables for LD(50)-oral (r = 0.906, Q(2) = 0.701) were selected. External group and cross-validation by use of leave-n-out tests were also performed in order to assess the stability and the prediction performance of the selected topological models.

True prediction of lowest observed adverse effect levels.

A database of structurally heterogeneous chemical structures with their experimental values of Lowest Observed Adverse Effect Levels (LOAELs) was modeled using graph theoretical descriptors. Variable selection for multiple linear regression (MLR) and linear discriminant analysis (LDA) was accomplished by the Internal Test Set (ITS) method in order to achieve true predicted LOAEL values. The results obtained can be considered good if we take in count the structural diversity of the training set.

Predictability and prediction of lowest observed adverse effect levels in a structurally heterogeneous set of chemicals.

A database of chronic lowest observed adverse effect levels (LOAELs) for 234 compounds, previously compiled from different sources (Toxicology Letters79, 131-143 (1995)), was modelled using graph theoretical descriptors. This study reveals that data are not homogeneous. Only those data originating from the U.S. Environmental Protection Agency (EPA) reports could be well modelled by multilinear regression (MLR) and linear discriminant analysis (LDA). In contrast, data available from the specific procedures of the National Toxicology Program (NTP) database introduced noise and did not render good models either alone, or in combination with the EPA data.

Structural invariants for the prediction of relative toxicities of polychloro dibenzo-p-dioxins and dibenzofurans.

Multivariate models are reported that can predict the relative toxicity of compounds with severe environmental impact, namely polychloro dibenzo-p-dioxins (PCDDs) and dibenzofurans (PCDFs). Multiple linear regression analysis (MLR) and partial least square projections of latent variables (PLS) show the usefulness of graph-theoretical descriptors, mainly topological charge indices (TCIs), in these series. The general trends of the group are correctly reproduced and better results are presented than have previously been published. In general, the more toxic compounds exhibit more symmetric molecular structures.

New hypoglycaemic agents selected by molecular topology.

New compounds showing hypoglycaemic activity have been designed through a computer aided method based on quantitative structure-activity relationship (QSAR) and molecular connectivity. After calculation of topological indices for a set of 89 compounds including active and inactive with regards to hypoglycaemic action, linear discriminant analysis was performed so that a useful model to predict such an activity was achieved. Later on, the discriminant model was applied on a huge database so that fourteen compounds were selected as potential new hypoglycaemics. From them, just five were finally selected for experimental test on expected hypoglycaemic activity. Among the selected compounds, l-arabitol, Acid blue 161, 1,4-butanediol diglycidil ether and Acid red 151 stand out, which are comparable in potency to standard drugs such as tolbutamide. Acid blue has a glycaemia profile similar to that of tolbutamide but does not lead to a severe hypoglycaemic condition, while the profile of the other agents is near normality.

Topological virtual screening: a way to find new anticonvulsant drugs from chemical diversity.

A topological virtual screening (tvs) test is presented, which is capable of identifying new drug leaders with anticonvulsant activity. Molecular structures of both anticonvulsant-active and non active compounds, extracted from the Merck Index database, were represented using topological indexes. By means of the application of a linear discriminant analysis to both sets of structures, a topological anticonvulsant model (tam) was obtained, which defines a connectivity function. On the basis of this model, 41 new structures with anticonvulsant activity have been identified by a topological virtual screening.

Use of molecular topology for the prediction of physico-chemical, pharmacokinetic and toxicological properties of a group of antihistaminic drugs.

We used molecular connectivity to search mathematical models for predicting physico-chemical (e.g. the partition coefficient, P), pharmacokinetic (e.g. the time of maximum plasma level, and toxicological properties (lethal dose, LD) for a group of antihistaminic drugs. The results obtained clearly reveal the high efficiency of molecular topology for the prediction of these properties. Randomization and cross-validation by use of leave-one-out tests were also performed in order to assess the stability and the prediction ability of the connectivity functions selected.

Search of a topological pattern to evaluate toxicity of heterogeneous compounds.

Molecular connectivity has been applied to the search of mathematical models able to predict the carcinogenic and teratogenic activity of a wide group of structurally heterogeneous compounds. Through the linear discriminant analysis and the diagrams of distribution of pharmacological activity, the classification criteria that minimizes the percentage of error are established. The easiness and speed of the calculation of the descriptors used in this work make the models developed useful in data bases containing a huge number of compounds.

Prediction of the chemiluminescent behavior of pharmaceuticals and pesticides.

The present paper deals with the first attempt to apply molecular connectivity calculations to predict a chemical property with analytical usefulness: the chemiluminescent behavior of substances when reacted with common oxidants in a liquid phase. Preliminary evidence when searching for new direct CL methods consisted of the examination of analyte reaction with a wide range of oxidants and media. This task, which results in time-consuming and trial-and-error expensive procedures, is necessary due to ensure empirical or theoretical rules for CL prediction are available. On the other hand, in quantitative structure-activity relationship studies, molecular connectivity is a topological method capable of describing the structure of a molecule by means of numbers named indices; subsequent regression in relation to the experimental values of the physical, chemical, or biological properties yields a series of functions called connectivity functions. Discriminant analysis was applied to 200 either chemiluminescent or nonchemiluminescent substances found either bibliographically or in an experimental screening. The method used for the selection of descriptors was a stepwise linear discriminant analysis from the Snedecor F-parameter. The classification criterion used was the minimum value of Mahalanobis. The quality of the discriminant function was calculated through the Wilks U-statistical parameter. Finally, the function was applied to a database including of more than 50,000 structurally heterogeneous compounds. The theoretical predictions were faced with the empirical evidence obtained through a continuous-flow manifold.

Optimization of a mathematical topological pattern for the prediction of antihistaminic activity.

