Molecular Connectivity Indices Research Articles

Descriptors-based machine-learning prediction of cetane number using quantitative structure–property relationship

The physicochemical properties of liquid alternative fuels are important but difficult to measure/predict, especially when complex surrogate fuels are concerned. In the present work, machine learning is used to develop quantitative structure–property relationship models. The fuel chemical structure is represented by molecular descriptors, allowing the linking of important features of the fuel composition and key properties of fuel utilization. Feature selection is employed to select the most relevant features that describe the chemical structure of the fuel and several machine learning algorithms are tested to construct interpretable models. The effectiveness of the methodology is demonstrated through the development of accurate and interpretable predictive models for cetane numbers, with a focus on understanding the link between molecular structure and fuel properties. In this context, matrix-based descriptors and descriptors related to the number of atoms in the molecule are directly linked with the cetane number of hydrocarbons. Furthermore, the results showed that molecular connectivity indices play a role in the cetane number for aromatic molecules. Also, the methodology is extended to predict the cetane number of ester and ether molecules, leveraging the design of alternative fuels towards fully sustainable fuel utilization.

Designing the structure of cationic star-shaped trimeric surfactants most active in micelle formation using molecular connectivity indices

A model expressing the relationship between the logarithm of critical micelle concentration of cationic star-shaped trimeric surfactants and topological indices was obtained using only molecular connectivity indices. Based on the obtained model, the exemplary compound most active in micelle formation was designed. The analysis of the influence of various structural factors on the value of the critical micelle concentration was supported by atomic charge studies. The obtained model will be used to design new star-shaped trimeric surfactants that are more active in formation of micelle as well as to predict their critical micelle concentration.

Solubility in supercritical carbon dioxide of two novel amine derivatives of 2,3-dichloronaphthalene-1,4-dione (dichlone)

Dichlone (2,3-dichloronaphthalene-1,4-dione) is an important antimicrobial agent for agriculture, which effectiveness could be improved by modifying its structure, while the recovery of high-purity synthesized derivatives from a reaction mixture could be accomplished by extracting them with supercritical carbon dioxide. Two new amine derivatives, 2-chloro-3-((4-chlorobenzyl)amino)naphthalene-1,4-dione (dCl-2B-Cl) and 2-chloro-3-((4-chlorophenethyl)amino)naphthalene-1,4-dione (dCl‐3P‐Cl), were synthesized from dichlone, and their solubility in supercritical carbon dioxide was measured afterwards at (313, 323 and 333) K and a pressure range from (8−33) MPa. Experimental solubilities spanned from (10.3·10-6 to 22.1·10-6) mol·mol-1 for dCl-2B-Cl, and from (32.7·10-6 to 131·10-6) mol·mol-1 for dCl‐3P‐Cl. The solubility data of the dichlone family (dichlone, dCl-2B-Cl, dCl‐3P‐Cl, 2‐(benzylamino)‐3‐chloronaphthalene‐1,4‐dione (dCl-2B) and 2-chloro-3-(phenethylamino)naphthalene-1,4-dione (dCl‐3P)) was compared using three models, i.e., the Chrastil equation, the Molecular Connectivity Indices model, and the Statistical Associating Fluid Theory of Variable Range and Mie Potential equation of state, to identify the quantitative structure-property relationship between them. Solubility had an inverse relation with solute size and polarity, but there were some exceptions that could be explained by performing a stereochemical analysis, which showed that steric effects involved in the folding of dCl‐3P and dCl‐3P‐Cl provided them a better geometry for solvation than dCl-2B and dCl-2B-Cl, respectively, making them more soluble. This demonstrates that the solute geometry is an important factor in the solvation process, and it must be represented accurately to develop better predictive models.

