Molecular Connectivity Indices Research Articles

Synthesis, antimicrobial activities and QSAR studies of heterocyclic Schiff base ligands with organosilicon(IV) halides

A series of heterocyclic Schiff base ligands 4-hydroxy-3-[1-{(2-hydroxy-aryl) imino}-ethyl]-6-methyl-pyran-2-one; aryl = phenyl, 4-methylphenyl, 4-chlorophenyl or 4-nitrophenyl (1–4) and their organosilicon(IV) complexes R2Si(L) (R = Et, Bu or Ph) (5–16) were synthesized and characterized. The elemental analyses, molar conductance and spectral (IR, 1H, 13C and 29Si NMR) data suggested pentacoordinated environment around the silicon atom, where tridentate ligands coordinate through the azomethine nitrogen and oxygen of hydroxyl groups. The ligands and their organosilicon complexes were tested in vitro against pathogenic bacteria and fungi to assess their antimicrobial properties. The activity of the ligands improved against most of the strains of the tested microorganisms on complexation to the organosilicon groups. The antimicrobial activities were correlated with QSAR studies using the linear free energy relationship model which indicate the importance of topological parameters, third-order and valence first-order molecular connectivity indices (3 χ v and 1 χ v ), to explain the antibacterial and antifungal activities of all synthesized compounds.

Predicting the adsorption of organic pollutants from water onto activated carbons based on the pore size distribution and molecular connectivity index

A new model approach is developed to predict the adsorption isotherms of low-molecular-weight nonpolar organic compounds (LMWNPOCs) onto activated carbons (ACs). The model is based on the Polanyi-Dubinin (PD) equation, with the limiting pore volume of adsorbent estimated from the pore size distribution (PSD) data, and the adsorption affinity of adsorbate described by the molecular connectivity index (MCI). To obtain the MCI parameters, the model was first tested for the adsorption of 34 LMWNPOCs primarily on F400 AC from 3 reports. The models fit the experimental data well, with only 39.2% of errors. The approach was further employed to predict the adsorption capacity of 40 LMWNPOCs on F400 AC, 12 LMWNPOCs onto 9 other ACs, and 8 LMWNPOCs onto 5 ACs with unknown PSD, with the errors of 41.9%, showing the model being reasonable. The model approach may provide a simple means for predicting adsorption capacities of LMWNPOCs onto different ACs.

Synthesis, antimicrobial, anticancer and QSAR studies of 1-[4-(substituted phenyl)-2-(substituted phenyl azomethyl)-benzo[b]-[1,4]diazepin-1-yl]-2-substituted phenylaminoethanones

A series of 1-[4-(substituted phenyl)-2-(substituted phenyl azomethyl)-benzo[b]-[1,4]diazepin-1-yl]-2-substituted phenylaminoethanones (1–17) was synthesized and evaluated in vitro for its antimicrobial and anticancer activities. Results of antimicrobial screening indicated that 1-[4-[4-Chloro-phenyl)-2-[(3-nitro-phenylazo)-methyl]-benzo[b][1,4]diazepin-1-yl]-2-(2-methyl-5-nitro-phenyl amino)-ethanone (16) and 1-[4-(3,4-dimethoxy-phenyl)-2-[(4-nitro-phenylazo)-methyl]-benzo[b][1,4]diazepin-1-yl]-2-(3-nitrophenylamino)-ethanone (17) were found to be the most effective antimicrobial agents. The anticancer screening results indicated that 1-[4-(4-dimethylamino-phenyl)-2-(p-tolylazo-methyl)-benzo[b][1,4]diazepin-1-yl]-2-phenyl aminoethanone (1, IC50=1.42μM/mL against HCT 116 cancer cell lines) was the most potent anticancer agent and was more potent than 5-FU. The results of QSAR studies demonstrated the importance of topological parameters, Kier’s alpha first order shape index (κα1) and valence zero order molecular connectivity index (0χv) in describing antimicrobial activity, LUMO and κα2 in describing anticancer activity of 1-[4-(substituted phenyl)-2-(substituted phenyl azomethyl)-benzo[b]-[1,4]diazepin-1-yl]-2-substituted phenylaminoethanones.

