Steric Descriptors Research Articles

Design, Synthesis, QSAR Studies, and Molecular Modeling of Some Novel Bis Methyl 2-[3-(benzo[d]thiazol-2-yl)-2-terephthaloyl-bis-4-oxo-thiazolidin- 5-ylidene]acetates and Screening of their Antioxidant and Enzyme Inhibition Properties.

Benzothiazolamine-based bisthiourea precursors were prepared in good yields. These bisthiourea derivatives were cyclized into symmetrical Bis Methyl 2-[3-(benzothiazol- 2-yl)-2-terephthaloyl-bis-4-oxo-thiazolidin-5-ylidene]acetates, by their condensation with (DMAD) dimethyl but-2-meditate in the presence of dry methanol. All these compounds were evaluated for their biological applications. Antioxidant activities were performed by adopting a DPPH radical assay, and an in vitro enzyme inhibition assay was performed to investigate their enzyme inhibitory potential against butyrylcholinesterase (BChE) and acetylcholinesterase (AChE). Molecular modeling and QSAR studies were performed to monitor the binding propensity of imidathiazolidinone derivatives with enzymes and DNA. Also, electronic and steric descriptors were calculated to determine the effect of structure on the activity of imidathiazolidinone derivatives. The characterization of all the synthesized compounds was done by their physical data, FT-IR, NMR and elemental analysis.

Reactivity and Steric Parameters from 2D to 3D Bulky Pyridines: Increasing Steric Demand at Nitrogen with Chiral Azatriptycenes

AbstractSterically hindered pyridines embedded in a three‐dimensional triptycene framework have been synthesized, and their resolution by chiral HPLC enabled access to unprecedented enantiopure pyridines exceeding the known steric limits. The design principles for new axially chiral pyridine derivatives are then described. To rationalize their associations with Lewis acids and transition metals, a comprehensive determination of the steric and electronic parameters for this new class of pyridines was performed. This led to the general parameterization of the steric parameters (percent buried volume %VBur, Tolman cone angle θ, and He8_steric descriptor) for a large set of two‐ and three‐dimensional pyridine derivatives. These parameters are shown to describe quantitatively their interactions with carbon‐ and boron‐centered Lewis acids and were used to predict the ΔG° of association with the prototypical B(C6F5)3 Lewis acid widely used in frustrated Lewis pair catalysis. This first parameterization of pyridine sterics is a fundamental basis for the future development of predictive reactivity models and for guiding new applications of bulky and chiral pyridines in organocatalysis, frustrated Lewis pairs, and transition‐metal catalysis.

Reactivity and Steric Parameters from 2D to 3D Bulky Pyridines: Increasing Steric Demand at Nitrogen with Chiral Azatriptycenes.

Sterically hindered pyridines embedded in a three-dimensional triptycene framework have been synthesized, and their resolution by chiral HPLC enabled access to unprecedented enantiopure pyridines exceeding the known steric limits. The design principles for new axially chiral pyridine derivatives are then described. To rationalize their associations with Lewis acids and transition metals, a comprehensive determination of the steric and electronic parameters for this new class of pyridines was performed. This led to the general parameterization of the steric parameters (percent buried volume %VBur, Tolman cone angle θ, and He8_steric descriptor) for a large set of two- and three-dimensional pyridine derivatives. These parameters are shown to describe quantitatively their interactions with carbon- and boron-centered Lewis acids and were used to predict the ΔG° of association with the prototypical B(C6F5)3 Lewis acid widely used in frustrated Lewis pair catalysis. This first parameterization of pyridine sterics is a fundamental basis for the future development of predictive reactivity models and for guiding new applications of bulky and chiral pyridines in organocatalysis, frustrated Lewis pairs, and transition-metal catalysis.

Monodentate Phosphinoamine Nickel Complex Supported on a Metal-Organic Framework for High-Performance Ethylene Dimerization.