On the spacer group effect on anti-Escherichia Coli activity of cationic gemini surfactants using molecular connectivity indices and atomic charges

The molecular connectivity indices were used to investigate the effect of the spacer group structure on the value of minimum inhibitory concentration of bis-quaternary ammonium bromide salts. QSAR model obtained allows to examine the influence of branching, multiple bonds, and heteroatoms of the spacer group on minimum inhibitory concentration value. The effect of spacer’s branches and heteroatoms on anti – Escherichia coli activity of cationic gemini surfactants was also investigated using the atomic charges. This study leads to some significant conclusions regarding the effect of the spacer group structure on antibacterial activity of cationic gemini surfactants against Escherichia coli. The general conclusion is that a small modification of the straight spacer chain structure can give gemini surfactant with of excellent activity against such resistant bacterium as Escherichia coli.

Evaluation of the Key Structural Features of Various Butyrylcholinesterase Inhibitors Using Simple Molecular Descriptors.

In this study, we developed several QSAR models based on simple descriptors (such as topological and constitutional) to estimate butyrylcholinesterase (BChE) inhibition potency, pKi (or pIC50), of a set of 297 (289 after exclusion of outliers) structurally different compounds. The models were similar to the best model that we obtained previously for acetylcholinesterase AChE and were based on the valence molecular connectivity indices of second and third order (2χv and 3χv), the number of aliphatic hydroxyl groups (nOH), AlogP Ghose–Crippen octanol–water partition coeff. (logP), and O-060–atom-centred fragments (Al-O-Ar, Ar-O-Ar, R..O..R and R-O-C=X). The best models with two and three descriptors yielded r = 0.787 and S.E. = 0.89, and r = 0.827 and S.E. = 0.81, respectively. We also correlated nine scoring functions, calculated for 20 ligands whose complexes with BChE we found in the Protein Data Bank as crystal structures to pKi (or pIC50). The best correlations yielded PLP1 and PLP2 (Piecewise Linear Pairwise potential functions) with r = 0.619 and 0.689, respectively. Correlation with certain simple topological and constitutional descriptors yielded better results, e.g., 3χv (r = 0.730), on the same set of compounds (N = 20).

Use of connectivity index and simple topological parameters for estimating the inhibition potency of acetylcholinesterase

Acetylcholinesterase (AChE) has proven to be an effective drug target in the treatment of neurodegenerative diseases such as Alzheimer’s, Parkinson’s and dementia. We developed a novel QSAR regression model for estimating potency to inhibit AChE, pKi, on a set of 75 structurally different compounds including oximes, N-hydroxyiminoacetamides, 4-aminoquinolines and flavonoids. Although the model included only three simple descriptors, the valence molecular connectivity index of the zero-order, 0χv, the number of 10-membered rings (nR10) and the number of hydroxyl groups (nOH), it yielded excellent statistics (r = 0.937, S.E. = 0.51). The stability of the model was evaluated when an initial set of 75 compounds was broadened to 165 compounds in total, with the increase of the range of pKi (exp) from 6.0 to 10.2, yielding r = 0.882 and S.E. = 0.89. The predictive power of the model was evaluated by calculating pKi values for 55 randomly chosen compounds (S.E.test = 0.90) from the calibration model created on other 110 compounds (S.E. = 0.89), all taken from the pool of 165 compounds.

Study on quantitative structure-biodegradability relationships of amine collectors by GFA-ANN method

In this study, the biodegradability of 17 amine collectors, categorized by fatty amine, quaternary ammonium compounds and oxygen-containing amine collectors, are tested with the Closed Bottle Test for 90 days, and the results indicate most amine collectors are not readily biodegradable. The oxygen-containing amine collectors have the best biodegradability due to the introduced oxygen-containing functional groups, subsequently fatty amine collectors with branched chains, while the tested quaternary ammonium compounds all have poor biodegradation ability. Besides, we search for and calculate 35 molecular descriptors to develop the quantitative structure biodegradability relationship (QSBR) of amine collectors. With the Genetic Function Approximation (GFA) algorithm, two sets of important molecular descriptors related to biodegradability (q) of amine collectors are selected from 35 molecular descriptors. Based on internal and external validations, the robust and reliable non-linear QSBR model with the squared correlation coefficient above 0.99 is determined via Artificial Neural Network (ANN) method, where the descriptors are respectively CL, N, ELUMO, δv2, indicating the biodegradable ability of amine collectors is correlated with the alkyl chain lengths (CL), the number of nitrogen atom-containing compounds (N), energy of the lowest unoccupied molecular orbital (ELUMO) and valence second-order connectivity index (δv2).