Synthesis, biological evaluation, and QSAR studies of organosilicon(IV) complexes derived from tridentate ONO Schiff bases of dehydroacetic acid and aromatic hydrazides

A pioneer series of organosilicon(IV) complexes with general formula R2Si(L), where R = ethyl, butyl, phenyl, and L = anion of the Schiff base ligands 4-nitro/chloro-benzoic acid [1-(4-hydroxy-6-methyl-2-oxo-2H-pyran-3-yl)ethylidene]hydrazide, and benzothiazole-2-carboxylic acid [1-(4-hydroxy-6-methyl-2-oxo-2H-pyran-3-yl)ethylidene]hydrazide)] were synthesized. These complexes were characterized by elemental analyses, molar conductance, and spectroscopic techniques (IR, 1H, 13C, and 29Si NMR). A trigonal bipyramidal geometry around the silicon atom was proposed, where the Schiff base ligand acted as tridentate (ONO) coordinating through oxygen of hydroxyl group, azomethine nitrogen, and oxygen of keto group after enolization. The ligands and their organosilicon complexes were tested in vitro for antibacterial activity against Gram-positive bacteria viz. Staphylococcus aureus and Bacillus subtilis and Gram-negative bacterium viz. Escherichia coli and antifungal activity against Aspergillus niger and Candida albicans. Antimicrobial activity results indicated the biological activity of ligands improved on complexation with the organosilicon group and compound Ph2Si(L2) was found to be most potent antimicrobial agent (pMICam = 1.62). Quantitative structure activity relationship analysis was carried out to correlate antimicrobial activity of synthesized compounds with their molecular descriptors using the linear free energy relationship model which indicates that topological parameters, valence third order, and third-order molecular connectivity indices (3 χ v and 3 χ) were most significant in explaining the antibacterial and antifungal activities of all the ligands and their organosilicon(IV) complexes.

Synthesis, antimicrobial, anticancer evaluation and QSAR studies of 3/4-bromo benzohydrazide derivatives.

A series 3/4-bromo-N'-(substituted benzylidene/furan-2-ylmethylene/5-oxopentylidene/3- phenylallylidene)benzohydrazides (1-23) was synthesized and characterized by physicochemical and spectral means. The synthesized compounds were screened for their antimicrobial and anticancer potentials. Antimicrobial activity results indicated that compound 12 (pMICam = 1.67 μM/ml) was the most potent antimicrobial agent. The synthesized benzohydrazides were also having good anticancer potential and compound 22 (IC50 = 1.20 μM μM) was found to be the most potent anticancer agent which was more potent than standard drugs, tetrandrine (IC50 = 1.53) and 5- fluorouracil (IC50 = 4.6 μM). QSAR studies indicated that antimicrobial activity of synthesized compounds was best described by electronic parameter, total energy (Te) and topological parameters, valance zero order molecular connectivity index ((0)χ(v)) and Wiener index (W).

Synthesis, antimicrobial, anticancer evaluation of 2-(aryl)-4- thiazolidinone derivatives and their QSAR studies.

A new class of 4-thiazolidinones clubbed with quinozolinone nucleus has been synthesized. The title compounds were screened for their in vitro antimicrobial and anticancer potentials. Results of antimicrobial and anticancer study revealed that compounds 7 (pMICam = 1.69 μM/ml) and 2 (IC50 = 12.83 μM) were found to be the most potent antimicrobial and anticancer agents respectively. QSAR studies indicated that antimicrobial activity of synthesized 4-thiazolidinone derivatives was governed by the electronic parameters, dipole moment (μ), energy of highest occupied molecular orbital (HOMO), lipophilic parameter, log P and topological parameter, valence third order molecular connectivity index ((3)χ(v)).

Models of the equilibrium distribution of organic chemicals between water and solid phases of environmental media

Water–solid phase equilibrium distributions are foundational to multimedia environmental fate models of anthropogenic organic chemicals. This contextual review of equilibrium models of ionizing organics in aqueous–solids systems highlights the broad range of modeling assumptions and paradigms that have been employed. The complexity of soils and sediment, especially the organic phase, is provided as background along with a description of equilibrium models for nonpolar, nonionizing organics. The ways in which these single-species models have been modified and adapted for application to ionizing organics is detailed. The individual species proposed as contributing to observed distributions include the neutral parent, ions, and ion pairs. The debate over the role of the organic phase in soil and sediment solids is presented. Both absorption and surface adsorption models are described. Organic carbon (OC)-dependent models range from the simple Karickhoff equation to complex molecular connectivity indices models and polyparameter linear free energy relationship (pp-LFER) models. Adsorption models are derived from inorganic interaction chemistry. They include the early Langmuir model and Freundlich equation and continue to the modern Model VI and the NICA-Donnan model. Adsorption models focus on the mineral phase, but the role of the organic phase is not entirely dismissed. Dual mode models seek to combine absorption to OC with adsorption interactions. Conclusions drawn from studies of acid behavior do not predict the sorption of bases; bases are described separately. No single explanation and accompanying model of the distribution behavior of ionizing organics has emerged as the clear choice for regulatory use. The complexity of chemical–environment interactions is such that models are either challenging to parameterize and understand or they fail to capture key aspect(s) of the system critical to understanding of one or more classes of chemical or environmental medium. Future research directions are suggested including the possible benefit of removing sorbate–sorbent or chemical–environment distinctions.

