Enrique Molina

3D connectivity indices in QSPR/QSAR studies.

Topographic (3D) molecular connectivity indices based on molecular graphs weighted with quantum chemical parameters are used in QSPR and QSAR studies. These descriptors were compared to 2D connectivity indices (vertex and edge ones) and to quantum chemical descriptors in modeling partition coefficient (log P) and antibacterial activity of 2-furylethylene derivatives. In describing log P the 3D connectivity indices produced a significant improvement (more than 29%) in the predictive capacity of the model compared to those derived with topological and quantum chemical descriptors. The best linear discriminant model for classifying antibacterial activity of these compounds was also obtained with the use of 3D connectivity indices. The global percent of good classification obtained with 3D and 2D connectivity as well as quantum chemical descriptors were 94.1, 91.2, and 88.2, respectively. In general, all these models predict correctly the antibacterial activity of a set of nine new 2-furylethylene derivatives. The best result is obtained with 3D connectivity indices that classified correctly 100% of these compounds versus 88.9% obtained with 2D connectivity or quantum chemical descriptors.

Designing antibacterial compounds through a topological substructural approach.

A novel application of TOPological Substructural MOlecular DEsign (TOPS-MODE) was carried out in antibacterial drugs using computer-aided molecular design. Two series of compounds, one containing antibacterial and the other containing non-antibacterial compounds, were processed by a k-means cluster analysis in order to design training and predicting series. All clusters had a p-level < 0.005. Afterward, a linear classification function has been derived toward discrimination between antibacterial and non-antibacterial compounds. The model correctly classifies 94% of active and 86% of inactive compounds in the training series. More specifically, the model showed a global good classification of 91%, i.e., 263 cases out of 289. In predicting series, the model has shown overall predictabilities of 91 and 83% for active and inactive compounds, respectively. Thereby, the model has a global percentage of good classification of 89%. The TOPS-MODE approach, also, similarly compares with respect to one of the most useful models for antimicrobials selection reported to date.

Can 3D structural parameters be predicted from 2D (topological) molecular descriptors

The dihedral angle between both phenyl rings determined by photoelectron spectroscopy in a series of seven alkylbiphenyl is described by the local spectral moments of the bond matrix. This series is extended to 78 alkylbiphenyl compounds by estimating the dihedral angle from molecular mechanics force field calculations. The linear correlation obtained between this angle and the local spectral moments shown a correlation coefficient of 0.9838. This result proves that 2D (topological) descriptors can account for 3D structural parameters. A new substituent constant is calculated as the contribution of groups to the studied rotational angle by using the information encoded into the local spectral moments. This substituent constant is not linearly related to the Tafts steric constants E(S) as they have a correlation coefficient of only 0.75. These steric constants are able to account only for 71% of the variance in the studied 3D parameter. The implications for QSPR/QSAR studies of the demonstration that 2D (topological) descriptors can describe 3D structural parameters are also analyzed.

Quantitative structure-toxicity relationships using TOPS-MODE. 2. Neurotoxicity of a non-congeneric series of solvents.

Abstract Neurotoxicities of a series of solvents in rats and mice have been modeled by means of the TOPS-MODE approach. Two quantitative structure-toxicity relationship (QSTR) models were obtained explaining more than 80% of the variance in the experimental values of neurotoxicity of 45 solvents. Only one compound was detected as statistical outlier for these models. In contrast, previous models explained less than 60% of the variance in this property for 44 solvents. Finally, the contributions to neurotoxicity in rats and mice for a series of structural fragments were found. Structural characteristics of chlorinated fragments responsible for their different neurotoxicities were analyzed. The differences in neurotoxic behavior of some fragments in rats and mice were also analyzed, which could give insights on the toxicological mechanism of action of solvents studied.

Structural Contributions of Substrates to their Binding to P-Glycoprotein. A TOPS-MODE Approach

A topological substructural molecular design approach (TOPS-MODE) has been used to formulate structural rules for binding of substrates of P-glycoprotein (P-gp). We first review some of the models developed in the recent literature for predicting binding to P-gp. Then, we develop a model using TOPS-MODE, which is able to identify 88.4% of substrates and 84.2% of non-substrates. When the model is presented to an external prediction set of 100 substrates and 77 nonsubstrates it identifies correctly 81.8% of all cases. Using TOPS-MODE strategy we found structural contributions for binding to P-gp, which identifies 24 structural fragments responsible for such binding. We then carried out a chemico-biological analysis of some of the structural fragments found as contributing to P-gp binding of substrates. We show that in general the model developed so far can be used as a virtual screening method for identifying substrates of P-gp from large libraries of compounds.

Monoamino Oxidase A: An Interesting Pharmacological Target for the Development of Multi-Target QSAR

With the significant increase of life expectancy of populations in societies today, the importance of the discovery of drugs associated with neurodegenerative diseases has emerged. Therefore, neurodegenerative diseases are an important topic in Medicinal Chemistry. Although drug discovery is considered a complex and slow process, new approaches and methods have been developed with the intention of finding new chemical entities in more efficient ways. This work provides a review of virtual methodologies applied in drug discovery and especially a new model for the prediction of MAO-A inhibitors using a multi-target QSAR methodology. This model involves a mixed approach containing simple descriptors based on atom-centered fragments and functional groups (DRAGON) and topological substructural molecular design descriptors (MODESLAB). This unified multi-species QSAR model was validated through a virtual screening of a new series of oxoisoaporphine derivatives, taking into account the information in the calculated fragmental contributions. Therefore, this method represents a useful tool for the in silico screening of MAO-A inhibitors.

QSAR Model: Prediction of the Clastogenic Potential of 3-Arylcoumarins

Drug discovery is a challenging task for researchers due to the complexity of biomolecules involved in pathologic processes. Design and development of efficient drugs is still urgent for several diseases. Cheminformatics tools are useful to better understand the interaction between new chemical entities and their targets. We studied a selected series of 3-arylcoumarins with antioxidant potential, and determined how their chemical features can contribute for the clastogenic activity. A virtual screening, based on the TOPSMODE approach, using a clastogenic model, was performed. The results suggested that the presence and position of hydroxyl groups in the scaffold is important for the activity. This communication is focused on cheminformatics, and its applications in drug effectiveness and safety.

In silico study of new structural alerts of agents clastogenic

Natural polyphenols and their derivatives from diet have been reported by their pro-oxidant and clastogenic activities. In an aim of elucidating structural alerts for this genotoxicity endpoint, a QSTR study was conducted under the TOPS-MODE approach. It was possible to establish structural alerts from the DNA oxidative damage as an endpoint of clastogenicity at optimum leaders with high probability of being clastogenic. Some important fragments to obviate this activity were also identified. The results constitute a reference system for designing new food or pharmaceutical matrices as an alternative to the experimental toxicology.