Assessment of pesticides volatilization potential based on their molecular properties using the TyPol tool

Following treatment, amounts of pesticides can reach the atmosphere because of spray drift, volatilization from soil or plants, and/or wind erosion. Monitoring and risk assessment of air contamination by pesticides is a recent issue and more insights on pesticide transfer to atmosphere are needed. Thus, the objective of this work was to better understand and assess pesticides emission potential to air through volatilization. The TyPol tool was used to explore the relationships between the global, soil and plant volatilization potentials of 178 pesticides, and their molecular properties. The outputs of TyPol were then compared to atmospheric pesticide concentrations monitored in various French regions. TyPol was able to discriminate pesticides that were observed in air from those that were not. Clustering considering parameters driving the emission potential from soil (sorption characteristics) or plant (lipophilic properties), in addition to vapor pressure, allowed better discrimination of the pesticides than clustering considering all parameters for the global emission potential. Pesticides with high volatilization potential have high total energy, and low molecular weight, molecular connectivity indices and polarizability. TyPol helped better understand the volatilization potential of pesticides. It can be used as a first step to assess the risk of air contamination by pesticides.

RETRACTED: Development of a simple QSAR model for reliable evaluation of acetylcholinesterase inhibitor potency

With the aging of the western population, more and more people are affected by the neurodegenerative Alzheimer's and Parkinson's disease. Inhibitors of acetylcholinesterase (AChE) have proven to be effective in the treatment of disease symptoms. We report the QSAR regression model for the estimation of potency of a set of 94 structurally diverse compounds (oximes, N-hydroxyiminoacetamides, 4-aminoquinolines and flavonoids) to inhibit AChE, pKi (AChE). The model is based on three simple descriptors: the valence molecular connectivity index of the zero-order, 0χv, combined with the number of 10-membered rings (nR10) and number of hydroxyl groups in a molecule (nOH). QSAR model yielded r = 0.947, S.E. = 0.51 and S.E.cv= 0.53; the range of pKi (exp) = 6.03. It showed its stability when the set of 94 compounds was enlarged, comprising 184 compounds in total (r = 0.886, S.E. = 0.85 and S.E.cv = 0.88; the range of pKi (exp) = 10.21), resulting in regression parameters which were similar, although only for 0χv coefficients within the limits of S.E. (0.167(13) and 0.172(16) for the set with 94 and 184 compounds, respectively. The predictive power of the model was shown by the prediction of pKi values for 61 randomly chosen compounds (S.E.test = 0.86) from the calibration model made on the other 123 compounds (S.E. = 0.85), all taken from the pool of 184 compounds. QSAR descriptors 0χv, nR10 and nOH were well chosen for describing the interactions of the AChE active site (amino acid interaction) with ligands through the estimation of the inhibitory potency.

Fluorescence Study of the Influence of the Structure and Hydrophobicity of Fluorescent Dyes and Cationic Surfactants on their Association in Aqueous Solutions

The statistical characteristics of the dependences of the fluorescence signal of analytical systems as a function of the integral parameters of the structure of fluorescent reagents and cationic surfactants on their association in aqueous solutions has been investigated. Molecular weight, surface area, and their first-order molecular connectivity index have been taken as parameters of the structure of the reagents and cationic surfactants. The influence of the hydrophobicity of the reagent and cationic surfactants, such as the octanol–water distribution constant and octanol–water partition coefficient, on the fluorescence signal of the reagent–cationic surfactant associates have also been investigated. It is shown that the associates of anionic reagents with cationic surfactant counter ions are characterised by high stability and a higher analytical signal compared with associates in which there is no electrostatic attraction between the reagent and the surfactant ion. The effect of hydrophobicity of the reagent and cationic surfactant in the absence of electrostatic attraction between the interacting particles is similar. The increase in the role of the influence of the structure of cationic reagents in their association with cationic surfactants, when the electrostatic attraction is absent and the stability of the associates is due mainly to hydrophobic interactions, is noticeable. The regularities of the influence of the colloid-chemical state on the analytical signal of associated cationic surfactants in solutions have been investigated. The study made it possible to formulate a rational basis for the search and design of analytical systems for the determination of large cations by the fluorescence method.