Synthesis, antimicrobial evaluation and QSAR studies of p-coumaric acid derivatives

A series of p-coumaric acid derivatives (1–36) was synthesized and characterized by physicochemical as well as spectral means. The synthesized compounds were evaluated in vitro for their antimicrobial activity against different Gram positive and Gram negative bacterial and fungal strains by the tube dilution method. Results of antimicrobial screening indicated that compound 17 was the most active antimicrobial agent (pMICam=1.73μM/mL). The results of QSAR studies demonstrated that antibacterial, antifungal and overall antimicrobial activities of synthesized p-coumaric acid derivatives were governed by the electronic parameter, electronic energy (Ee) and topological parameters first order molecular connectivity index (1χ) and Wiener index (W).

Synthesis, antimicrobial, anticancer evaluation and QSAR studies of 2/3-bromo-N′-(substituted benzylidene/3-phenylallylidene)benzohydrazides

In the present study, a series of 2/3-bromo-N′-(substituted benzylidene/3-phenylallylidene)benzohydrazides was synthesized and evaluated in vitro for its antimicrobial and anticancer potentials. The results of antimicrobial and anticancer study indicated that compounds 3, 15 and 18 (pMICam=1.62μM/ml) were found to be most potent antimicrobial agents and compound 4 (IC50=1.88±0.03μM) was found to be the most potent anticancer agent. The results of QSAR analysis indicated the importance of topological parameters, Balaban index (J) and valence first and second order molecular connectivity indices (1χv and 2χv) in describing antimicrobial activity of the synthesized benzohydrazides.

Detour Cum Distance Matrix Based Topological Descriptors for QSAR/QSPR Part-I: Development and Evaluation

The structure of the compound depends on connectivity of its constituent atoms. Topological descriptors based on connectivity can reveal the role of structural and substructural information of molecules in estimating biological activity. The present study involves conceptualization of six detour cum distance matrix based topological descriptors (TDs) termed as relative distance sum descriptors and relative distance product descriptors (denoted by S1R , S2R , S3R , S4R , S1RP and S2RP ) as well as their topochemical versions (denoted by S1Rc, S2Rc, S3Rc S4Rc, S1RPc and S2RPc ). The proposed descriptors have been specifically designed to take care of the molecules containing cyclic substituents. An inhouse computer program was utilized to compute the values of the proposed TDs for all possible four, five and six membered hydrogen depleted structures. Proposed TDs were assessed for similarity analysis, intercorrelation, degeneracy, discriminating power, and sensitivity towards branching apart from relative position of substituent in molecules containing cyclic moieties. The said TDs exhibited negligible degeneracy, high sensitivity towards branching/relative position of substituent(s) in cyclic structures amalgamated with exceptionally high discriminating power. The above mentioned attributes provide proposed descriptors a huge potential for use in characterization of molecules, lead identification/ optimization, combinatorial library prototype, dissimilarity/similarity studies and (Q)SAR/QSPR/QSTR/QSPkR studies so as to facilitate drug design. This has been further supported by application of the proposed descriptors on the real dataset in the part-II of the manuscript. Keywords: Balaban's index, Molecular connectivity index, Relative distance product descriptors, Relative distance sum descriptors, Topological descriptors, Wiener’s index.

