Guillermo Ramírez-Galicia

Effect of water molecules on the fluorescence enhancement of Aflatoxin B1 mediated by Aflatoxin B1:β-cyclodextrin complexes. A theoretical study

In order to explain the observed fluorescence enhancement of Aflatoxin B1 (AFB1) when forming AFB1:beta-cyclodextrin (AFB1:beta-CD) inclusion complexes, we have performed a theoretical (quantum chemistry calculations) study of AFB1 and AFB1:beta-CD in vacuum and in the presence of aqueous solvent. The AM1 method was used to calculate the absorption and emission wavelengths of these molecules. With the help of density functional theory (DFT) and time-dependent DFT (TDDFT) vibrational frequencies and related excitation energies of AFB1 and AFB1.(H2O)m = 4,5,6,11 were calculated. On the basis of these calculations we propose a plausible mechanism for the fluorescence enhancement of AFB1 in the presence of beta-CD: (1) before photoexcitation of AFB1 to its S1 excited state, there is a vibrational coupling between the vibrational modes involving the AFB1 carbonyl groups and the bending modes of the nearby water molecules (CG + WM); (2) these interactions allow a thermal relaxation of the excited AFB1 molecules that results in fluorescence quenching; (3) when the AFB1 molecules form inclusion complexes with beta-CD the CG + WM interaction decreases; and (4) this gives rise to a fluorescence enhancement.

Exploring QSAR of antiamoebic agents of isolated natural products by MLR, ANN, and RTO

A QSAR study of antiamoebic agents isolated from natural products was performed by multi linear regression (MLR), artificial neuron network (ANN), and regression through origin (RTO). After several procedures to reduce the number of descriptors, 11 descriptors were selected from the descriptor pool by a complete MLR methodology. The best proportion between training:predicted:validation sets is 100:43:16 molecules. The Mor23m descriptor is a 3D-MoRSE descriptor and it is the main descriptor in the models studied. This result suggests that the three-dimensional structure and atomic properties like masses are very important in the models. The best quantitative structure–activity relationship model was proved to be independent of chance correlation.

QSAR Study on the Antinociceptive Activity of Some Morphinans

Quantitative structure–activity relationship studies were performed to describe and predict the antinociceptive activity of 31 morphinan derivatives reported by the US Drug Evaluation Committee in 2005 and 2006. From these, three data sets were constructed and several models were calculated following the multiple linear regression and Leave‐One‐Out Cross‐Validation (LOO‐CV) tests. In general, these models achieved good descriptive power (approximately 92%) as well as predictive power (approximately 76%), but were unable to predict an external validation set of morphinan derivatives. When artificial neural networks were applied to these models, an improvement of the predictive and external validation values was obtained. It was observed that the results of the NN models are significantly better that those obtained by multiple linear regression. In spite that the problem under investigation can be handled adequately by a linear model, a neural network does bring slight improvements in the predictive power.

QSAR/QSPR as an Application of Artificial Neural Networks

Quantitative Structure-Activity Relationships (QSARs) and Quantitative Structure-Property Relationships (QSPRs) are mathematical models used to describe and predict a particular activity/property of compounds. On the other hand, the Artificial Neural Network (ANN) is a tool that emulates the human brain to solve very complex problems. The exponential need for new compounds in the drug industry requires alternatives for experimental methods to decrease development time and costs. This is where chemical computational methods have a great relevance, especially QSAR/QSPR-ANN. This chapter shows the importance of QSAR/QSPR-ANN and provides examples of its use.

MLR–ANN and RTO Approach to μ‐opioid Receptor‐binding Affinity. Pooling Data from Different Sources

One hundred and six morphinan derivatives were taken from the Drug Evaluation Committee reports to propose several quantitative structure–activity relationship models to describe the μ‐receptor‐binding affinity. After several procedures to reduce the descriptor number, 21 descriptors were selected for the descriptor pool by a complete Multiple Linear Regression methodology. In this procedure only three molecules were considered as outliers. Several tests changing the relation between training:predicted sets were considered to find the best relation between these sets. The higher the number of molecules in the predicted set the higher the predictive power was observed. The optimal number of descriptors was established using the Akaike’s information criterion and Kubinyi fitness function parameters. The Artificial Neuron Network methodology was applied to improve the Multiple Linear Regression best result. Finally, the regression through the origin methodology was applied to establish the best model from the Artificial Neuron Network methodology. The best quantitative structure–activity relationship model was proven to be independent of chance correlation.

