Quantitative structure-activity relationship studies of HIV-1 integrase inhibition. 1. GETAWAY descriptors.

The GEometry, Topology, and Atom-Weights AssemblY (GETAWAY) approach has been applied to the study of the HIV-1 integrase inhibition of 172 compounds that belong to 11 different chemistry families. A model able to describe more than 68.5% of the variance in the experimental activity was developed with the use of the mentioned approach.

QSAR studies about cytotoxicity of benzophenazines with dual inhibition toward both topoisomerases I and II: 3D-MoRSE descriptors and statistical considerations about variable selection.

Deoxyribonucleic acid (DNA) topoisomerases are involved in diverse cellular processes, such as replication, transcription, recombination, and chromosome segregation. Searching new compounds that inhibit both topoisomerases I and II is very important due to the deficiency of the specific inhibitors to overcome multidrug resistance (MDR). A QSAR study was developed, employing the 3D-MoRSE descriptors and a set of 64 benzophenazines in order to model the inhibition of the topoisomerases I and II, expressed by the cytotoxicity of these compounds (IC(50)) versus drug-resistant human small cell lung carcinoma line cell H69/LX4.

Simple stochastic fingerprints towards mathematical modeling in biology and medicine. 3. Ocular irritability classification model.

MARCH-INSIDE methodology and a statistical classification method--linear discriminant analysis (LDA)--is proposed as an alternative method to the Draize eye irritation test. This methodology has been successfully applied to a set of 46 neutral organic chemicals, which have been defined as ocular irritant or nonirritant. The model allow to categorize correctly 37 out of 46 compounds, showing an accuracy of 80.

Simple stochastic fingerprints towards mathematical modeling in biology and medicine 2. Unifying Markov model for drugs side effects.

Most of present mathematical models for biological activity consider just the molecular structure. In the present article we pretend extending the use of Markov chain models to define novel molecular descriptors, which consider in addition other parameters like target site or biological effect. Specifically, this mathematical model takes into consideration not only the molecular structure but the specific biological system the drug affects too.

Quantitative structure-activity relationship studies for the prediction of antifungal activity of N-arylbenzenesulfonamides against Botrytis cinerea.

The Botrytis cinerea is one of the most interesting fungal pathogens. It can infect almost every plant and plant part and cause early latent infections which damage the fruit before ripening. The QSAR is an alternative method for the research of new and better fungicides against B.

Non-stochastic quadratic fingerprints and LDA-based QSAR models in hit and lead generation through virtual screening: theoretical and experimental assessment of a promising method for the discovery of new antimalarial compounds.

In order to explore the ability of non-stochastic quadratic indices to encode chemical information in antimalarials, four quantitative models for the discrimination of compounds having this property were generated and statistically compared. Accuracies of 90.2% and 83.

A computer-based approach to the rational discovery of new trichomonacidal drugs by atom-type linear indices.

Computational approaches are developed to design or rationally select, from structural databases, new lead trichomonacidal compounds. First, a data set of 111 compounds was split (design) into training and predicting series using hierarchical and partitional cluster analyses. Later, two discriminant functions were derived with the use of non-stochastic and stochastic atom-type linear indices.

A topological substructural approach for the prediction of P-glycoprotein substrates.

A topological substructural molecular design approach (TOPS-MODE) has been used to predict whether a given compound is a P-glycoprotein (P-gp) substrate or not. A linear discriminant model was developed to classify a data set of 163 compounds as substrates or nonsubstrates (91 substrates and 72 nonsubstrates). The final model fit the data with sensitivity of 82.

A radial-distribution-function approach for predicting rodent carcinogenicity.

Carcinogenic activity has been investigated using the Radial-Distribution-Function (RDF) approach. A discriminant model was developed to predict the carcinogenic and non-carcinogenic activity on a data set of 188 compounds. The percentage of overall classification was 76.

Quantitative structure activity relationship for the computational prediction of nitrocompounds carcinogenicity.

Several nitrocompounds have been screened for carcinogenicity in rodents, but this is a lengthy and expensive process, taking two years and typically costing 2.5 million dollars, and uses large numbers of animals. There is, therefore, much impetus to develop suitable alternative methods.

Novel 2D maps and coupling numbers for protein sequences. The first QSAR study of polygalacturonases; isolation and prediction of a novel sequence from Psidium guajava L.

The development of 2D graph-theoretic representations for DNA sequences was very important for qualitative and quantitative comparison of sequences. Calculation of numeric features for these representations is useful for DNA-QSAR studies. Most of all graph-theoretic representations identify each one of the four bases with a unitary walk in one axe direction in the 2D space.

Atom-based 3D-chiral quadratic indices. Part 2: prediction of the corticosteroid-binding globulinbinding affinity of the 31 benchmark steroids data set.

A quantitative structure-activity relationship (QSAR) study to predict the relative affinities of the steroid 'benchmark' data set to the corticosteroid-binding globulin (CBG) is described. It is shown that the 3D-chiral quadratic indices closely correlate with the measured CBG affinity values for the 31 steroids. The calculated descriptors were correlated with biological data through multiple linear regressions.

New tyrosinase inhibitors selected by atomic linear indices-based classification models.

In the present report, the use of the atom-based linear indices for finding functions that discriminate between the tyrosinase inhibitor compounds and inactive ones is presented. In this sense, discriminant models were applied and globally good classifications of 93.51% and 92.

