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Carmen Terán

Publications and source records attributed to Carmen Terán.

10 recordsLinked to original sources
PubMed2006

ANN-QSAR model for selection of anticancer leads from structurally heterogeneous series of compounds.

Developing a model for predicting anticancer activity of any classes of organic compounds based on molecular structure is very important goal for medicinal chemist. Different molecular descriptors can be used to solve this problem. Stochastic molecular descriptors so-called the MARCH-INSIDE approach, shown to be very successful in drug design. Nevertheless, the structural diversity of compounds is so vast that we may need non-linear models such as artificial neural networks (ANN) instead of linear ones. SmartMLP-ANN analysis used to model the anticancer activity of organic compounds has shown high average accuracy of 93.79% (train performance) and predictability of 90.88% (validation performance) for the 8:3-MLP topology with different training and predicting series. This ANN model favourably compares with respect to a previous linear discriminant analysis (LDA) model [H. González-Díaz et al., J. Mol. Model 9 (2003) 395] that showed only 80.49% of accuracy and 79.34% of predictability. The present SmartMLP approach employed shorter training times of only 10h while previous models give accuracies of 70-89% only after 25-46 h of training. In order to illustrate the practical use of the model in bioorganic medicinal chemistry, we report the in silico prediction, and in vitro evaluation of six new synthetic tegafur analogues having IC(50) values in a broad range between 37.1 and 138 microgmL(-1) for leukemia (L1210/0) and human T-lymphocyte (Molt4/C8, CEM/0) cells. Theoretical predictions coincide very well with experimental results.

Animals↗
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

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

A topological function based on spectral moments for predicting affinity toward A3 adenosine receptors.

The spectral moment descriptors have been applied to the study of affinity for A(3) adenosine receptors of 32 adenosine analogues. A model, able to describe more than 95% of the variance in the experimental activity, was developed with the use of the above-mentioned approach. The fragment contributions to the activity carried out show that the sulfonamido moiety at the N(6) position and hydrogen bonding play an important role in the interaction with the receptor.

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

BCUT descriptors to predicting affinity toward A3 adenosine receptors.

The BCUT descriptors have been applied to the study of the A(3) adenosine receptor agonist effect of 32 adenosine analogues. A model, able to describe more than 80% of the variance in the experimental activity was developed with the use of the above-mentioned approach. Four different approaches (topological, Galvez topological charges indexes, Randić molecular profiles, and geometrical descriptors) failed to give satisfactory models for this property with the same number of variables in the equation. Although statistically significant models were derived containing descriptors other than BCUT, the best fitted model was still found with these descriptors.

Adenosine↗
PubMed2005

Geometry, topology, and atom-weights assembly descriptors to predicting A1 adenosine receptors agonists.

The GEometry, Topology, and Atom-Weights AssemblY (GETAWAY) approach has been applied to the study of the A1 adenosine receptors agonist effect of 32 adenosine analogues: N6-arylcarbamoyl, 2-arylalkynyl-N6-arylcarbamoyl, and N6-carboxamido derivatives. A model, able to describe more than 77% of the variance in the experimental activity, was developed with the use of the above mentioned approach. Five different approaches (Topological, Galvez Topological Charges indexes, Randic Molecular Profiles, Geometrical, and WHIM descriptors) failed to give satisfactory models (R2=0.70) for this property with the same number of variables in the equation. Although statistically significant models were derived containing descriptors other than GETAWAY, the best fitted out model was still found with these descriptors.

Models, Molecular↗
PubMed2005

A radial distribution function approach to predict A(2B) agonist effect of adenosine analogues.

The radial distribution function (RDF) approach has been applied to the study of the A(2B) agonist effect of a set of 89 adenosine analogues reported with this activity. A model able to describe more than 70% of the variance in the experimental activity was developed with the use of the mentioned approach. In contrast, none of the eleven different approaches including the use of Constitutional, Topological, Molecular walk count, BCUT, Galvez topological charge indices, 2D autocorrelations, Randić molecular profiles, Geometrical, 3D Morse, WHIM and GETAWAY descriptors was able to explain more than 47% of the variance in the mentioned property with the same number of descriptors.

Adenosine↗
PubMed2002

Synthesis and structure-activity relationships of new arylpiperazines: para substitution with electron-withdrawing groups decrease binding to 5-HT(1A) and D(2A) receptors.

Compounds in which N-phenylpiperazines were linked by a propyloxy chain to position 6 or 7 of a coumarin ring were designed and synthesised, and their affinities for 5-HT(1A) and D(2A) receptors were determined by radioligand binding assays. The influence of para substitution in the phenyl ring, substitution at position 4 of the coumarin system, and the coumarin position at which the piperazinylalkyl chain is linked was explored. Electron-withdrawing phenyl ring substituents para to the piperazine strongly reduced activity at both receptors. Binding at 5HT(1A) was influenced by the bulk of substituents at position 4 of the coumarin system, and binding at D(2A) by their electronic properties. Neither binding affinity was significantly affected by whether the piperazinylalkyl chain was inserted at position 6 or 7 of the coumarin system.

8-Hydroxy-2-(di-n-propylamino)tetralin↗