Neural Network Model for Predicting Anticancer Activity of Pyridopyrimidines Derivatives

Molecular structures of pyridopyrimidines derivatives as known as dihydrofolate reductase (DHFR) inhibitors were investigated by using the neural network method. Based on the molecular connectivity, molecular connectivity index and molecular electronegativity distance vectors of 32 pyridopyrimidine derivatives were obtained. Among these parameters, three optimized structural parameters 1χ3χ and M17 as the input neurons of the artificial neural network were selected by step-wise regression. Then a 3:4:1 network architecture was employed and a satisfying neural network model for predicting anticancer activity (lg1/C) was constructed with the back-propagation (BP) algorithm. The total correlation coefficient R and the standard deviation S were 0.925 and 0.336 respectively that showed significantly nonlinear relationships between lg1/C and three structural parameters. It was concluded that the predictions of BP neural network are better than those of methods in the literatures.

Stability prediction of copper(II) complexes with peptides containing cysteinic disulfide bridge by models based on the connectivity index 3χv

The article is a continuation of our work on the modeling of the stability of complex compounds. For copper(II) complexes with five protonated and deprotonated cyclic oligopeptides containing a disulfide bridge, we developed two common models. The variables used in both models are valence molecular connectivity index of the third order, 3χv, and indicator variable(s), In or In1 and In2. The model with In1 and In2 (they represent the number of terminal and non-terminal glycine residues, respectively) yielded better estimates (S.E.cv = 0.51) than the model with In (= In1 + In2); S.E.cv = 0.60.

Prediction of Partition Coefficients of Organic Compounds between SPME/PDMS and Aqueous Solution

Polydimethylsiloxane (PDMS) is commonly used as the coated polymer in the solid phase microextraction (SPME) technique. In this study, the partition coefficients of organic compounds between SPME/PDMS and the aqueous solution were compiled from the literature sources. The correlation analysis for partition coefficients was conducted to interpret the effect of their physicochemical properties and descriptors on the partitioning process. The PDMS-water partition coefficients were significantly correlated to the polarizability of organic compounds (r = 0.977, p < 0.05). An empirical model, consisting of the polarizability, the molecular connectivity index, and an indicator variable, was developed to appropriately predict the partition coefficients of 61 organic compounds for the training set. The predictive ability of the empirical model was demonstrated by using it on a test set of 26 chemicals not included in the training set. The empirical model, applying the straightforward calculated molecular descriptors, for estimating the PDMS-water partition coefficient will contribute to the practical applications of the SPME technique.

Predictive modeling of chemical toxicity towards Pseudokirchneriella subcapitata using regression and classification based approaches

Biodiversity nurturing may be a valuable pathway in controlling chemical stress on the ecosystem. In the present work, in silico studies have been performed to develop regression based quantitative structure toxicity relationship (QSTR) models using a data set containing 105 organic chemicals for the prediction of 48-h chemical toxicity towards Pseudokirchneriella subcapitata. Classification based linear discriminant analysis (LDA) was also performed to distinguish chemicals into toxic and nontoxic groups using the same data set. The developed models were found to possess good predictive quality in terms of internal, external and overall validation parameters. The regression based QSTR model suggests that second order molecular connectivity index (molecular size and lipophilicity), density (aromaticity), relative shape of molecules (cyclicity/aromaticity), and specific molecular fragments of the chemicals are important properties of chemicals to exert their toxicity on P. subcapitata. The classification based LDA QSTR model suggested that fused ring aromatic systems, secondary carbon atom fragments, second order valence molecular connectivity indices (molecular size and branching) and molecular weight are the distinguishing features to differentiate chemicals into toxic and nontoxic groups.

The Anti-Influenza Activity of Cyclohexene Derivatives Studied by Topological Indices and Neural Network

In order to establish quantitative structure-activity relationship (QSAR) model of cyclohexene derivatives as neuraminidase inhibitors, the relationships between the anti-influenza activity of 34 cyclohexene derivatives and their molecular connectivity indices (mX) as well as their electrotopological state indices (Im) were analyzed, meanwhile molecular structures of the substances were also effectively characterized. On the one hand, two molecular connectivity 0X, 3X and four electrotopological state indices I1, I3, I6, I16, which were obtained by optimization of multiple linear stepwise regression, were employed to found QSAR model through stepwise regression analysis, where the correlation coefficient of equation was 0.906 and the average absolute error between experimental values and estimates was 0.37. On the other hand, Using these structural parameters above as the input neurons, a satisfying neural network model with the 6-2-1 network architecture was also constructed by the back-propagation (BP) algorithm, where the total correlation coefficient reached up to 0.966 and the average absolute error between experimental values and estimates of the model decreased to 0.19. Therefore it was concluded that the results of BP network were better than those of multiple linear regression methods.

