Acceptor Fields Research Articles

3D QSAR - Comparative Molecular Similarity Indices Analysis of 2-Heteroarylquinoline-4-amines as Possible Anti-HIV-1 Agents

Three dimensional quantitative structure activity relationships (3D-QSAR), based upon Comparative Molecular Similarity Indices Analysis (CoMSIA) and partial least square analysis (PLS) with dipole moment and shape descriptors for thirty seven 2-heteroarylquinolines as anti-HIV-1 agents were investigated. The training set and test set consisted of 30 and 7 heteroarylquinoline-4-amines respectively. The combination of steric, donor and acceptor fields in CoMSIA gave the best results with cross-validated and conventional correlation coefficients of 0.55 and 0.92 respectively. The predictive ability of CoMSIA was determined using a test set of seven quinoline-4-amine derivatives giving predictive correlation coefficients of 0.67. These results provided further understanding of the relationship between the structural features of quinoline-4-amines and its activities, which should be applicable to design and find new potential anti-HIV-1 agents.

Design of some phthalazine molecules as novel VEGFR-2 target inhibitors through 3D-QSAR modeling, molecular docking and dynamic simulation and pharmacokinetics profiling

AbstractBreast cancer is one of the dominant cause of cancer-related mortality in females, with an incidence of approximately 1.3 million cases annually, necessitating the development of effective therapeutic strategies. In this study, 3D-QSAR models were reported based on Phthalazine derivatives as VEGFR-2 inhibitors. The activities of these derivatives were correlated with the steric (S), electrostatic (E), hydrogen bond acceptor (A), and donor (D), and hydrophobic (H) fields, which served as critical parameters in model development. Statistical studies of these models showed that the best models are; CoMFA_S (Q2 = 0.623, R2 = 0.941), and CoMSIA_E + D (Q2 = 0.615, R2 = 0.977). Based on the insights from the model fields and docking simulation of the template (compound 17), twelve molecules were designed. These novel molecules exhibited stronger potency compared to the template and the standard, Sorafenib. Compound 17A emerged as the most potent, with pIC50 = 5.98, for CoMFA_S and 5.85, for CoMSIA_E + D, and a strong docking affinity of − 97.271 kcal/mol, therefore subjected to a 100-ns MD simulation. Results indicate better interaction and stabilizing potential over Sorafenib, due to the lower RMSD, RMSF, Rg, values and favorable hydrogen bond analyses. These conclusions were validated by Gibbs free energy analysis and MM-GBSA calculations, revealing a more favorable interaction free energy of − 18.48 kcal/mol related to Sorafenib. Furthermore, these designed compounds demonstrated promising pharmacokinetic profiles.

3D-QSAR, ADMET, and molecular docking studies of aztreonam analogs as E. colis inhibitors.

The development of multidrug resistant strains of extended-spectrum β-lactamase-producing Escherichia coli has become a global problem; therefore, the discovery of new antibacterial agents is the only available solution. To improve and propose new compounds with antibacterial activity, the three-dimensional quantitative structure-activity relationship and molecular docking studies were carried out on Aztreonam analogs as E. coli inhibitors in DNA gyrase B. This study's 3D-Quantitative structure-activity relationship model was created using on the Comparative Molecular Field Analysis and the Comparative Molecular Similarity Indices Analysis. Using the Comparative Molecular Field Analysis (Q 2 = 0.73; R 2 = 0.82), excellent predictability was achieved, and the best Comparative Molecular Similarity Indices Analysis model (Q 2 = 0.88; R 2 = 0.9). The generated model's ability to predict outcomes was assessed through external validation using a test set compound and an applicability domain technique. In this study, the steric, electrostatic, and hydrogen bond acceptor fields played a key role in antibacterial activity. The results of the molecular docking revealed that the newly generated compound A6 has the highest binding affinity with DNA gyrase B. It forms 10 hydrogen bonds with amino acid residues of Asn104, Asn274, Asn132, Ser70, Ser237, Thr105, Glu273, and 2 salt bridges with amino acid residues of Ser70 and Glu273 and one pi-pi interacting with Gys271 amino acid residue in the binding site of 5G1, and this result was validated by a new assessment method. We created some novel, highly effective DNA gyrase B inhibitors based on the earlier findings, and the most accurate model predicted their inhibitory actions. The ADMET characteristics and pharmacological similarity of these novel inhibitors were also examined. These findings would be very beneficial in guiding the optimization process for the identification of novel drugs that can address the issue of multiple drug resistance.