Ethylene dimerization is an efficient industrial chemical process to produce 1-butene, with demanding selectivity and activity requirements on new catalytic systems. Herein, a series of monodentate phosphinoamine-nickel complexes immobilized on UiO-66 are described for ethylene dimerization. These catalysts display extensive molecular tunability of the ligand similar to organometallic catalysis, while maintaining the high stability attributed to the metal-organic framework (MOF)scaffold. The highly flexible postsynthetic modification method enables this study to prepare MOFs functionalized with five different substituted phosphines and 3 N-containing ligands and identify the optimal catalyst UiO-66-L5-NiCl2 with isopropyl substituted nickel mono-phosphinoamine complex. This catalyst shows a remarkable activity and selectivity with a TOF of 29000 (molethyl/molNi/h) and 99% selectivity for 1-butene under ethylene pressure of 15bar. The catalyst is also applicable for continuous production in the packed column micro-reactor with a TON of 72000 (molethyl/molNi). The mechanistic insight for the ethylene oligomerization has been examined by density functional theory (DFT) calculations. The calculated energy profiles for homogeneous complexes and truncated MOF models reveal varying rate-determining step as β-hydrogen elimination and migratory insertion, respectively. The activation barrier of UiO-66-L5-NiCl2 is lower than other systems, possibly due to the restriction effect caused by clusters and ligands. A comprehensive analysis of the structural parameters of catalysts shows that the cone angle as steric descriptor and butene desorption energy as thermodynamic descriptor can be applied to estimate the reactivity turnover frequency (TOF) with the optimum for UiO-66-L5-NiCl2. This work represents the systematic optimization of ligand effect through combination of experimental and theoretical data and presents a proof-of-concept for ethylene dimerization catalyst through simple heterogenization of organometallic catalyst on MOF.

Computer-assisted design of CGC catalysts for ethylene/1-octene copolymerization: A combined DFT and artificial neural network approach

High temperature solution polymerization of ethylene/1-octene is the main means to produce polyolefin elastomer (POE), one of the most important polyolefin materials, and the metallocene catalysts with high-efficiency are the key to achieve POE production. However, there remains a deficiency in a universal approach for creating catalysts for ethylene/1-octene copolymerization. In this document, we outlined a method for designing catalysts for ethylene/1-octene copolymerization by combining density functional theory (DFT) with an artificial neural network (ANN) was proposed. By optimizing the structure and computing electronically a sequence of constrained geometry catalysts (CGC) by DFT, descriptors related to steric and electronic descriptors were gathered to create the dataset. After ANN training, an ANN-based model using for predicting catalyst activity with high R2 value was optimized, and a class of novel highly efficient CGC have been successfully designed. Furthermore, an exploration was conducted into how the structure of catalysts correlates with their performance. The electronic properties of the catalysts were found to have more significantly effect on the catalytic performance than the steric properties. In addition, the effect of the β-site substitution of the cyclopentadienyl group on the catalytic activity of predicted CGC was studied and discussed. The groups with ring-structure, especially with conjugate system, were proved to possess a good promoting effect on the catalytic activity.

Ligand-Controlled Regiodivergent Nickel-Catalyzed Hydroaminoalkylation of Unactivated Alkenes.

Ligand modulation of transition-metal catalysts to achieve optimal reactivity and selectivity in alkene hydrofunctionalization is a fundamental challenge in synthetic organic chemistry. Hydroaminoalkylation, an atom-economical approach for alkylating amines using alkenes, is particularly significant for amine synthesis in the pharmaceutical, agrochemical, and fine chemical industries. However, the existing methods usually require specific substrate combinations to achieve precise regio- and stereoselectivity, which limits their practical utility. Protocols allowing for regiodivergent hydroaminoalkylation from the same starting materials, controlling both regiochemical and stereochemical outcomes, are currently absent. Herein, we report a ligand-controlled, regiodivergent nickel-catalyzed hydroaminoalkylation of unactivated alkenes with N-sulfonyl amines. The reaction initiates with amine dehydrogenation and involves aza-nickelacycle intermediates. Tritert-butylphosphine promotes branched regioselectivity and syn diastereoselectivity, whereas ethyldiphenylphosphine enables linear selectivity, yielding regioisomers with inverse orientation. Systematic evaluation of diverse monodentate phosphine ligands reveals distinct regioselectivity cliffs, and % Vbur (min), a ligand steric descriptor, was established as a predictive parameter correlating ligand structure to regioselectivity. Computational investigations supported experimental findings, offering mechanistic insights into the origins of regioselectivity. Our method provides an efficient and predictable route for amine synthesis, demonstrating broad substrate scope, excellent tolerance toward various functional groups, and practical advantages. These include the use of readily available starting materials and cost-effective nickel(II) salts as precatalysts.

