Eduardo A. Castro

Large Order Perturbation Theory and Summation Methods in Quantum Mechanics

The problems usually found in Theoretical Chemistry and Physical Chemistry involve the use of quantum mechanical models which, as a general rule, do not have exact solutions. Due to this very compelling reason a formidable effort has been devoted to develop approximate methods to solve the Schrodinger equation from the very birth of the Quantum Mecha nics.

New QSPR study for the prediction of aqueous solubility of drug-like compounds

Solubility has become one of the key physicochemical screens at early stages of the drug development process. Solubility prediction through Quantitative Structure-Property Relationships (QSPR) modeling is a growing area of modern pharmaceutical research, being compatible with both High Throughput Screening technologies and limited compound availability characteristic of early stages of drug development. We resort to the QSPR theory for analyzing the aqueous solubility exhibited by 145 diverse drug-like organic compounds (0.781 being the average Tanimoto distances between all possible pairs of compounds in the training set). An accurate and generally applicable model is derived, consisting on a linear regression equation that involves three DRAGON molecular descriptors selected from more than a thousand available. Alternatively, we apply the linear QSPR to other 21 commonly employed validation compounds, leading to solubility estimations that compare fairly well with the performance achieved by previously reported Group Contribution Methods.

Replacement method and enhanced replacement method versus the genetic algorithm approach for the selection of molecular descriptors in QSPR/QSAR theories.

We compare three methods for the selection of optimal subsets of molecular descriptors from a much greater pool of such regression variables. On the one hand is our enhanced replacement method (ERM) and on the other is the simpler replacement method (RM) and the genetic algorithm (GA). These methods avoid the impracticable full search for optimal variables in large sets of molecular descriptors. Present results for 10 different experimental databases suggest that the ERM is clearly preferable to the GA that is slightly better than the RM. However, the latter approach requires the smallest amount of linear regressions and, consequently, the lowest computation time.

A comparative QSAR on 1,2,5-thiadiazolidin-3-one 1,1-dioxide compounds as selective inhibitors of human serine proteinases.

Selective inhibitors of target serine proteinases have a potential therapeutic role for the treatment of various inflammatory and related diseases. We develop a comparative quantitative structure-activity relationships based analysis on compounds embodying the 1,2,5-thiadiazolidin-3-one 1,1-dioxide scaffold. By means of classical Molecular Dynamics we obtain the conformation of each lowest-energy molecular structure from which we derive more than a thousand of structural descriptors necessary for building predictive QSAR models. We resort to two different modeling approaches with the purpose of testing the consistency of our results: (a) multivariable linear regressions based on the replacement method and forward stepwise regression, and (b) the calculation of flexible descriptors with the CORAL program. All the models are properly validated by means of standard procedures. The resulting QSAR models are supposed to be of great utility for the rational search and design (including synthesis and/or in vitro biochemical studies) of new effective non-peptidyl inhibitors of serine proteinases.

A molecular mechanics study of 1:1 complexes between azobenzene derivatives and β-cyclodextrin

Abstract Azobenzene derivatives in solution present thermal cis–trans isomerization through the double bond –N N–. Experimental results showed that β-cyclodextrin inhibited the isomerization process for some azoderivatives while others were not affected. As previous model studies on the inclusion complexation of cyclodextrins with various guests, offered significant insights into the non-covalent intermolecular interactions and theoretical calculations helped to illustrate the driving forces for the complexation, here we have undertaken a theoretical study of the entire process of the formation of 1:1 stoichiometry azobenzene/β-cyclodextrin structures, in order to contribute to the understanding and rationalization of the experimental results reported before. With this purpose, we first searched for a possible way of formation of each substituted azobenzene/β-cyclodextrin inclusion complex and then we have performed an analysis of the strain energy changes involved and we have also analyzed the interaction forces driving towards the different kinds of stable structures yielded by the calculation procedure.

Advances in the replacement and enhanced replacement method in QSAR and QSPR theories.

The selection of an optimal set of molecular descriptors from a much greater pool of such regression variables is a crucial step in the development of QSAR and QSPR models. The aim of this work is to further improve this important selection process. For this reason three different alternatives for the initial steps of our recently developed enhanced replacement method (ERM) and replacement method (RM) are proposed. These approaches had previously proven to yield near optimal results with a much smaller number of linear regressions than the full search. The algorithms were tested on four different experimental data sets, formed by collections of 116, 200, 78, and 100 experimental records from different compounds and 1268, 1338, 1187, and 1306 molecular descriptors, respectively. The comparisons showed that one of the new alternatives further improves the ERM, which has shown to be superior to genetic algorithms for the selection of an optimal set of molecular descriptors from a much greater pool. The new proposed alternative also improves the simpler and the lower computational demand algorithm RM.

QSAR prediction of inhibition of aldose reductase for flavonoids

We performed a predictive analysis based on quantitative structure-activity relationships (QSAR) of an important property of flavonoids, which is the inhibition (IC(50)) of aldose reductase (AR). The importance of AR inhibition is that it prevents cataract formation in diabetic patients. The best linear model constructed from 55 molecular structures incorporated six molecular descriptors, selected from more than a thousand geometrical, topological, quantum-mechanical, and electronic types of descriptors. As a practical application, we used the obtained QSAR model to predict the AR inhibitory effect of newly synthesized flavonoids that present 2-, 7-substitutions in the benzopyrane backbone.

New Hybrid Genetic Based Support Vector Regression as QSAR Approach for Analyzing Flavonoids-GABA(A) Complexes

Several studies were conducted in past years which used the evolutionary process of Genetic Algorithms for optimizing the Support Vector Regression parameter values although, however, few of them were devoted to the simultaneously optimization of the type of kernel function involved in the established model. The present work introduces a new hybrid genetic-based Support Vector Regression approach, whose statistical quality and predictive capability is afterward analyzed and compared to other standard chemometric techniques, such as Partial Least Squares, Back-Propagation Artificial Neural Networks, and Support Vector Machines based on Cross-Validation. For this purpose, we employ a data set of experimentally determined binding affinity constants toward the benzodiazepine binding site of the GABA (A) receptor complex on 78 flavonoid ligands.

Prediction of drug intestinal absorption by new linear and non-linear QSPR

In order to minimize the high attrition rate that usually characterizes drug research and development projects, current medicinal chemists aim to characterize both pharmacological and ADME profiles at the beginning of drug R&D initiatives. Thus, the development of ADME High-Throughput Screening in vitro and in silico ADME models has become an important growing research area. Here we present new linear and non-linear predictive QSPR models to predict the human intestinal absorption rate, which are derived from a medium sized, balanced and diverse training set of organic compounds. The structure-property relationships so obtained involve only 4 molecular descriptors, and display an excellent ratio of number of cases to number of descriptors. Their adjustment of the training set data together with the performance achieved during the internal and external validation procedures are comparable to previously reported modeling efforts.

Improved molecular descriptors to calculate boiling points based on the optimization of correlation weights of local graph invariants

Abstract We report the calculation of boiling points for several alkyl alcohols through the use of improved molecular descriptors based on the optimization of correlation weights of local invariants of graphs. As local invariants we have used the presence of different chemical elements (i.e. C, H, and O) and the existence of different vertex degree values (i.e. 1, 2, 3 and 4). The inherent flexibility of the chosen molecular descriptor seems to be rather suitable to obtain satisfactory predictions of the property under study. Comparison with other similar approximation reveals a very good behavior of the present method. The use of higher order polynomials do not seem to be necessary to improve the results regarding the simple linear fitting equations. Some possible future extensions are pointed out in order to achieve a more definitive conclusion about this approximation.