Design, 3D-QSAR, molecular docking, ADMET, molecular dynamics and MM-PBSA simulations for new anti-breast cancer agents

Breast cancer is the most frequent form of malignant tumor in women, and represents a major public health problem due to its high mortality rate. Although a multitude of therapeutic options exist for control of this disease, the emergence of resistance to current pharmaceutical treatments underscores the urgency of developing new anti- breast cancer drugs, with a focus on reducing the adverse effects associated with current therapeutic agents. The present study concerns a new series of (23) compounds based on 1,4-quinone and quinoline derivatives to design candidate drugs against breast cancer. For this purpose, integrated computational techniques were applied, including 3D-QSAR, molecular docking and molecular dynamics simulations (MD). CoMFA and CoMSIA were used to build a robust and highly reliable 3D-QSAR models. To validate the model's predictive capabilities, an external validation was carried out. The results of the best model (CoMSIA/SEA) revealed that electrostatic, steric and hydrogen bond acceptor fields had a significant effect on the anti-breast cancer activity of molecules studied. In addition, evaluation of ADMET properties determined whether these newly designed ligands were likely to be selected as drug-candidates. To confirm the binding stability of the selected ligands to aromatase (3S7S) and validate the molecular docking results, molecular dynamics simulations lasting 100 nanoseconds were performed by calculating RMSD, RMSF, RoG, H-bond, SASA and MM-PBSA parameters. As a result, only one designed compound (ligand 5) emerged as the most promising drug candidate for experimental in vitro and in vivo testing, due to its potential inhibition of breast cancer.

Discovery of anti-colon cancer agents targeting wild-type and mutant p53 using computer-aided drug design

Mutations in the p53 gene are common and occur in over 50% of all cancers, as it is involved in DNA damage repair, cell cycle regulation and apoptosis. Moreover, the p53 gene is mutated in 70% of colon cancers. Therefore, the development of drugs to combat this mutation requires urgent attention. With this in mind, in silico drug design approaches were applied on quinoline derivatives with anticancer activity. In 3D-QSAR study, steric, electrostatic, hydrophobic and H-bond acceptor fields (SEHA) play an important role in prediction and design of new colon cancer compounds. Indeed, the two best CoMSIA/SEHA models with (Q 2 = 0.737, R 2 = 0.914, R pred 2 = 0.720) and (Q 2 = 0.738, R 2 = 0.919, R pred 2 = 0.739) show good prediction of human colon carcinoma HCT 116 (p53+/+) and (p53−/−) activities, respectively. Furthermore, the predictive ability and robustness of these models were tested by several validation methods. Molecular docking analyses reveal crucial interactions with the active sites of the p53 protein in both wild type and mutant. Based on these theoretical studies, we designed 10 new compounds with good anticancer activity potential, which were evaluated using ADMET properties. Molecular dynamics simulations were performed to confirm the detailed binding mode of the docking results. Finally, the MM-GBSA based on molecular dynamics simulation confirmed that the designed compounds were able to form stable hydrogen bonding interactions with the crucial residues, which are essential to overcome the p53 mutation in colon cancer. Communicated by Ramaswamy H. Sarma

5D-QSAR studies of 1H-pyrazole derivatives as EGFR inhibitors.

