Tuning the resonance-assisted hydrogen bond (RAHB) of malonaldehyde using π-conjugated substituents and presentation of its energy decomposition.

Position of substituents (R1 = H and R2, R3 = H, F, CnHn+1) on malonaldehyde has been changed to amplify its intramolecular hydrogen bonding (IHB) interaction. Results show that π-conjugated substituents at R3 position cause increase of IHB energies of the substituted malonaldehydes. Then, amplification of the IHB interaction in malonaldehyde with C20H21 group at R3 position was verified. Thus, polyethylene chains influence on resonance-assisted hydrogen bond (RAHB) of malonaldehyde and can form malonaldehyde-polyene materials with improved properties. Then, hydrogen bond (HB) interaction of formaldehyde with the substituted malonaldehydes without RAHB ring was studied and HB interaction energies calculated. The HB interaction energies have linear relation with intermolecular hydrogen bond length (dHB). A procedure was employed to estimate IHB energies of the substituted malonaldehydes using the mentioned relation and intramolecular hydrogen bond length (dIHB). Relation between geometrical parameters, spectroscopic data, topological properties, and molecular orbital descriptors with the estimated IHB energies were surveyed. Moreover, IHB energies of the current system were estimated using some quantum chemistry methods to validate the data and find more consistent method with the proposed viewpoint. Results indicated that the estimated IHB energies using proposed procedure have good correlations with other methods. Also, energy decomposition of the IHB interactions in the substituted malonaldehydes was performed using a new method and results showed that electrostatic term has major contribution in the IHB energies.

Assessment of O-H⋯O and O-H⋯S intramolecular hydrogen bond energies in some substituted pyrimidines using quantum chemistry methods.

Resonance-assisted hydrogen bond (RAHB) theory was studied in some substituted pyrimidines in which encompass O-H⋯Y unit (Y= O and S). Alteration of substituents (R 1, R2, R3 = H, C2H, C2F) on pyrimidine ring changes properties of electron charge density at this ring and influences indirectly on strength of intramolecular hydrogen bond (IHB) interactions in the mentioned structures. Then, IHB energies were estimated using cis-trans method (CTM), related rotamers method (RRM), Espinosa' method (EM), and a viewpoint based on properties of electron charge densities at ring critical point (RCP) of RAHB ring. Moreover, the estimated IHB energies with these methods were compared with those obtained using modified Espinosa' method (MEM), Iogansen's relationship, and chemical shift-based method to find more consistent method with the proposed viewpoint based on RCP characteristics. The linear correlations between the all estimated IHB energies and some hydrogen bonding descriptors such as geometrical, spectroscopic, topological, and molecular orbital factors were examined. Results indicated that the IHB energies that obtained by way of MEM and Iogansen's relationship have better correlations with hydrogen bonding descriptors. Also, there are good consistencies between results of these two methods with viewpoint based on properties of RCPs. Therefore, IHB energies can suitably estimate using properties of RCPs in heterocyclic molecular systems especially in cases that rotation around C-C/CC bonds makes additional interactions in isomers and influences on accuracy of calculated IHB energies using approaches such as CTM and RRM.

Predicting stability constants of transition metals; Y3+, La3+, and UO2 2+ with organic ligands using the 3D-QSPR methodology.