Molecular topology was used to develop a mathematical model capable of classifying compounds according to antihistaminic activity. The equations used for this purpose were derived using multilinear regression and linear discriminant analysis. The topological pattern of activity obtained allows the reliable prediction of antihistaminic activity in drugs frequently used for other therapeutic purposes. Based on the results, the proposed pattern is seemingly only valid for drugs that interact with histamine through competitive inhibition with H1 receptors.

Getting discriminant functions of antibacterial activity from physicochemical and topological parameters.

Linear discriminant analysis has been demonstrated to be a very useful tool in the selection and design of new drugs. Up to now we have used it through the search of a topological pattern of activity. In this work our goal is to calculate a complete set of physicochemical parameters using semiempirical (quantum chemical) calculations as well as topological indices (TIs) and try to find out any discriminant function for antibacterial activity through the combined use of both types of descriptors. The physicochemical parameters, such as heat of formation, HOMO, LUMO, dipole moment, polarizability, hyperpolarizability, PM3 generated IR vibrational frequencies, etc., were calculated using PM3 Hamiltonian implemented within the MOPAC97 package. Among the TIs, connectivity as well as topological charge indices stands as the most representatives. The obtained results suggest that one of the maxima and minima vibrational frequencies play an important role in the antibacterial activity. These frequencies are associated with the torsional molecular vibration (N3) and the stretching vibration (N5) of X-H groups (X = C, N, O). Furthermore, the differences between the maxima and minima values showed an even better discriminant ability than the values themselves. The additional use of the topological indices provided a clear improvement in the discriminant function and also provided a straightforward way to predict the values of such frequencies, so that the results can be applied to a large set of compounds searching for new candidates as antibacterials.

General topological patterns of known drugs.

Discriminating "drug-like" from "non-drug-like" compounds is a relatively emerging topic within the drug research. The basic assumption is that it is possible to obtain relevant information from structural features common to the known drugs, in order to discard a huge number of candidate chemical structures with low probability of becoming drugs. A graph-theoretical contribution to this subject is reported in this paper, by making exclusive use of linear relationships. The results suggest that it is possible to achieve a pattern of general pharmacological activity based on molecular topology. Conclusions are tentative pending verification of the results with larger compound libraries.

Prediction of quinolone activity against Mycobacterium avium by molecular topology and virtual computational screening.

We conducted a quantitative structure-activity relationship study using a database of 158 quinolones previously tested against Mycobacterium avium-M. intracellulare complex in order to develop a model capable of predicting the activity of new quinolones against the M. avium-M. intracellulare complex in vitro. Topological indices were used as structural descriptors and were related to anti-M. avium-M. intracellulare complex activity by using the linear discriminant analysis (LDA) statistical technique. The discriminant equation thus obtained correctly classified 137 of the 158 quinolones, including 37 of a test group of 44 randomly chosen compounds. This model was then applied to 24 quinolones, including recently developed fluoroquinolones, whose MICs were subsequently determined in vitro by using the Alamar blue microplate assay; the biological results confirmed the model's predictions. The MICs of these 24 quinolones were then treated by multilinear regression (MLR) to establish a model capable of classifying them according to their in vitro activities. Using this model, a good correlation between measured and predicted MICs was found (r(2) = 0.88; r(2)(cv) [cross-validation correlation] = 0.82). Moxifloxacin, sparfloxacin, and gatifloxacin were the most potent against the M. avium- M. intracellulare complex, with MICs of 0.2, 0.4, and 0.9 microg/ml, respectively. Finally, virtual modifications of these three drugs were evaluated in LDA and MLR models in order to determine the importance of different substituents in their activity. We conclude that the combination of molecular-topology methods, LDA, and MLR provides an excellent tool for the design of new quinolone structures with enhanced activity.

Anti-toxoplasma activities of 24 quinolones and fluoroquinolones in vitro: prediction of activity by molecular topology and virtual computational techniques.

The apicoplast, a plastid-like organelle of Toxoplasma gondii, is thought to be a unique drug target for quinolones. In this study, we assessed the in vitro activity of quinolones against T. gondii and developed new quantitative structure-activity relationship models able to predict this activity. The anti-Toxoplasma activities of 24 quinolones were examined by means of linear discriminant analysis (LDA) using topological indices as structural descriptors. In parallel, in vitro 50% inhibitory concentrations (IC(50)s) were determined in tissue culture. A multilinear regression (MLR) analysis was then performed to establish a model capable of classifying quinolones by in vitro activity. LDA and MLR analysis were applied to virtual structures to identify the influence of each atom or substituent of the quinolone ring on anti-Toxoplasma activity. LDA predicted that 20 of the 24 quinolones would be active against T. gondii. This was confirmed in vitro for most of the quinolones. Trovafloxacin, grepafloxacin, gatifloxacin, and moxifloxacin were the quinolones most potent against T. gondii, with IC(50)s of 0.4, 2.4, 4.1, and 5.1 mg/liter, respectively. Using MLR analysis, a good correlation was found between measured and predicted IC(50)s (r(2) = 0.87, cross-validation r(2) = 0.74). MLR analysis showed that the carboxylic group at position C-3 of the quinolone ring was not essential for anti-Toxoplasma activity. In contrast, activity was totally dependent on the presence of a fluorine at position C-6 and was enhanced by the presence of a methyl group at C-5 or an azabicyclohexane at C-7. A nucleophilic substituent at C-8 was essential for the activity of gatifloxacin and moxifloxacin.

Molecular topology: a useful tool for the search of new antibacterials.

Molecular topology has been applied to find new lead antibacterial compounds. Among the selected compounds, hesperidin, neohesperidin and Mordant Brown 24 stand out, with minimum inhibitory concentrations 90, MIC90 < 0.3 mg/mL.