Non-stochastic and stochastic linear indices of the molecular pseudograph's atom-adjacency matrix: a novel approach for computational in silico screening and "rational" selection of new lead antibacterial agents.

A novel approach (TOMOCOMD-CARDD) to computer-aided "rational" drug design is illustrated. This approach is based on the calculation of the non-stochastic and stochastic linear indices of the molecular pseudograph's atom-adjacency matrix representing molecular structures. These TOMOCOMD-CARDD descriptors are introduced for the computational (virtual) screening and "rational" selection of new lead antibacterial agents using linear discrimination analysis.

Stochastic entropy QSAR for the in silico discovery of anticancer compounds: prediction, synthesis, and in vitro assay of new purine carbanucleosides.

A Markov model based QSAR is introduced for the rational selection of anticancer compounds. The model discriminates 90.3% of 226 structurally heterogeneous anticancer/non-anticancer compounds in training series.

3D-chiral atom, atom-type, and total non-stochastic and stochastic molecular linear indices and their applications to central chirality codification.

Non-stochastic and stochastic 2D linear indices have been generalized to codify chemical structure information for chiral drugs, making use of a trigonometric 3D-chirality correction factor. These descriptors circumvent the inability of conventional 2D non-stochastic [Y. Marrero-Ponce.

Correcting the use of ensemble averages in the calculation of harmonics to noise ratios in voice signals (L).

A correcting formula for the estimation of harmonics-to-noise ratios (HNR) based on ensemble-averaging techniques is derived. The original method yields a biased approximation which is more accurate as the number of averaged pulses (N) increases. However, the method treats gradual waveform changes incorrectly as noise, which is worsened for large values of N.

A novel non-stochastic quadratic fingerprints-based approach for the 'in silico' discovery of new antitrypanosomal compounds.

A non-stochastic quadratic fingerprints-based approach is introduced to classify and design, in a rational way, new antitrypanosomal compounds. A data set of 153 organic chemicals, 62 with antitrypanosomal activity and 91 having other clinical uses, was processed by a k-means cluster analysis to design training and predicting data sets. Afterwards, a linear classification function was derived allowing the discrimination between active and inactive compounds.

Ligand-based virtual screening and in silico design of new antimalarial compounds using nonstochastic and stochastic total and atom-type quadratic maps.

Malaria has been one of the most significant public health problems for centuries. It affects many tropical and subtropical regions of the world. The increasing resistance of Plasmodium spp.

A linear discrimination analysis based virtual screening of trichomonacidal lead-like compounds: outcomes of in silico studies supported by experimental results.

A computational (virtual) screening test to identify potential trichomonacidals has been developed. Molecular structures of trichomonacidal and non-trichomonacidal drugs were represented using stochastic and non-stochastic atom-based quadratic indices and a linear discrimination analysis (LDA) was trained to classify molecules regarding their antiprotozoan activity. Validation tests revealed that our LDA-QSAR models recognize at least 88.

Unified drug-target interaction thermodynamic Markov model using stochastic entropies to predict multiple drugs side effects.

Most of present molecular descriptors consider just the molecular structure. In the present article we pretend extending the use of Markov chain (MC) models to define novel molecular descriptors, which consider in addition other parameters like target site or toxic effect. Specifically, this molecular descriptor takes into consideration not only the molecular structure but the specific system the drug affects too.

Proteins Markovian 3D-QSAR with spherically-truncated average electrostatic potentials.

Proteins 3D-QSAR is an emerging field of bioorganic chemistry. However, the large dimensions of the structures to be handled may become a bottleneck to scaling up classic QSAR problems for proteins. In this sense, truncation approach could be used as in molecular dynamic to perform timely calculations.

TOMOCOMD-CARDD, a novel approach for computer-aided 'rational' drug design: I. Theoretical and experimental assessment of a promising method for computational screening and in silico design of new anthelmintic compounds.

In this work, the TOMOCOMD-CARDD approach has been applied to estimate the anthelmintic activity. Total and local (both atom and atom-type) quadratic indices and linear discriminant analysis were used to obtain a quantitative model that discriminates between anthelmintic and non-anthelmintic drug-like compounds. The obtained model correctly classified 90.

Linear indices of the 'macromolecular graph's nucleotides adjacency matrix' as a promising approach for bioinformatics studies. Part 1: prediction of paromomycin's affinity constant with HIV-1 psi-RNA packaging region.

The design of novel anti-HIV compounds has now become a crucial area for scientists around the world. In this paper a new set of macromolecular descriptors (that are calculated from the macromolecular graph's nucleotide adjacency matrix) of relevance to nucleic acid QSAR/QSPR studies, nucleic acids' linear indices. A study of the interaction of the antibiotic Paromomycin with the packaging region of the HIV-1 psi-RNA has been performed as example of this approach.

Protein linear indices of the 'macromolecular pseudograph alpha-carbon atom adjacency matrix' in bioinformatics. Part 1: prediction of protein stability effects of a complete set of alanine substitutions in Arc repressor.

A novel approach to bio-macromolecular design from a linear algebra point of view is introduced. A protein's total (whole protein) and local (one or more amino acid) linear indices are a new set of bio-macromolecular descriptors of relevance to protein QSAR/QSPR studies. These amino-acid level biochemical descriptors are based on the calculation of linear maps on Rn[f k(xmi):Rn-->Rn] in canonical basis.