Exploring the Ligand Recognition Properties of the Human Vasopressin V1a Receptor Using QSAR and Molecular Modeling Studies

Vaptans are compounds that act as non‐peptide vasopressin receptor antagonists. These compounds have diverse chemical structures. In this study, we used a combined approach of protein folding, molecular dynamics simulations, docking, and quantitative structure–activity relationship (QSAR) to elucidate the detailed interaction of the vasopressin receptor V1a (V1aR) with some of its blockers (134). QSAR studies were performed using MLR analysis and were gathered into one group to perform an artificial neural network (ANN) analysis. For each molecule, 1481 molecular descriptors were calculated. Additionally, 15 quantum chemical descriptors were calculated. The final equation was developed by choosing the optimal combination of descriptors after removing the outliers. Molecular modeling enabled us to obtain a reliable tridimensional model of V1aR. The docking results indicated that the great majority of ligands reach the binding site under π–π, π–cation, and hydrophobic interactions. The QSAR studies demonstrated that the heteroatoms N and O are important for ligand recognition, which could explain the structural diversity of ligands that reach V1aR.

Exploring QSARs for inhibitory effect of a set of heterocyclic thrombin inhibitors by multilinear regression refined by artificial neural network and molecular docking simulations

Several non-peptide heterocyclic compounds reported as potent thrombin inhibitors in vitro were chosen to carry out a QSAR study upon them using MLR and ANN analysis. In order to identify the best QSAR models, the input for ANN consisted of those subsets of descriptors used in the MLR models. The best QSAR models contained the SIC0 descriptor as the main topological descriptor. To identify the physical and chemical properties involved in the ligand–thrombin complexes, an automated ligand-flexible docking procedure was used. The docking results suggest that the thrombin inhibition by these heterocyclic compounds is driven by π–π, hydrogen bonds and salt bridge interactions. The best Gibbs free energy of ligand binding was found at the thrombin sites S1 and D. We have shown that it is possible to build MLR models with geometries taken from two different sources (semi-empirical and MD geometries) and obtain similar results.

QSAR study on the relaxant agents from some Mexican medicinal plants and synthetic related organic compounds.

Quantitative Structure–Activity Relationship studies were performed to describe and predict the antispasmodic activity of some molecules isolated from Mexican Medicinal Flora as well as for some synthetic ones based on stilbenoid bioisosteres. The relaxant activity of these molecules was taken from experiments on rat and guinea‐pig ileum tissues. Given that there is some evidence of species‐specific on the relaxant effects, two data sets were proposed, one for rat ileum and the other for guinea‐pig ileum. These data were statistically treated in order to find a Quantitative Structure–Activity Relationship model that could describe the corresponding biological models. The goodness of prediction for the best models was measured in terms of the Leave‐One‐Out Cross‐Validation R2 (LOO q2) and the correlation coefficients of regressions through the origin (RTO R20). Results show that papaverine activity could not be used as reference in rat ileum tests; however, this molecule can be used as a good reference molecule in guinea‐pig ileum tests. Our study shows that MATS5p and R8m+ descriptors are the most important descriptors in predicting the rat ileum activity and that atomic polarizability is the main atomic property. On the other hand, the R3u GETAWAY descriptor turns out to be important in predicting the guinea‐pig ileum activity where the influence/distance of substituents on these molecules could describe the observed activity.

A new model for the theoretical tautomeric constant (KT) calculation of 2-, 3- and 4-substituted pyridines☆

Abstract AM1, PM3, HF/STO-3G, HF/3-21G ∗ , HF/6-31G ∗ , MP2/STO-3G//HF/STO-3G, MP2/3-21G ∗ //HF/3-21G ∗ and MP2/6-32G ∗ //HF/6-31G ∗ calculations and the Boltzmann distribution law were used to establish a new model for the calculation of the tautomeric constant ( K T ) of 2-, 3- and 4-substituted pyridines in both gas and solution phase. The solvent effect was approximated with one water molecule around each tautomeric species. Three mathematical models were used to calculate the K T values. A linear correlation was carried out to show that some results were similar to the experimental ones.

A proposal for a oxycyclobuta Cope aromatic rearrangement of cis-1-cyano-1(4-methyl-3-methoxyphenyl)-2-isopropenyl-cyclobutane. AM1 study ☆

Abstract AM1 calculations were carried out to study the oxycyclobuta Cope aromatic rearrangement of the cis -1-cyano-1(4-methyl-3-methoxyphenyl)-2-isopropenyl-cyclobutane system. Eight possible pathways were analyzed for explaining this rearrangement (four conrotatory motions and four disrotatory motions). The results indicate that the reaction mechanism involves a tricyclic intermediate.