Highly efficient PCCP/Cr(III) ethylene tetramerization catalyst designed using artificial neural network approach

Chromium complexes are feasible for inducing ethylene selective oligomerization to generate valuable linear α-olefins, such as 1-hexene and 1-octene. The current theoretical calculation techniques can effectively accelerate the design of molecular catalysts. In this work, we combined density functional theory (DFT) and artificial neural network (ANN) methods to model vinyl-bridged PCCP/Cr active species and used an ANN-based model to predict and screen the designed novel PCCP ligands. DFT was used to optimize the structure of active species and calculate electronic properties. The datasets were established using steric descriptors, electronic descriptors, and experimental data. The ANN-based models were trained based on the established datasets. We found that the optimized ANN-based models ANN-HT and ANN-OT, respectively for the prediction of 1-hexene and 1-octene selectivity, provide satisfactory predictive performance, with R2 values of 0.962 for ANN-HT and 0.976 for ANN-OT. We investigated the contribution of steric and electronic descriptors on catalytic performance, and the results showed that steric descriptors have a greater contribution to controlling product selectivity compared to electronic descriptors. The similarity between the ethylene oligomerization test results of the experimentally prepared L28/Cr catalyst and the ANN-based model prediction results proved the accuracy of the models. The experimentally prepared L28/Cr exhibited catalytic activity of 2181 kg∙g−1∙h−1 and 65.8 % 1-C8 selectivity under suitable reaction conditions.

QMugs 1.1: Quantum mechanical properties of organic compounds commonly encountered in reactivity datasets

Here, the Quantum Mechanical Properties of Drug-like Molecules (QMugs) dataset is expanded to facilitate its use as training data for surrogate machine learning models to predict quantum mechanical properties for tasks related to chemical reactivity. Small molecules from reaction databases as well as charged and boron-containing compounds from ChEMBL were added. Each of these compounds was passed through a pipeline of MMFF94s/UFF conformer generation, followed by GFN2-xTB optimization and finally a density functional theory single-point calculation at the ωB97X-D/def2-SVP level of theory. In total, 71,632 new molecules were evaluated in this manner. Steric (SASA) and dispersion (Pint) descriptors were computed at the semiempirical GFN2-xTB level of theory for the lowest energy conformer of all species in the enlarged QMugs dataset. The expanded dataset aims to facilitate the construction of surrogate models of much broader scope than the original QMugs dataset which was limited to biologically active compounds.

Assessment of Structural Basis for Thiazolopyridine Derivatives as DNA Gyrase-B Inhibitors.

Tuberculosis (TB) is one of the leading causes of death in the post-COVID- 19 era. It has been observed that there is a devastating condition with a 25-30% increase in TB patients. DNA gyrase B isoform has proved its high potential to be a therapeutically effective target for developing newer and safer anti-TB agents. This study aims to identify minimum structural requirements for the optimization of thiazolopyridine derivatives having DNA gyrase inhibitory activities. Moreover, developed QSAR models could be used to design new thiazolopyridine derivatives and predict their DNA gyrase B inhibitory activity before synthesis. 3D-QSAR and Group-based QSAR (G-QSAR) methodologies were adopted to develop accurate, reliable, and predictive QSAR models. Statistical methods such as kNN-MFA SW-FB and MLR SW-FB were used to correlate dependent parameters with descriptors. Both models were thoroughly validated for internal and external predictive abilities. The 3D-QSAR model significantly correlated steric and electrostatic descriptors with q2 0.7491 and predicted r2 0.7792. The G-QSAR model showed that parameters such as SsOHE-index, slogP, ChiV5chain, and T_C_C_3 were crucial for optimizing thiazolopyridine derivatives as DNA gyrase inhibitors. The 3D-QSAR model was interpreted extensively with respect to 3D field points, and the pattern of fragmentation was studied in the G-QSAR model. The 3D-QSAR and G-QSAR models were found to be highly predictive. These models could be useful for designing potent DNA gyrase B inhibitors before their synthesis.

Computational Chemistry: QSAR of Dental Disease Causing Streptococcus mutans Using Eugenol Derivative Compounds from Clove Essential Oil

Research on the activity of eugenol derived compounds as antimicrobials using the qualitative structure-activity relationship (QSAR) model has been conducted. Molecular structure modeling was executed computationally using the ChemDraw Ultra 12.0 program package. Geometry optimization was performed using AM1 semi-empirical method to stabilize structure with the least energy level. Calculations of hydrophobic, electronic, and steric descriptors were done using Gaussian-09 and Marvin Sketch 64 softwares. Statistical calculations was conducted through multiple linear regression method using the IBM SPSS 25 program. Statistical analysis was carried out based on the statistical requirements of several equation models so that the best equation model was obtained. The analysis with the QSAR equation model obtained that the model 1 equation presented the best results.