Stability constants prediction plays a critical role in the identification and optimization of ligand design for selective complexation of metal ions in solution. Thus, it is important to assess the potential of metal-binding ligand organic in the complex formation process. However, quantitative structure-activity/property relationships (QSAR/QSPR) provide a time-and cost-efficient approach to predict the stability constants of compounds. To this end, we applied a free alignment three-dimensional QSPR technique by generating GRid-INdependent Descriptors (GRINDs) to rationalize the underlying factors effecting on stability constants of transition metals; 105 (Y3+), 186 (La3+), and 66 (UO2 2+) with diverse organic ligands in aqueous solutions at 298 K and an ionic strength of 0.1 M. Kennard- Stone algorithm was employed to split data set to a training set of 75% molecules and a test set of 25% molecules. Fractional factorial design (FFD) and genetic algorithm (GA) applied to derive the most relevant and optimal 3 D molecular descriptors. The selected descriptors using various feature selection were correlated with stability constants by partial least squares (PLS). GA-PLS models were statistically validated ( R 2 = 0.96, q2 = 0.82 and R2 pred=0.81 for Y3+; R 2 = 0.90, q2 = 0.73 and R2 pred=0.82 for La3+ and R 2 = 0.95, q2 = 0.81 and R2 pred=0.88 for UO2 2+), and from the information derived from the graphical results confirmed that hydrogen bonding properties, shape, size, and steric effects are the main parameters influencing stability constant of metal complexation. The provided information in this research can predict the stability constant of the new organic ligand with the transition metals without experimental processes.

Expedient multicomponent synthesis of a small library of some novel highly substituted pyrido[2,3-d]pyrimidine derivatives mediated and promoted by deep eutectic solvent and in vitro and quantum mechanical study of their antibacterial and antifungal activities.

A facile and efficient catalyst- and oxidant-free multicomponent synthesis of a small library of highly substituted pyrido[2,3-d]pyrimidine derivatives is reported. The products were obtained within relatively short reaction times in good to excellent yields in the presence of deep eutectic solvents as media and promoters. Simple purification and reusability of the deep eutectic solvent were the other beneficial factors of the reported protocol. All of the synthesized derivatives were thoroughly screened for possible in vitro antibacterial and antifungal effects against twenty-two bacterial and three fungal pathogens. Some of the prepared pyrido[2,3-d]pyrimidine derivatives showed remarkable antibacterial and antifungal activities in comparison with some typical known antibacterial and antifungal agents. Finally, the derivatives possessing bioactivity effects were subjected to quantum chemical computational studies in order to reveal the probable structural and electronic effects governing the spotted bioactivities. It was found that the observed bioactivities could be best devoted to the HOMO-LUMO energy gap and para delocalization index of the corresponding derivatives.

Multicomponent Solvent-Free Synthesis, Antibacterial Evaluation and QSAR Study of 2-(Bis(benzylthio)methylene) malononitriles.

Multicomponent reaction of malononitrile, carbon disulphide and various benzyl halides was developed as an efficient strategy for the synthesis of 2-(bis(benzylthio)methylene)malononitrile derivatives via two different procedures: (a) in the presence of K2CO3 as a base in acetonitrile and (b) under solvent-free conditions in the presence of triethylamine. Higher yields with shorter reaction times were obtained from the latter procedure. Inhibitory activity of all derivatives was evaluated against 22 pathogenic bacteria including both Gram-negative and Gram-positive strains. Thioether 4b showed broad-spectrum antibacterial activities according to the antibiogram tests. DFT calculations (B3LYP/6-311++G**) were performed to determine the type of drug-receptor interactions. It was found that reversible dipole-dipole forces play a key role in most interactions.

Toward an Optimal Approach for Variable Selection in Counter-Propagation Neural Networks: Modeling Protein-Tyrosine Kinase Inhibitory of Flavanoids Using Substituent Electronic Descriptors.

Counter propagation neural network (CPNN) is one of the attractive tools of classification in QSAR studies. A major obstacle in classification by CPNN is finding the best subset of variables. In this study, the performance of some different feature selection algorithms including F score-based ranking, eigenvalue ranking of PCs obtained from data set, Non-Error-Rate (NER) ranking of both descriptors and PCs, and 3-way handling of data, Parallel Factor Analysis (PARAFAC), was evaluated in order to find the best classification model. The methods were applied for modeling protein-tyrosine kinase inhibitory of some flavonoid derivatives using substituent electronic descriptors (SED) as novel source of electronic descriptors. The results showed that the best performance was achieved by F-score ranking while the NER ranking of principal components (PCs) showed very fluctuate results and the worst performance was belonging to PARAFAC-CPNN. Furthermore, comparison of results of these nonlinear algorithms with linear discriminate analysis method revealed better predictions by the former.