Evaluation of the Estrogenic/Antiestrogenic Activities of Perfluoroalkyl Substances and Their Interactions with the Human Estrogen Receptor by Combining In Vitro Assays and In Silico Modeling.

The potential estrogenic activities of perfluoroalkyl substances (PFASs) are controversial. Here, we investigated the estrogenic/antiestrogenic activities of PFASs and explored the corresponding interaction mode of PFASs with the estrogen receptor (ER) by combining in vitro assays and in silico modeling. We found that three PFASs (perfluorobutanoic acid, perfluorobutane sulfonate, and perfluoropentanoic acid) exerted antiestrogenic effects by inhibiting luciferase activity, whereas perfluorohexane sulfonate (PFHxS) and perfluorooctane sulfonate (PFOS) exerted estrogenic effects by inducing luciferase activity. When coexposed to 17β-estradiol (E2), all tested PFASs attenuated the E2-stimulated luciferase activity; unexpectedly, each PFAS could further attenuate the luciferase activity generated by the cotreatment with ICI 182,780 and E2, with a minimal effective concentration comparable to that found in human serum. PFHxS and PFOS significantly induced the gene expression of TFF1; additionally, all PFASs inhibited the E2-induced gene expression of TFF1 and EGR3. Furthermore, the results of the blind docking analyses suggested that the interaction with the coactivator-binding region on the ER surface should be included as a pathway through which PFASs exert estrogenic and antiestrogenic activities. Finally, we revealed the critical molecular property of the zero-order molecular connectivity index (MCI) (0χ) that affects the antiestrogenic activity of PFASs.

Theoretical chemistry study on the toxicity of some polychlorobiphenyl (PCB) compounds using molecular descriptors

This study was carried out to establish, validate and apply a model that relates the toxicity of some PCBs (2,4-dichlorobiphenyl, 2,4’5-trichlorobiphenyl, 2,2’3,4,4’,5’,6-heptachlorobiphenyl, 2,2’,5,5’-tetrachlorobiphenyl and 2,3,3’4,4’-pentachlorobiphenyl) to molecular descriptors (quantum chemical, physicochemical and topological descriptors) through quantitative structure toxicity relationship. The established model reveal that the total molecular energy, electronic energy, core repulsion energy, energy of the highest occupied molecular orbital, energy of the lowest unoccupied molecular orbital, cosmo area, cosmo volume and dipole moment displayed excellent correlation with LD50 values of the PCBs. Some topological parameters (namely molecular topological index, Weiner index, total connectivity and Balaban index) also exhibited excellent correlation with experimentally determined LD50 data. Two equations suitable for estimating theoretical LD50 values of the studied PCBs were derived using quantum chemical and topological descriptors. These equations gave perfect fitness when experiment and theoretical LD50 values were plotted (i.e. R2 value approximated unity) against each other. Perfect correlation was also obtained between theoretical and experimentally derived LD50 values for 2,2’,3,4’,5’,6-hexaxhlorobiphenyl, 2,2’,4,5,5’-pentachlorobiphenyl, 2,3’,4,4’-tetrachlorobiphenyl and 2,2’,4,4’,5,5’-hexachlorobiphenyl, where were PCBs used for model validation. The model was also used to predict the toxicity of other PCBs that lacks accurate experimental data. The study revealed that the reactivity of PCBs can be influence by the number of substituted chlorine atom and by the position of the reactive aromatic carbon. From the results and findings of the study, it was concluded that the applications of quantitative structure-toxicity relation principles to quantum and topological parameters provide excellent avenue for estimating the degree of toxicity of PCBs.