Predicting Regioselectivity of Cytosolic Sulfotransferase Metabolism for Drugs.

Cytosolic sulfotransferases (SULTs) are a family of enzymes responsible for the sulfation of small endogenous and exogenous compounds. SULTs contribute to the conjugation phase of metabolism and share substrates with the uridine 5'-diphospho-glucuronosyltransferase (UGT) family of enzymes. UGTs are considered to be the most important enzymes in the conjugation phase, and SULTs are an auxiliary enzyme system to them. Understanding how the regioselectivity of SULTs differs from that of UGTs is essential from the perspective of developing novel drug candidates. We present a general ligand-based SULT model trained and tested using high-quality experimental regioselectivity data. The current study suggests that, unlike other metabolic enzymes in the modification and conjugation phases, the SULT regioselectivity is not strongly influenced by the activation energy of the rate-limiting step of the catalysis. Instead, the prominent role is played by the substrate binding site of SULT. Thus, the model is trained only on steric and orientation descriptors, which mimic the binding pocket of SULT. The resulting classification model, which predicts whether a site is metabolized, achieved a Cohen's kappa of 0.71.

Rational Design of a Low-Data Regime of Pyrrole Antioxidants for Radical Scavenging Activities Using Quantum Chemical Descriptors and QSAR with the GA-MLR and ANN Concepts

A series of pyrrole derivatives and their antioxidant scavenging activities toward the superoxide anion (O2•-), hydroxyl radical (•OH), and 1,1-diphenyl-2-picryl-hydrazyl (DPPH•) served as the training data sets of a quantitative structure-activity relationship (QSAR) study. The steric and electronic descriptors obtained from quantum chemical calculations were related to the three O2•-, •OH, and DPPH• scavenging activities using the genetic algorithm combined with multiple linear regression (GA-MLR) and artificial neural networks (ANNs). The GA-MLR models resulted in good statistical values; the coefficient of determination (R2) of the training set was greater than 0.8, and the root mean square error (RMSE) of the test set was in the range of 0.3 to 0.6. The main molecular descriptors that play an important role in the three types of antioxidant activities are the bond length, HOMO energy, polarizability, and AlogP. In the QSAR-ANN models, a good R2 value above 0.9 was obtained, and the RMSE of the test set falls in a similar range to that of the GA-MLR models. Therefore, both the QSAR GA-MLR and QSAR-ANN models were used to predict the newly designed pyrrole derivatives, which were developed based on their starting reagents in the synthetic process.

A Quantitative Structure-Activity Relationship Approach to Determine Biotoxicity of Amide Herbicides for Ecotoxicological Risk Assessment

Amide herbicides have been widely applied in agriculture and found to be widespread and affect nontarget organisms in the environment. To better understand the biotoxicity mechanisms and determine the toxicity to the nontarget organisms for the hazard and risk assessment, five QSAR models were developed for the biotoxicity prediction of amide herbicides toward five aquatic and terrestrial organisms (including algae, daphnia, fish, earthworm and avian species), based on toxicity concentration and quantitative molecular descriptors. The results showed that the developed models complied with OECD principles for QSAR validation and presented excellent performances in predictive ability. In combination, the investigated QSAR relationship led to the toxicity mechanisms that eleven electrical descriptors (EHOMO, ELUMO, αxx, αyy, αzz, μ, qN-, Qxx, Qyy, qH+, and q-), four thermodynamic descriptors (Cv, Sθ, Hθ, and ZPVE), and one steric descriptor (Vm) were strongly associated with the biotoxicity of amide herbicides. Electrical descriptors showed the greatest impacts on the toxicity of amide herbicides, followed by thermodynamic and steric descriptors.