Epidermal growth factor receptor (EGFR) is highlighted as a target for anticancer treatment. Several EGFR inhibitors were approved in cancer treatment. Comparatively, 5D-QSARis a new methodology which considers an ensemble of different induced-fit models. Based on 1H-pyrazole derivatives as EGFR inhibitors, a 5D-QSAR was studied in which the method of quasi-atomistic receptor surface modeling was used. The presented QSAR model showed contributions of the hydrogen bond acceptor, and hydrophobic and salt bridge fields to the activity. The QSAR model was statistically validated and also externally validated applying 19 compounds (test set) which were not included in the model generation process. The scramble tests were performed to further verify the robustness. Apart from exploration of the binding of 1H-pyrazole derivatives to the EGFR, the 5D-QSAR model can be helpful to design of new EGFR inhibitors. The five-dimensional quantitative structure-activity relationship (5D-QSAR) of 1H-pyrazole derivatives as EGFR inhibitors with quasi-atomistic receptor surface modeling approach is described.

Design of novel quinoline derivatives as antibreast cancer using 3D-QSAR, molecular docking and pharmacokinetic investigation.

Breast cancer has been one of the most challenging women's cancers and leading cause of mortality for decades. There are several studies being conducted all the time to find a cure for breast cancer. Quinoline derivatives have shown their potential as antitumor agents in breast cancer therapy. In this work, three-dimensional quantitative structure-activity relationships (3D-QSAR) and molecular docking with aromatase enzyme (Protein Data Bank: 3S7S) studies were performed to suggest the current scenario of quinoline derivatives as antitumor agents and to refine the path of these derivatives to discover and develop new drugs against breast cancer. For developing the 3D-QSAR model, comparative molecular similarity indices analysis (CoMSIA) and comparative molecular field analysis (CoMFA) were included. To attain the high level of predictability, the best CoMSIA model was applied. External validation utilizing a test set has been used in order to validate the predictive capabilities of the built model. According to the findings, electrostatic, hydrophobic and hydrogen bond donor, and acceptor fields had a significant impact on antibreast cancer activity. Thus, we generated a variety of novel effective aromatase inhibitors based on prior findings and we predicted their inhibitory activity using the built model. In addition, absorption, distribution, metabolism, elimination and toxicity properties were employed to explore the effectiveness of new drug candidates.

3D-QSAR, molecular docking and ADMET studies of thioquinazolinone derivatives against breast cancer

Breast cancer is a deadly disease and the second largest cause of mortality on a worldwide platform. Despite the availability of several cancer treatments, life expectancies stay relatively poor. Consequently, the medicinal chemistry community prioritizes the quick discovery of novel anticancer drugs. In recent years, computational approaches have been widely used to accelerate the drug development process. In light of this, in the current work, we performed three-dimensional quantitative structure-activity relationships (3D-QSAR) and molecular docking analyses on thioquinazolinone derivatives with aromatase enzyme (PDB: 3S7S). External validation was used to validate the prediction capabilities of the generated model. The best CoMSIA (comparative molecular similarity indices analysis) model exhibited the significant values of Q2, R2andRpred2. These findings suggested that the electrostatic, hydrophobic and hydrogen bond donor and acceptor fields have a significant effect on inhibition of breast cancer. Thus, a number of innovative potent aromatase inhibitors were designed and their biological activities were predicted based on the best model. Furthermore, molecular docking studies were carried out for the designed compounds against breast cancer. Additionally, ADMET proprieties were used to evaluate drug-likeness of these novel drug candidates. The most active compounds found by these computational studies could be helpful for synthesis and testing as prospective future anti-cancer treatments.