Synthesis, Biological Evaluation, QSAR, Molecular Docking and ADMET Studies of N-aryl/N,N-dimethyl Substituted Sulphonamide Derivatives

<P>Background: Rapid evolution of drug resistance and side effects of currently used drugs develop more efficacious and newer antimicrobial agents. Further, for the management of Type II Diabetes, α-gulcosidase and α-amylase inhibitors play a very important role by inhibiting the postprandial hyperglycemia. </P><P> Objectives: The objective of this study was to synthesize N-aryl/N,N-dimethyl sulphonamides, investigate their antihyperglycemic and antimicrobial potential, develop QSAR model for identifying molecular descriptors and predict their binding modes and in silico ADMET properties. </P><P> Methods: Synthesized derivatives were subjected to in vitro studies for their antidiabetic activity against α-glucosidase and α-amylase enzymes and antimicrobial activity. Molecular docking studies were carried out to find out molecular binding interactions of the ligand molecules with their respective targets. QSAR studies were carried out to identify structural determinants responsible for antimicrobial activity. </P><P> Results: Antidiabetic study demonstrated the potent activity of two compounds 2 and 6 as α- glucosidase and α-amylase inhibitors, as well as compound 1 and 2, exhibited potent antimicrobial activity against all the tested microbes. All the compounds have more antifungal potential against Candida albicans. QSAR studies confirmed the role of molecular connectivity indices (valence first order and second order) in controlling the antimicrobial activity. Molecular docking studies supported the observed in vitro biological activities of the synthesized compounds. </P><P> Conclusion: The compounds with 2,3-dimethyl substitution were found to be antidiabetic agents and molecules having bromo and 2,3-dimethyl substituents on phenyl ring have established themselves as potent antimicrobial agents. The role of valence first and 2nd order molecular connectivity indices as molecular properties were identified for antimicrobial activity and various electrostatic, hydrogen bonding and hydrophobic interactions were found to be prominent in the binding of molecules at the target site.</P>

DEPENDENCE OF DYNAMIC VISCOSITY OF NAPHTHENIC HYDROCARBONS IN CYCLOPENTANE SERIES ON QUANTUM AND STRUCTURAL PARAMETERS

Interrelation of parameters for Newtonian fluid viscous flow of monocyclic hydrocarbons with quantum and structural (topological) characteristics of molecules are considered. Ionization potentials and topological indices, respectively, were considered as quantum and topological characteristics. The vertical ionization potentials were calculated by the Koopmans ' theorem of quantum chemical methods with full molecular geometry optimization. We have studied topological indices that take into account the size and shape of the molecular graph. As a topological descriptors the Wiener index, the Balaban centric index, the Randic index (the index of molecular connectivity), Gutman (Szeged) index, Platt index and the Harary index were considered. For hydrocarbons in cyclopentane series with side chains, the kinetic compensation effect of dynamic viscosity has been established, which connects the activation energy and Arrhenius factor in the framework of the Frenkel-Eyring model. It is established that for compounds of a number of five-membered naphthenes, the apparent activation energy for viscous flow and the associated pre-exponential factor, depends on quantum parameters (ionization potentials) and the topology of the molecules. In this paper, a regression quantitative structure−property relationship (QSPR) is proposed using as descriptors the ionization potentials and topological indices for the prediction of dynamic viscosity. Prognostics capabilities of the proposed model and the adequacy of the forecast were verified by calculating the values of the dynamic viscosity of hydrocarbons that are not included in the base series. Experimental and theoretical substantiation of the proposed regularity was given within the framework of the representation of the viscous flow activation energy as a measure of intermolecular interaction and the predominance of dispersion interaction in hydrocarbon molecules for cyclopentane series. The equation obtained during the study can be used to predict the viscosity characteristics of synthesized and natural five-membered naphthenes.