Topological models of retention index of thin-layer chromatogram for chiral organic acids

On the basis of Kier's molecular connectivity indices and conjugated matrix, novel molecular connectivity indices ((m) G(t)(v)) were defined and calculated for 18 chiral hydroxyl acids and amino acids. The chiral connectivity indices ((m)C(t)(v)) were introduced by extending (m)G(t)(v): (m)C(t)(v) = (m)G(t)(v) x w(j), where w(j) is the chiral index. The quantitative structure-retention index relationship (QSRR) between the retention index (R(M)) of thin-layer chromatogram for the chiral organic acids and (m)C(t)(v) was studied by multivariate statistical regression. By leaps-and-bounds regression analysis, the best four-parameter QSRR model was set up, and the traditional correlation coefficient (R2) and the cross-validation correlation coefficient (Q2) of leave-one-out (LOO) were 0.973 and 0.950, respectively. The results demonstrated that the model was highly reliable and had good predictive ability from the point of view of statistics. From the four parameters (0C(p)(v), 2C(p)(v), C(ch),(v), 5C(p)(v)) of the model, it is known that the dominant influence factors of the retention index were the molecular structure characteristics of two-dimensional and the space factors: the chiral characteristics, the flexibility and the puckered degree of molecules for the chiral organic acids. The results showed that the new parameter mC(t)(v) had good rationality and efficiency for the retention indices of the chiral organic acids. Therefore, an effective method was provided to predict the retention indices of the chiral organic acids.

QSPR with descriptors based on averages of vertex invariants. An artificial neural network study

Mean molecular connectivity indices (MMCI) defined as averages of vertex invariants together with molecular connectivity indices (MCI) and experimental parameters build optimal semiempirical relationships for eight out of eleven properties of organic solvents studied with artificial neural networks.

QSAR for Antimycobacterial Activity of β-Thia Adduct of Chalcone and Diazachalcone Derivatives

Quantitative structure-activity antimycobacterial relationships have been studied for a series of β-thia adduct of chalcone and diazachalcone derivatives by means of multiple linear regression (MLR) and artificial neural networks (ANN). The antimycobacterial activity against M. tuberculosis H37Rv of the compounds studied was well correlated with descriptors encoding the chemical structure. Using the pertinent descriptors revealed by a stepwise procedure in the multiple linear regression technique, a correlation coefficient of 0.9798 (s=0.0869) for the training set was obtained for the ANN model in a [3-3-1] configuration. The results show that the antimycobacterial activity of these compounds is strongly dependent on hydrogen-bonding donors, molecular refraction and also molecular connectivity indices for 2nd order.

Comparison of Two Methods for the Estimation of Stability of Copper(II) Bis-Complexes with Aromatic Ligands Relevant to Alzheimer’s Disease

In order to compare the density functional theory (DFT) and the method with the molecular valence connectivity index of the 3rd order (3χv) in the estimation of stability constants log β2, we used copper(II) complexes with thioflavin-based intercalation compounds designed for application in Alzheimer's disease. Correlation of ten log β2 values calculated by DFT with 3χv yielded r=0.988 and S.E.=0.85. The correlations of indices I(NG), HOMA and 3χv with log β2, and the intercorrelations between the indices revealed the crucial influence of 3χv on the prediction of the stability of these complexes.

Synthesis, Antimicrobial Evaluation and QSAR Studies of 2-chlorobenzoic Acid Derivatives

In the present study, 2-chlorobenzoic acid derivatives were synthesized and evaluated for their antimicrobial activity against Gram-positive bacteria: Staphylococcus aureus, Bascillus subtilis, Gram-negative bacterium Escherichia coli, fungal strains: Candida albicans and Aspergillus niger by tube dilution method. Results of antimicrobial screening indicated that the synthesized compounds exhibited greater antibacterial potential against Gram-negative bacterium (Escherichia coli) than Gram-positive bacteria and the Schiff's bases of 2-chloro benzoic acid were more potent antimicrobial agents than its esters. Compound 6 (pMIC(am)=1.91 µM/ml, pMIC(ec)=2.27 µM/ml) emerged as most potent antimicrobial agent and was found comparable to standard drug norfloxacin (pMIC(ec)=2.61 µM/ml) against Escherichia coli. QSAR studies revealed that the antibacterial, antifungal and the overall antimicrobial activities of the 2-chlorobenzoic acid derivatives were governed by the topological parameters, second order and valence second order molecular connectivity indices ((2)χ and (2)χ(v)).