Computer-assisted design of asymmetric PNP ligands for ethylene tri-/tetramerization: A combined DFT and artificial neural network approach

The combination of computational and experimental sciences accelerates the design and development of molecular catalysts. A general strategy for developing ethylene oligomerization catalysts is still lacking. Consequently, herein, we proposed a widely applicable strategy for designing ethylene oligomerization catalysts. We combined density functional theory (DFT) and an artificial neural network (ANN) to establish a relation between catalyst structure and performance. The structure optimization and electronic calculation of a series of asymmetric PNP/Cr active species were conducted using DFT, and the steric and electronic descriptors were extracted to establish datasets. The catalyst prediction model was constructed using ANN and the leave-one-out cross-validation (LOOCV) method was used to verify the generalization ability of the models. The optimized ANN-based models used to predict 1-hexene and 1-octene selectivity exhibited high R2 values, which indicates satisfactory prediction accuracy of the models. We designed new PNP ligands and successfully predicted the ethylene oligomerization performance of PNP/Cr precatalysts using ANN-based models, which were verified through experiments. In addition, we found that the steric properties more significantly affect the performance of precatalysts than the electronic properties.

Novel ciprofloxacin and norfloxacin-tetrazole hybrids as potential antibacterial and antiviral agents: Targeting S. aureus topoisomerase and SARS-CoV-2-MPro

This study was designed to synthesize hybridizing molecules from ciprofloxacin and norfloxacin by enhancing their biological activity with tetrazoles. The synthesized compounds were investigated in the interaction with the target enzyme of fluoroquinolones (DNA gyrase) and COVID-19 main protease using molecular similarity, molecular docking, and QSAR studies. A QSAR study was carried out to explore the antibacterial activity of our compounds over Staphylococcus aureus a QSAR study, using descriptors obtained from the docking with DNA gyrase, in combination with steric type descriptors, was done obtaining suitable statistical parameters (R2=87.00, QLMO2=71.67, and QEXT2=73.49) to support our results. The binding interaction of our compounds with CoV-2-Mpro was done by molecular docking and were compared with different covalent and non-covalent inhibitors of this enzyme. For the docking studies we used several crystallographic structures of the CoV-2-Mpro. The interaction energy values and binding mode with several key residues, by our compounds, support the capability of them to be CoV-2-Mpro inhibitors. The characterization of the compounds was completed using FT-IR, 1H-NMR, 13C-NMR, 19F-NMR and HRMS spectroscopic methods. The results showed that compounds 1, 4, 5, 10 and 12 had the potential to be further studied as new antibacterial and antiviral compounds

Machine learning prediction of empirical polarity using SMILES encoding of organic solvents.

Machine learning based statistical models have played a significant role in increasing the speed and accuracy with which the chemical and physical properties of chemical compounds can be predicted as compared to the experimental, and traditional ab initio and quantum mechanical approaches. The transformative impact that these techniques have, in the field of chemical sciences has completely changed the way experiments are designed. The last decade has seen the prominence of computer-aided molecular design based on machine learning algorithms. The major challenge has been the generation of machine-readable data in the form of descriptors and observations for training the model, which can again be time-consuming and computationally expensive if atomic coordinates based molecular encoding approach is used. In this study, we have tried to solve this problem using SMILES representation of molecules for generating various topological, physicochemical, electronic and steric descriptors using open-source cheminformatics packages. With the aid of the data generated using these packages, we have been able to develop a simple and explainable quantitative structure property relationship model using artificial neural network based on 7 numerical descriptors and 1 categorical descriptor for predicting the empirical polarity of a wide diversity of organic solvents. Since polarity is the representation of various solute-solvent and solvent-solvent interactions taking place in an organic transformation, its intuition beforehand will definitely help a chemist in a better experimental design. An ANN algorithm based on 8 descriptors was successfully employed to predict the ET(30) values of organic solvents.

Preparation and Evaluation of Sterically Hindered Acridine Photocatalysts

AbstractA practical nickel‐catalyzed protocol for the synthesis of sterically hindered 9‐arylacridines via Negishi type cross‐coupling was developed. The method enables a convenient approach to a set of active acridine photocatalysts starting from commercially available 9‐chloroacridine, inexpensive catalyst [NiCl2(Ph3P)2], and organozinc reagent with no need for additional ligands and precious metals. The synthetic protocol could be readily performed on a gram scale. The photocatalytic efficiency of obtained acridines was tested in decarboxylative thiolation and amination reactions. Structures of 19 acridines were studied by X‐ray analysis, which allowed to propose a steric descriptor relevant to the catalytic activity.magnified image

New Phosphonite Ligands with High Steric Demand and Low Basicity: Synthesis, Structural Properties and Cyclometalated Complexes of Pt(II)

