José M. García de la Vega

Basis set superposition error in MP2 and density-functional theory: A case of methane-nitric oxide association

A systematic study of basis set superposition error (BSSE) behavior in H(3)C-H[ellipsis (horizontal)][NO] complexes for both -H...N- and -H...O- orientations were carried out using MP2 and density-functional theory with Poples [6-31G(d,p),6-311++G(nd,nd), where n=1,2,3, and 6-311++G(3df,3pd)] and Dunnings augmented correlation consistent basis sets [aug-cc-pVXZ (X=D and T)]. Corrected and uncorrected counterpoise potential-energy surfaces (PESs) were explored and differences obtained between them indicate that reliable optimizations of these molecular interactions must be carried out in a PES free of BSSE, even in the case of large basis sets and popularly used functionals such as B3LYP. Although all basis used could be always considered within a margin of approximation for representing molecular orbitals and show important values of BSSE, 6-311++G(2d,2p) basis set shows the best results in uncorrected PES with respect to the corrected ones. B3LYP functional produces erratic results: complexes appear repulsive and the intermolecular distances are always large, evidencing the lack of a correct dispersive forces treatment in the original parameterization. According to the MP2 results, the -H...N- interactions appear as slightly more stable than those of the -H...O- orientation.

Applying pattern recognition methods plus quantum and physico‐chemical molecular descriptors to analyze the anabolic activity of structurally diverse steroids

The great cost associated with the development of new anabolic–androgenic steroid (AASs) makes necessary the development of computational methods that shorten the drug discovery pipeline. Toward this end, quantum, and physicochemical molecular descriptors, plus linear discriminant analysis (LDA) were used to analyze the anabolic/androgenic activity of structurally diverse steroids and to discover novel AASs, as well as also to give a structural interpretation of their anabolic–androgenic ratio (AAR). The obtained models are able to correctly classify 91.67% (86.27%) of the AASs in the training (test) sets, respectively. The results of predictions on the 10% full‐out cross‐validation test also evidence the robustness of the obtained model. Moreover, these classification functions are applied to an “in house” library of chemicals, to find novel AASs. Two new AASs are synthesized and tested for in vivo activity. Although both AASs are less active than some commercially AASs, this result leaves a door open to a virtual variational study of the structure of the two compounds, to improve their biological activity. The LDA‐assisted QSAR models presented here, could significantly reduce the number of synthesized and tested AASs, as well as could increase the chance of finding new chemical entities with higher AAR.

Two-configuration mc potential energy surface for the reaction of Mg with HF

Abstract The three-dimensional potential energy surface for the reaction of Mg and HF has been obtained from a two-configuration MC direct minimization method using an extended GTO basis set. Features of the surface were examined by fitting calculated values using cubic splines. The height of the barrier to reaction was found to be quite insensitive to the direction of the impinging Mg atom for a wide range of angles of approach. The transition state occurs in the exit channel at an angle of approach of 75°, 42 kcal/mole above the reactants asymptote. Alternative paths to reaction and possible dynamical properties of the system are also discussed.

On the unusual 2J C 2H f coupling dependence on syn/anti CHO conformation in 5‐X‐furan‐2‐carboxaldehydes

A remarkable difference for 2J  C 2H f coupling constant in syn and anti conformers of 5‐X‐furan‐2‐carboxaldehydes (X = CH3, Ph, NO2, Br) and a rationalization of this difference are reported. On the basis of the current knowledge of the Fermi‐contact term transmission, a rather unusual dual‐coupling pathway in the syn conformer is presented. The additional coupling pathway resembles somewhat that of the JHH in homoallylic couplings, which are transmitted by hyperconjugative interactions involving the πCC electronic system. The homoallylic coupling pathway can be labeled as σ*CH ← πCC → σ*CH. In the present case, this additional coupling pathway, using an analogous notation, can be labeled as σ*  C 2C C ← LP1(O1)··· LP2(OC) →σ*  C CH f (σ*  C 2C C ) where O1 and OC stand for the ring and carbonyl O atoms, respectively. This additional coupling pathway is not activated in the anti conformers since both oxygen lone pairs do not overlap. Copyright

Comparison of combinatorial clustering methods on pharmacological data sets represented by machine learning-selected real molecular descriptors.

Cluster algorithms play an important role in diversity related tasks of modern chemoinformatics, with the widest applications being in pharmaceutical industry drug discovery programs. The performance of these grouping strategies depends on various factors such as molecular representation, mathematical method, algorithmical technique, and statistical distribution of data. For this reason, introduction and comparison of new methods are necessary in order to find the model that best fits the problem at hand. Earlier comparative studies report on Wards algorithm using fingerprints for molecular description as generally superior in this field. However, problems still remain, i.e., other types of numerical descriptions have been little exploited, current descriptors selection strategy is trial and error-driven, and no previous comparative studies considering a broader domain of the combinatorial methods in grouping chemoinformatic data sets have been conducted. In this work, a comparison between combinatorial methods is performed,with five of them being novel in cheminformatics. The experiments are carried out using eight data sets that are well established and validated in the medical chemistry literature. Each drug data set was represented by real molecular descriptors selected by machine learning techniques, which are consistent with the neighborhood principle. Statistical analysis of the results demonstrates that pharmacological activities of the eight data sets can be modeled with a few of families with 2D and 3D molecular descriptors, avoiding classification problems associated with the presence of nonrelevant features. Three out of five of the proposed cluster algorithms show superior performance over most classical algorithms and are similar (or slightly superior in the most optimistic sense) to Wards algorithm. The usefulness of these algorithms is also assessed in a comparative experiment to potent QSAR and machine learning classifiers, where they perform similarly in some cases.