On the hydrophobic chains effect on critical micelle concentration of cationic gemini surfactants using molecular connectivity indices

The influence of the structure of hydrophobic tail chains on the critical micelle concentration of cationic gemini surfactants, using only the molecular connectivity indices, has been investigated in this work. The best model obtained shows that the relationship between the logarithm of critical micelle concentration and the alkyl chains length is parabolic. The formula has been derived for compounds with the same head groups and the same, medium length, spacer but with various hydrocarbon tail chains. Good-quality QSPR model obtained can be used to predict the critical micelle concentration value of structurally similar gemini surfactants as well as to design the structure of the hydrophobic tail chains to obtain new molecules more active in micelle formation.Graphic abstract

Prediction of adsorption capacity for pharmaceuticals, personal care products and endocrine disrupting chemicals onto various adsorbent materials

Adsorption is a common process used to remove pharmaceuticals, personal care products and endocrine disrupting chemicals (PPCPs/EDCs) from water. However, as PPCPs/EDCs cover a wide range of molecules and chemical structures, prediction of the adsorption capacity is very challenging. In this study, a novel model was developed to predict adsorption isotherms of PPCPs/EDCs onto various types of adsorbents using a combination of Polanyi potential theory, molecular connectivity indices (MCIs) and molecular characteristics. Polanyi theory provided the basic mathematical form for the correlation. MCIs, hydrophobicity and H-bond count were used to normalize the Polanyi equation based on the molecular structure and interactions among the chemicals, the adsorbents, and the solution. In total, adsorption data were collected from 158 reports for 55 PPCPs/EDCs onto 306 different adsorbent materials. The correlation was first trained with 46 PPCPs/EDCs adsorbed onto 162 carbonaceous adsorbents (CAs), with 44.8% SDEV. Then the model was employed to predict 46 PPCPs/EDCs onto 118 other CAs and 9 new PPCPs/EDCs onto 23 CAs in ultrapure water, with error from 42 to 48% SDEV. When applying to non-carbonaceous adsorbents, the models can still predict the adsorption of PPCPs/EDCs with 90.09% SDEV. For the first time, a model, the PD – MCI – hydrophobic – H bond model, was developed to predict adsorption of a wide group of complicated PPCPs/EDCs onto a big variety of carbonaceous and non-carbonaceous adsorbents. The proposed model approach may provide a simple means for predicting adsorption capacities of PPCPs/EDCs onto various adsorbents.

On the Head Group Effect on Critical Micelle Concentration of Cationic Surfactants Using Molecular Connectivity Indices and Atomic Partial Charges

The influence of the head group structure on the critical micelle concentration of cationic surfactants, mainly quaternary ammonium bromide salts with one alkyl tail, has been studied theoretically by QSPR using molecular connectivity indices and also the atomic partial charges. Two models obtained allow to study the effect of head group structure modification on the value of the critical micelle concentration. The model, which contains molecular connectivity indices together with the charge localized on the central nitrogen atom, has a wider descriptive capability than the model which contains topological indices only. The present semi-empirical charge investigations of different quaternary ammonium salts show that the charges of the quaternary ammonium head groups are approximately the same, but one of the obtained models shows that the significant part of that charge, which is localized on the central nitrogen atom, affects the value of the critical micelle concentration.

A general-applicable model for estimating the binding coefficient of organic pollutants with dissolved organic matter