3D-QSAR, ADME-Tox In Silico Prediction and Molecular Docking Studies for Modeling the Analgesic Activity against Neuropathic Pain of Novel NR2B-Selective NMDA Receptor Antagonists

A new class of selective antagonists of the N-Methyl-D-Aspartate (NMDA) receptor subunit 2B have been developed using molecular modeling techniques. The three-dimensional quantitative structure–activity relationship (3D-QSAR) study, based on comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) models, indicate that steric, electrostatic and hydrogen bond acceptor fields have a key function in the analgesic activity against neuropathic pain. The predictive accuracy of the developed CoMFA model (Q2 = 0.540, R2 = 0.980, R2 pred = 0.613) and the best CoMSIA model (Q2 = 0.665, R2 = 0.916, R2 pred = 0.701) has been successfully examined through external and internal validation. Based on ADMET in silico properties, L1, L2 and L3 ligands are non-toxic inhibitors of 1A2, 2C19 and 2C9 cytochromes, predicted to passively cross the blood–brain barrier (BBB) and have the highest probability to penetrate the central nervous system (CNS). Molecular docking results indicate that the active ligands (L1, L2 and L3) interact specifically with Phe176, Glu235, Glu236, Gln110, Asp136 and Glu178 amino acids of the transport protein encoded as 3QEL. Therefore, they could be used as analgesic drugs for the treatment of neuropathic pain.

Development of novel monoamine oxidase B (MAO-B) inhibitors by combined application of docking-based alignment, 3D-QSAR, ADMET prediction, molecular dynamics simulation, and MM_GBSA binding free energy

Unsaturated ketone derivatives are known as inhibitors of monoamine oxidase B (MAO-B), a potential drug target of Parkinson’s disease. Here, docking-based alignment, 3 D-QSAR (three-dimensional quantitative structure-activity relationship) studies, ADMET (absorption, distribution, metabolism, excretion, and toxicity) prediction, molecular dynamics (MD) simulation, and MM_GBSA binding free energy were performed on a novel series of MAO-B inhibitors. The objective is to predict new MAO-B inhibitors with high potency activity. The 3 D-QSAR models were created using comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA). Molecular docking findings indicated that compounds with strong inhibitory efficacy also had a high binding affinity. 3 D-QSAR studies showed the importance of steric, electrostatic, and H-bond acceptor fields on the inhibitory activity of MAO-B. Based on the appropriate 3 D-QSAR model, a new series of MAO-B inhibitors were predicted and their pharmacokinetic characteristics were evaluated using in silico ADMET prediction. All screened compounds show good oral bioavailability without any side effects. Moreover, the dynamic behavior and stability of the most active compounds were evaluated using MD simulations. The results showed that unsaturated ketone derivatives are stable and compact during the 100 ns of MD simulation. Finally, the binding free energy of complexes was determined using the MM_GBSA method; the findings indicated that the T1 compound is more stable (ΔGbinding = −409.506 KJ/mol) than the data set's highest active compound (ΔGbinding = −31.883 KJ/mol). Communicated by Ramaswamy H. Sarma

3D-QSAR, molecular docking, DFT and ADMET studies on quinazoline derivatives to explore novel DHFR inhibitors

Resistance to folate antagonists is caused by mutations in the dihydrofolate reductase (DHFR) genes. These mutations affect the amino acids at positions 51, 59, 108 and 164 of DHFR, which appear to play a major role in malaria treatment failure. Therefore, the design of new drugs able to overcome the problem of antifolate drug resistance should receive urgent attention. In this study, a three-dimensional quantitative structure-activity relationship (3 D-QSAR) and molecular docking studies have been performed on antimalarial quinazoline derivatives. The CoMFA (Q2 = 0.63, R2 = 0.83 and = 0.70) and the CoMSIA (Q2 = 0.584, R2 = 0.816, and = 0.73) models show a good prediction of antimalarial activity. The reliability and robustness of the proposed models have been tested using several validation methods, which showed that the steric, electrostatic, hydrophobic and H-bond acceptor fields of the CoMSIA model play a key role in the prediction of antimalarial activity. Molecular docking studies reveal important interactions between two isomeric compounds (meta and para) and the DHFR receptor in its wild and mutant forms. The obtained outcomes of molecular docking studies have been validated using a new method based on visual inspection. The DFT study of the two isomeric compounds confirms clearly the trends of 3 D-QSAR and molecular docking for the design of new compounds. Moreover, the consistency between theoretical, 3 D-QSAR and molecular docking analysis provides guidance for the design of new drug candidates, which have been tested using ADMET properties and drug likeness analysis. Communicated by Ramaswamy H. Sarma

Molecular Modelling Studies on Thiazole-Based α-Glucosidase Inhibitors Using Docking and CoMFA, CoMSIA and HQSAR.

