Biomedical subjects

Aliuska Morales Helguera

Publications and source records attributed to Aliuska Morales Helguera.

11 recordsLinked to original sources

PubMed2006

QSAR studies using radial distribution function for predicting A1 adenosine receptors agonists.

The radial distribution function (RDF) approach has been applied to the study of the A(1) adenosine receptors agonist effect of 32 adenosine analogues. A model able to describe more than 79% of the variance in the experimental activity was developed with the use of the mentioned approach. In contrast, none of the three different approaches, including the use of 2D autocorrelations, BCUT and 3D-MORSE descriptors were able to explain more than 72% of the variance in the mentioned property with the same number of variables in the equation. In addition, we established a comparison with other models reported by us for this receptor subtype using this data set, and the RDF descriptors continue getting the best results.

Adenosine↗

PubMed2006

A topological substructural molecular design to predict soil sorption coefficients for pesticides.

A TOPological Sub-structural MOlecular DEsign (TOPS-MODE) approach was used to predict the soil sorption coefficients for a set of pesticide compounds. The obtained model accounted for more than 85% of the data variance and demonstrated the importance of the dipole moment, the standard distance, the polarizability, and the hydrophobicity in describing the property under study. In addition, we compared this new model to a previous one using different descriptors such as WHIM and molecular connectivity indices. Finally, the TOPS-MODE was used to calculate the contribution of different fragments to the soil sorption coefficient of the compounds studied. The present approximation proved to be a good method for studying the soil sorption coefficient for pesticides, but it could also be extended to other series of chemicals.

Adsorption↗

PubMed2006

2D autocorrelation modelling of the inhibitory activity of cytokinin-derived cyclin-dependent kinase inhibitors.

The inhibitory activity towards p34(cdc2)/cyclin b kinase (CBK) enzyme of 30 cytokinin-derived compounds has been successfully modelled using 2D spatial autocorrelation vectors. Predictive linear and non-linear models were obtained by forward stepwise multi-linear regression analysis (MRA) and artificial neural network (ANN) approaches respectively. A variable selection routine that selected relevant non-linear information from the data set was employed prior to networks training. The best ANN with three input variables was able to explain about 87% data variance in comparison with 80% by the linear equation using the same number of descriptors. Similarly, the neural network had higher predictive power. The MRA model showed a linear dependence between the inhibitory activities and the spatial distributions of masses, electronegativities and van der Waals volumes on the inhibitors molecules. Meanwhile, ANN model evidenced the occurrence of non-linear relationships between the inhibitory activity and the mass distribution at different topological distance on the cytokinin-derived compounds. Furthermore, inhibitors were well distributed regarding its activity levels in a Kohonen self-organizing map (SOM) built using the input variables of the best neural network.

Animals↗

PubMed2006

Quantitative structure activity relationships as useful tools for the design of new adenosine receptor ligands. 1. Agonist.

In order to minimize expensive drug failures it is essential to determine the potential biological activity of new candidates as early as possible. In view of the large libraries of nucleoside analogues that are now being handled in organic synthesis, the identification of a drugs biological activity is advisable even before synthesis and this can be achieved using predictive biological activity methods. In this sense, computer aided rational drug design strategies like Quantitative Structure Activity Relationships (QSAR) or docking approaches have emerged as promising tools. Although a large number of in silico approaches have been described in the literature for the prediction of different biological activities, the use of traditional QSAR applications in the development of new agonist molecules with affinity toward adenosine receptors is scarce. This review attempts to summarize the current level of knowledge concerning computational affinity predictions for adenosine receptors using QSAR models based on knowledge of the agonist ligands. Several computational protocols and different 2D and 3D descriptors have been described in the literature for these targets, but more effort is still required in this area.

Adenosine↗

PubMed2005

Radial distribution function descriptors: an alternative for predicting A2 A adenosine receptors agonists.

The Radial Distribution Function approach has been applied to the study of the A2 A adenosine receptors agonist effect of 29 adenosine analogues: N6- arylcarbamoyl, 2-arylalkynyl-N6 -arylcarbamoyl, and N6 -carboxamido derivatives. A model able to describe around 85% of the variance in the experimental activity was developed with the use of the mentioned approach. In contrast, no one of nine different approaches, including the use of Galvez Topological Charges indexes, BCUT, Geometrical, 2D autocorrelations, Topological, Randić Molecular profile, WHIM, 3D-MORSE and GETAWAY descriptors were able to explain more than 78% of the variance in the mentioned property with the same number of variables in the equation. Finally, the model support that the bulkiness and stereoselectivity play an important role in the affinity for this receptor in this kind of compounds.

Adenosine↗

PubMed2005

Modeling of farnesyltransferase inhibition by some thiol and non-thiol peptidomimetic inhibitors using genetic neural networks and RDF approaches.