The binding constant (Kdoc) of organic pollutants (OPs) with dissolved organic matter (DOM) is an important parameter in determining the partitioning of OPs in the aquatic environment. Most estimation models have focused on calculating the Kdoc of a specific group of OPs but failed to obtain Kdoc values of different OPs effectively over the last three decades. In this study, we attempted to build a general-applicable Kdoc model based on various organic compounds' Kdoc values from the literature since 1973. Two multiple linear regression models, a DOM nonspecific model and an Aldrich HA model, were developed based on two solid and easy to access parameters—molecular connectivity indices (MCI) and polarity correction factors (PCF). In addition, the models' corresponding Kow-Kdoc models, which were mostly used in previous model studies, were developed for comparison. The adjusted determining coefficient (adj-R2) and standard error of the estimate (SEE) of the DOM nonspecific MCI-PCF-Kdoc model were 0.815 and 0.579, respectively, whereas the adj-R2 and SEE for the MCI-PCF-Kdoc model of Aldrich HA reached 0.907 and 0.438, respectively. The Aldrich HA model showed higher pertinence to the nonspecific model. Furthermore, both models exhibited better fit than the Kow-Kdoc models. The dipole moment modification attempts did not significantly improve either MCI-PCF-Kdoc models; hence, the two models were not altered with the dipole moment. The robustness tests by a Jackknifed method showed that the two MCI-PCF-Kdoc models exhibited higher robustness than the Kow-Kdoc. Of all of the OPs, the phenols contributed the most to their robustness. Furthermore, a sensitivity analysis showed that the two MCI-PCF-Kdoc models were sensitive to the robust parameters.

2D- and 3D-QSAR modelling, molecular docking and in vitro evaluation studies on 18β-glycyrrhetinic acid derivatives against triple-negative breast cancer cell line

Triple-negative breast cancers (TNBCs) are one of the most aggressive and complex forms of cancers in women. TNBCs are commonly known for their complex heterogeneity and poor prognosis. The present work aimed to develop a predictive 2D and 3D quantitative structure–activity relationship (QSAR) models against metastatic TNBC cell line. The 2D-QSAR was based on multiple linear regression analysis and validated by Leave-One-Out (LOO) and external test set prediction approach. QSAR model presented regression coefficient values for training set (r2), LOO-based internal regression (q2) and external test set regression (pred_r2) which are 0.84, 0.82 and 0.75, respectively. Five properties, Epsilon4 (electronegativity), ChiV3cluster (valence molecular connectivity index), chi3chain (retention index for three-membered ring), TNN5 (nitrogen atoms separated through 5 bond distance) and nitrogen counts, were identified as important structural features responsible for anticancer activity of MDA-MB-231 inhibitors. Five novel derivatives of glycyrrhetinic acid (GA) named GA-1, GA-2, GA-3, GA-4 and GA-5 were semi-synthesised and screened through the QSAR model. Further, in vitro activities of the derivatives were analysed against human TNBC cell line, MDA-MB-231. The result showed that GA-1 exhibits improved cytotoxic activity to that of parent compound (GA). Further, atomic property field (APF)-based 3D-QSAR and scoring recognise C-30 carboxylic group of GA-1 as major influential factor for its anticancer activity. The significance of C-30 carboxylic group in GA derivatives was also confirmed by molecular docking study against cancer target glyoxalase-I. Finally, the oral bioavailability and toxicity of GA-1 were assessed by computational ADMET studies.Communicated by Ramaswamy H. Sarma

Synthesis, Antimicrobial Activity and QSAR Studies of Some New Sparfloxacin Derivatives

The increasing global health problem of bacterial resistance to the major classes of antibiotics is driving scientists to search for newer antimicrobial agents. The present work was designed to synthesize and evaluate the antimicrobial activities of a new series of sparfloxacin derivatives. Aseries of 22 new sparfloxacin derivatives (1 – 22) were synthesized followed by their spectral characterization and antimicrobial evaluation using serial dilution method. QSAR studies were performed to relate their antimicrobial activity and structure. The results of antimicrobial activity testing against all the three selected bacterial strains (Bacillus subtilis, Staphylococcus aureus, and Escherichia coli) indicated that compounds 21 and 22 exhibited maximum antibacterial potential among all the synthesized sparfloxacin derivatives. Compounds 6, 17, and 22 exhibited maximum antifungal potential against two fungal strains (Candida albicans and Aspergillus niger). The results of QSAR studies revealed the fact that topological parameters, particularly the valence third-order molecular connectivity index, are the major factor in influencing the antibacterial potential of the synthesized molecules. These new derivatives can offer new avenues in the design of better antimicrobial molecules active against drug-resistant bacterial and fungal strains.