A computational strategy to understand structure-activity relationship of 1,3-disubstituted imidazole [1,5-α] pyrazine derivatives described as ATP competitive inhibitors of the IGF-1 receptor related to Ewing sarcoma.

We followed a comprehensive computational strategy to understand and eventually predict the structure-activity relationship of thirty-three 1,3-disubstituted imidazole [1,5-α] pyrazine derivatives described as ATP competitive inhibitors of the IGF-1 receptor related to Ewing sarcoma. The quantitative structure-activity relationship model showed that the inhibitory potency is correlated with the molar volume, a steric descriptor and the net charge calculated value on atom C1 (q1) and N4 (q4) of the pharmacophore, all of them appearing to give a positive contribution to the inhibitory activity. According to experimental and calculated values, the most potent compound would be 3-[4-(azetidin-2-ylmethyl) cyclohexyl]-1-[3-(benzyloxy) phenyl] imidazo [1,5-α]pyrazin-8-amine (compound 23). Docking was used to guess important residues involved in the ATP-competitive inhibitory activity. It was validated by 200 ns of molecular dynamics (MD) simulation using improved linear interaction energy (LIE) method. MD of previously preferred structures by docking shows that the most potent ligand could establish hydrogen bonds with the ATP-binding site of the receptor, and the Ser979 and Ser1059 residues contribute favourably to the binding stability of compound 23. MD simulation also gave arguments about the chemical structure of the compound 23 being able to fit in the ATP-binding pocket, expecting to remain stable into it during the entire simulation and allowing us to hint the significant contribution expected to be given by electrostatic and hydrophobic interactions to the ligand-receptor complex stability. This computational combined strategy here described could represent a useful and effective prime approach to guide the identification of tyrosine kinase inhibitors as new lead compounds.

Theoretical Evaluation of the Molecular Inclusion Process between Chlordecone and Cyclodextrins: A New Method for Mitigating the Basis Set Superposition Error in the Case of an Implicit Solvation Model.

The aim of this work is to describe the molecular inclusion of chlordecone with α-, ß-, and γ-cyclodextrin in aqueous solution using quantum mechanics. The guest-host complexes of chlordecone and cyclodextrins are modeled in aqueous solution using the multiple minima hypersurface methodology with a PM6-D3H4X semiempirical Hamiltonian, and the lowest energy minima obtained are reoptimized using the M06-2X density functional and the intermolecular interactions described using quantum theory of atoms in molecules (QTAIM). The studied complexes are classified according to the degree of inclusion, namely, total occlusion, partial occlusion, and external interaction. More stable complexes are obtained when γ-CD is used as the host molecule. The interactions characterized through QTAIM analysis are all of electrostatic nature, predominantly of dispersive type. In this work, a method based on the counterpoise correction is also discussed to mitigate the basis set superposition error in density functional theory calculations when using an implicit solvation model.

Electron density deformations provide new insights into the spectral shift of rhodopsins.

Spectral shifts of rhodopsin, which are related to variations of the electron distribution in 11-cis-retinal, are investigated here using the method of deformed atoms in molecules. We found that systems carrying the M207R and S186W mutations display large perturbations of the π-conjugated system with respect to wild-type rhodopsins. These changes agree with the predicted behavior of the bond length alternation (BLA) and the blue shifts of vertical excitation energies of these systems. The effect of the planarity of the central and Schiff-base regions of retinal chain on the electronic structure of the chromophore is also investigated. By establishing nonlinear polynomial relations between BLA, chain distortions, and vertical excitation energies, we are also able to provide a semiquantitative approach for the understanding of the mechanisms regulating spectral shifts in rhodopsin and its mutants.

Integration of ligand and structure-based virtual screening for identification of leading anabolic steroids.