Inhibition of farnesyltransferase (FT) enzyme by a set of 78 thiol and non-thiol peptidomimetic inhibitors was successfully modeled by a genetic neural network (GNN) approach, using radial distribution function descriptors. A linear model was unable to successfully fit the whole data set; however, the optimum Bayesian regularized neural network model described about 87% inhibitory activity variance with a relevant predictive power measured by q2 values of leave-one-out and leave-group-out cross-validations of about 0.7. According to their activity levels, thiol and non-thiol inhibitors were well-distributed in a topological map, built with the inputs of the optimum non-linear predictor. Furthermore, descriptors in the GNN model suggested the occurrence of a strong dependence of FT inhibition on the molecular shape and size rather than on electronegativity or polarizability characteristics of the studied compounds.

Enzyme Inhibitors↗

PubMed2005

Quantitative structure-activity relationship to predict differential inhibition of aldose reductase by flavonoid compounds.

Inhibitory activity against aldose reductase enzyme of flavonoid derivatives were modelled using 11 kinds of molecular descriptors from Dragon software. Model with four Galvez Charge Indices described 67% of data variance and overtaken other models using the same number of variables. Galvez indices showed to contain important information on the relationship between the inhibitor structures and its activity by describing the molecular topology and charge transfer through the molecule. In addition, artificial neural networks were trained using charge indices from the linear models but the obtaining networks overfitted the data having low predictive power.

Aldehyde Reductase↗

PubMed2005

A topological substructural approach applied to the computational prediction of rodent carcinogenicity.

The carcinogenic activity has been investigated by using a topological substructural molecular design approach (TOPS-MODE). A discriminant model was developed to predict the carcinogenic and noncarcinogenic activity on a data set of 189 compounds. The percentage of correct classification was 76.32%. The predictive power of the model was validated by three test: an external test set (compounds not used in the develop of the model, with a 72.97% of good classification), a leave-group-out cross-validation procedure (4-fold full cross-validation, removing 20% of compounds in each cycle, with a good prediction of 76.31%) and two external prediction sets (the first and second exercises of the National Toxicology Program). This methodology evidenced that the hydrophobicity increase the carcinogenic activity and the dipole moment of the molecule decrease it; suggesting the capacity of the TOPS-MODE descriptors to estimate this property for new drug candidates. Finally, the positive and negative fragment contributions to the carcinogenic activity were identified (structural alerts) and their potentialities in the lead generation process and in the design of 'safer' chemicals were evaluated.

Animals↗

PubMed2005

Quantitative structure-activity relationship to predict toxicological properties of benzene derivative compounds.

TOPological Sub-structural MOlecular DEsign (TOPS-MODE) was used to assess acute aquatic toxicity of a series of 69 benzene derivatives. The obtained model was able to explain more than 88% of data variance, stressing the importance of molecule hydrophobicity and its dipolar moment, as well as the distance between their bonds to describe the property under study. On the other hand, this model was better than those obtained with Dragon software (Constitutional, Galvez topological charges indices and BCUT) using the same number of variables. This approach proved to be a very good method to assess acute aquatic toxicity of these king of compounds, which could be applied to other series of substances.

Animals↗

PubMed2004

TOPS-MODE based QSARs derived from heterogeneous series of compounds. Applications to the design of new anti-inflammatory compounds.

A new application of TOPological Sub-structural MOlecular DEsign (TOPS-MODE) was carried out in anti-inflammatory compounds using computer-aided molecular design. Two series of compounds, one containing anti-inflammatory and the other containing nonanti-inflammatory compounds were processed by a k-means cluster analysis in order to design the training and prediction sets. A linear classification function to discriminate the anti-inflammatory from the inactive compounds was developed. The model correctly and clearly classified 88% of active and 91% of inactive compounds in the training set. More specifically, the model showed a good global classification of 90%, that is, (399 cases out of 441). While in the prediction set, they showed an overall predictability of 88% and 84% for active and inactive compounds, being the global percentage of good classification of 85%. Furthermore this paper describes a fragment analysis in order to determine the contribution of several fragments towards anti-inflammatory property, also the present of halogens in the selected fragments were analyzed. It seems that the present TOPS-MODE based QSAR is the first alternate general 'in silico' technique to experimentation in anti-inflammatory discovery.

Anti-Inflammatory Agents↗

PubMed2003

TOPS-MODE versus DRAGON descriptors to predict permeability coefficients through low-density polyethylene.

The TOPological Sub-Structural MOlecular DEsign (TOPS-MODE) approach has been applied to the study of the permeability coefficient of various compounds through low-density polyethylene at 0 degrees C. A model able to describe more than 92% of the variance in the experimental permeability of 38 organic compounds was developed with the use of the mentioned approach. In contrast, none of eight different approaches, including the use of constitutional, topological, BCUT, 2D autocorrelations, geometrical, RDF, 3D Morse, and GETAWAY descriptors was able to explain more than 75% of the variance in the mentioned property with the same number of descriptors. In addition, the TOPS-MODE approach permitted to find the contribution of different fragments to the permeability coefficients, giving to the model a straightforward structural interpretability.

Computational Biology↗