Alkyl substituent effect on density, viscosity and chemical behavior of 1-alkyl-3-methylimidazolium chloride

Molecular structure of the conformers of 1-C n-3-methylimidazolium chloride (n = 1 to 4) ionic liquids has been explored and the relationships with density and viscosity have been studied using COSMO related methodologies. Effects of the number of conformers, ionic character, anion-cation relative positions and the alkyl chain length of the cation on predictions of properties have been analyzed. The quality of the predictions has been tested by comparing with experimental results. Moreover, COSMO polarization charge densities, σ-profiles and σ-potentials of the conformers have been analyzed. Predictions on the chemical behavior based on the values of these properties in the conformers have been used to elucidate the affinity for electrophilic and nucleophilic reagents of ionic liquids.

Electron density analysis of 1-butyl-3-methylimidazolium chloride ionic liquid.

An analysis of the electron density of different conformers of the 1-butyl-3-methylimidazolium chloride (bmimCl) ionic liquid by using DFT through the BVP86 density functional has been obtained within the framework of Bader’s atom in molecules (AIM), localized orbital locator (LOL), natural bond orbital (NBO), and deformed atoms in molecules (DAM). We also present an analysis of the reduced density gradients that deliver the non-covalent interaction regions and allow to understand the nature of intermolecular interactions. The most polar conformer can be characterized as ionic by AIM, LOL, and DAM methods while the most stable and the least polar shows shared-type interactions. The NBO method allows to comprehend what causes the stabilization of the most stable conformer based on analysis of the second-order perturbative energy and the charge transferred among the natural orbitals involved in the interaction.

DOUBLE-ZETA SLATER-TYPE BASIS SETS WITH NONINTEGER PRINCIPAL QUANTUM NUMBERS AND COMMON EXPONENTS

Abstract A double-zeta basis set of extended Slater-type functions, whose principal quantum numbers are allowed to have noninteger values, is reported for the atoms He to Ar in their ground state. The total energies are lower than those from conventional and improved double-zeta basis sets, in which the principal quantum numbers are restricted to integer values. For the same atoms, we develop a new type of double-zeta basis set that combines the use of noninteger principal quantum numbers and the use of common exponents in Slater-type functions. The new double-zeta basis sets result in an improvement of the energy and a reduction of the computational time.

Chemometric and chemoinformatic analyses of anabolic and androgenic activities of testosterone and dihydrotestosterone analogues.

Predictive quantitative structure-activity relationship (QSAR) models of anabolic and androgenic activities for the testosterone and dihydrotestosterone steroid analogues were obtained by means of multiple linear regression using quantum and physicochemical molecular descriptors (MD) as well as a genetic algorithm for the selection of the best subset of variables. Quantitative models found for describing the anabolic (androgenic) activity are significant from a statistical point of view: R(2) of 0.84 (0.72 and 0.70). A leave-one-out cross-validation procedure revealed that the regression models had a fairly good predictability [q(2) of 0.80 (0.60 and 0.59)]. In addition, other QSAR models were developed to predict anabolic/androgenic (A/A) ratios and the best regression equation explains 68% of the variance for the experimental values of AA ratio and has a rather adequate q(2) of 0.51. External validation, by using test sets, was also used in each experiment in order to evaluate the predictive power of the obtained models. The result shows that these QSARs have quite good predictive abilities (R(2) of 0.90, 0.72 (0.55), and 0.53) for anabolic activity, androgenic activity, and A/A ratios, respectively. Last, a Williams plot was used in order to define the domain of applicability of the models as a squared area within +/-2 band for residuals and a leverage threshold of h=0.16. No apparent outliers were detected and the models can be used with high accuracy in this applicability domain. MDs included in our QSAR models allow the structural interpretation of the biological process, evidencing the main role of the shape of molecules, hydrophobicity, and electronic properties. Attempts were made to include lipophilicity (octanol-water partition coefficient (logP)) and electronic (hardness (eta)) values of the whole molecules in the multivariate relations. It was found from the study that the logP of molecules has positive contribution to the anabolic and androgenic activities and high values of eta produce unfavorable effects. The found MDs can also be efficiently used in similarity studies based on cluster analysis. Our model for the anabolic/androgenic ratio (expressed by weight of levator ani muscle, LA, and seminal vesicle, SV, in mice) predicts that the 2-aminomethylene-17alpha-methyl-17beta-hydroxy-5alpha-androstan-3-one (43) compound is the most potent anabolic steroid, and the 17alpha-methyl-2beta,17beta-dihydroxy-5alpha-androstane (31) compound is the least potent one of this series. The approach described in this report is an alternative for the discovery and optimization of leading anabolic compounds among steroids and analogues. It also gives an important role to electron exchange terms of molecular interactions to this kind of steroid activity.

Interaction of brassinolide with essential amino acid residues: A theoretical approach

The interaction of the most active natural brassinosteroid, brassinolide, with the twenty natural amino acids is studied applying the multiple minima hypersurface method to model the molecular interactions explicitly. The resulting thermodynamic data gives useful information about the amino acids with the greatest association for brassinolide and the stabilities of such complexes. Density functional theory (DFT) optimizations were further carried out to test the performance of semiempirical calculations. Additional calculations with a more accurate DFT method were performed to explore the formation of this type of molecular complexes. The semiempirical geometries and stability order of these complexes are in good agreement with the DFT calculations. Each group of amino acids possesses a preferential zone of interaction with brassinolide, forming the polar-charged amino acids the most stable complexes. This study could contribute to future investigations of the interaction of brassinosteroids with the receptor protein in plants.