Parallel ligand- and structure-based virtual screenings of 269 steroids with anabolic activity evaluated in vivo were performed. The quantitative structure-activity relationship (QSAR) model expressed by selected descriptors as the octanol-water partition coefficient, the molar volume and the quantum mechanical calculated charge values on atoms C1, C2, C5, C9, C10, C14 and C17 of the steroid skeleton, expresses structural features of anabolic steroids (AS) contributing to the transport and steroid-receptor interaction. On the other hand, computational simulations of a candidate ligand binding to a receptor study (a "docking" procedure) predict the association of these AS with the human androgen receptor (AR). Fourteen compounds were identified as lead; the most potent was the 7α-methylestr-4-en-3, 17-dione. It was concluded that a good anabolic activity requires hydrogen bonding interactions between both Arg752 and Gln711 residues in the cycles A with O3 atom of the steroid and either Asn705 and Thr877 residues in the cycles D of steroid with O17 atom.

Anabolic and androgenic activities of 19-nor-testosterone steroids: QSAR study using quantum and physicochemical molecular descriptors.

Quantitative structure-activity relationship (QSAR) study of 19-nor-testosterone steroids family was performed using quantum and physicochemical molecular descriptors. The quantum-chemical descriptors were calculated using semiempirical calculations. The descriptor values were statistically correlated using multi-linear regression analysis. The QSAR study indicated that the electronic properties of these derivatives have significant relationship with observed biological activities. The found QSAR equations explain that the energy difference between the LUMO and HOMO, the total dipole moment, the chemical potential and the value of the net charge of different carbon atoms in the steroid nucleus showed key interaction of these steroids with their anabolic-androgenic receptor binding site. The calculated values predict that the 17α-cyclopropyl-17ß, 3ß-hydroxy-4-estrene compound presents the highest anabolic-androgenic ratio (AAR) and the 7α-methyl-17ß-acetoxy-estr-4-en-3-one compound the lowest AAR. This study might be helpful in the future successful identification of "real" or "virtual" anabolic-androgenic steroids.

A theoretical approach to the solvation of brassinosteroids.

The interaction of three different brassinosteroids with water was studied by the Multiple Minima Hypersurface (MMH) procedure to model molecular interactions explicitly. The resulting thermodynamic data give useful information on properties of molecular association with water. This application can serve as a tool for future investigations and modelling concerning interactions of brassinosteroids with receptor proteins in plants. DFT/B3LYP calculations were also made in order to correlate and test the performance of the current AM1 Hamiltonian calculations of these complexes, which are inherent to MMH routine. Diol functionalities located in ring A and lateral chain appears as the sites that show the highest affinity to water. The oxalactone group does not appear to be a key structural requirement in the association with water. Parallel calculations with a "polarizable continuum method" (PCM) agreed with the reported experimental order of biological activities, where Brassinolide exhibited the best solubility features.

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.

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.

CNDOL: A fast and reliable method for the calculation of electronic properties of very large systems. Applications to retinal binding pocket in rhodopsin and gas phase porphine.

Very large molecular systems can be calculated with the so called CNDOL approximate Hamiltonians that have been developed by avoiding oversimplifications and only using a priori parameters and formulas from the simpler NDO methods. A new diagonal monoelectronic term named CNDOL/21 shows great consistency and easier SCF convergence when used together with an appropriate function for charge repulsion energies that is derived from traditional formulas. It is possible to obtain a priori molecular orbitals and electron excitation properties after the configuration interaction of single excited determinants with reliability, maintaining interpretative possibilities even being a simplified Hamiltonian. Tests with some unequivocal gas phase maxima of simple molecules (benzene, furfural, acetaldehyde, hexyl alcohol, methyl amine, 2,5 dimethyl 2,4 hexadiene, and ethyl sulfide) ratify the general quality of this approach in comparison with other methods. The calculation of large systems as porphine in gas phase and a model of the complete retinal binding pocket in rhodopsin with 622 basis functions on 280 atoms at the quantum mechanical level show reliability leading to a resulting first allowed transition in 483 nm, very similar to the known experimental value of 500 nm of "dark state." In this very important case, our model gives a central role in this excitation to a charge transfer from the neighboring Glu(-) counterion to the retinaldehyde polyene chain. Tests with gas phase maxima of some important molecules corroborate the reliability of CNDOL/2 Hamiltonians.