Two phosphonite ligands bearing the highly sterically demanding 2,6-bis (2,6-dimethylphenyl)phenyl group (ArXyl2), PArXyl2(OPhNO2)2 and PArXyl2(OPhNO2,Me)2, were prepared from the parent dihalophosphines PArXyl2X2 (X = Cl, Br) and the corresponding phenols, 4-nitrophenol and 4-nitro-2,6-dimethylphenol, respectively. DFT methods were used to examine their structural features and to determine three steric descriptors, namely the Tolman cone angle, the percentage of buried volume, and the percentage of the coordination sphere protected by the ligand. A comparison with the related terphenyl phosphines is also provided. Reactions of PArXyl2(OPhNO2)2 and PArXyl2(OPhNO2,Me)2 with several Pt(II) precursors were investigated, revealing a high tendency of both phosphonites to undergo C-H activation processes and generate five- or six-membered cyclometalated structures. The coordination chemistry of the new ligands was explored with isolation, among others, of three carbonyl complexes, 1-3∙CO, and the triphenylphosphine adduct 3∙PPh3. X-ray diffraction methods permitted the determination of the solid-state structures of the mononuclear methyl carbonyl complex 1∙CO, the dinuclear chloride-bridged complex 2 and the doubly cyclometalated complex 3∙SMe2, including the conformations adopted by the ligands upon coordination. All of the new compounds were characterized by multinuclear NMR spectroscopy in solution.

QSAR tool for optimization of nitrobenzamide pharmacophore for antitubercular activity

Tuberculosis (TB) is a leading cause of death worldwide from a single infectious agent, Mycobacterium tuberculosis (MTB), especially due to the development of resistant strains and its co-infections in HIV. Quantitative-structure activity relationship (QSAR) studies aid rapid drug discovery. In this work, 2D and 3D QSAR studies were carried out on a series of nitrobenzamide derivatives to design newer analogues for antitubercular activity. 2D QSAR was performed using MLR on a data set showing antitubercular activity. The 3D-QSAR studies were performed by kNN–MFA using simulated annealing variable selection method. Alignment of given set of molecules was carried out by the template-based alignment method and then was used to build the 3D-QSAR model. Robustness and predictive ability of the models were evaluated by using various traditional validating parameters. Different physiochemical, alignment-based, topological, electrostatic, and steric descriptors were generated, which indicated the key structural requirements for optimizing the pharmacophore for better antitubercular activity. For 2D QSAR, the best statistical model was generated using SA-MLR method (r2 = 0.892, q2 = 0.819) while 3D QSAR model was derived using the SA KNN method (q2 = 0.722). The positively contributing descriptors can be incorporated to design new chemical entities for future study.

Eco-friendly synthesis, biological evaluation and QSAR studies of dihydropyrimidinone/thione derivatives

Dihydropyrimidinone/thiones (DHPMs) hold earmarked place in the field of natural and synthetic organic chemistry, because of their therapeutic and pharmacological properties. Synthesis of such biologically important compounds assumes great concern. Objectives The aim of the present work was to synthesize structurally challenging DHPMs through green procedure, evaluated for antibacterial activity and to perform QSAR analysis to attain the structural requirements for the antibacterial activity. The synthesis of dihydropyrimidinone/thiones was accomplished via three-component one-pot cyclocondensation between substituted alkyl/aryl aldehydes, diketone and urea/thiourea in the presence of pine apple juice at room temperature. The structures of the compounds were established by spectral analysis and evaluated for antibacterial activity. Possible correlation between observed biological activity and physicochemical descriptors was studied. Synthesis of DHPMs by pineapple juice as a catalyst, complies with principle of green chemistry. Moreover, this solvent free approach totally offers nonpolluting environment and avoiding the usage of toxic organic solvents. The spectral analysis reveals that all the compounds 1a-1p was in good agreement with the standard values reported in the literature for these types of structures. The tested compounds 1e, 1m &1i, 1o showed maximum activity against S. aureus and E. coli at concentrations i.e250 & 500μg /ml respectively. The statistically significant equations with good coefficient of correlation were obtained by correlating antibacterial activities as dependent variables and substituent constants as independent variable. The result of the QSAR study suggested that steric and thermodynamic descriptors contributed significantly to biological activity. Green synthesis of dihydropyrimidinone/thiones is extremely useful and improved procedure for the Biginelli reaction with excellent yields in shorter reaction time with high purity of the products. Through the iterative computational approach, this study could be utilized to design more potent dihydropyrimidinone/thione analogues as antimicrobial agents. Keywords: Green Synthesis, Dihydropyrimidinone/thioneanalogues, Anti-bacterial.