The biological dataset was retrieved from two series of α-glucosidase inhibitors synthesized by Rahim et al. and Taha et al. and consisted of a total of 46 (forty-six) α- glucosidase inhibitors. The α-glucosidase inhibitory IC50 values (μM; performed against α-glucosidase from Saccharomyces cerevisiae) were converted into negative logarithmic units (pIC50). The CoMFA and CoMSIA models were developed using 37 as a training set, and externally validated using 9 as a test set. The CoMFA models MMFF94 were generated, ranging from 3.4661 to 5.2749 using leave-oneout PLS analysis cross-validated correlation coefficient q2 0.787, a high non-cross-validated correlation coefficient r2 0.819, with a low Standard Error Estimation (SEE) 0.041, F value 1316.074 and r2 pred 0.996. The steric and electrostatic fields contributions were 0.507 and 0.493, respectively. The CoMSIA model q2 0.805, r2 0.833 was attained, (SEE) 0.065, F value 520.302 and r2 pred 0.990. Contribution of steric, electrostatic, hydrophobic, donor and acceptor fields was 0.151, 0.268, 0.223, 0.234, 0.124, respectively. The HQSAR model of the training set exhibits a significant cross-validated correlation coefficient q2 0.800 and non-cross-validated correlation coefficient r2 0.943.

3D-QSAR, docking and ADMET properties of aurone analogues as antimalarial agents

The development of multi-resistant strains of plasmodium parasite has become a global problem, therefore, the discovery of new antimalarial agents is the only available solution. In order to improve and propose new compounds with antimalarial activity, the three-dimensional quantitative structure-activity relationship (3D-QSAR) and molecular docking studies were carried on aurone analogues acting as Qo site inhibitors in cytochrome b. The 3D-QSAR model was established in this study based on the Comparative Molecular Field Analysis (CoMFA) and the Comparative Molecular Similarity Indices Analysis (CoMSIA). The good predictability was obtained using the CoMFA model (Q2 = 0.5; R2 = 0.97; Rpred2 = 0.72) and the best CoMSIA model (Q2 = 0.526; R2 = 0.915; Rpred2= ​0.765). The predictive capacity of the developed model was evaluated through external validation using a test set compound and an applicability domain technique. In this study, the Steric, electrostatic and hydrogen bond acceptor fields played a key role in antimalarial activity. The results of the molecular docking revealed theoretically the importance of the residues his183 and his82 in the active site of the heme bL, this result was validated by a new assessment method. Based on the previous results, we designed several new potent Cytochrome b inhibitors and their inhibitory activities were predicted by the best model. Furthermore, these new inhibitors were analyzed for their ADMET properties and drug likeness. These results would be of great help in leading optimization for new drug discovery that can solve the problem of multiple drug resistance.

A Double-Activity (Green Algae Toxicity and Bacterial Genotoxicity) 3D-QSAR Model Based on the Comprehensive Index Method and Its Application in Fluoroquinolones' Modification.

The comparative molecular similarity index analysis (CoMSIA) model of double-activity quinolones targeting green algae toxicity and bacterial genotoxicity (8:2) was constructed in this paper on the basis of the comprehensive index method. The contour maps of the model were analyzed for molecular modifications with high toxicities. In the CoMSIA model, the optimum number of components n was 7, the cross-validated q2 value was 0.58 (>0.5), the standard deviation standard error of estimate (SEE) was 0.02 (<0.95), F was 1265.33, and the non-cross-validated R2 value was 1 (>0.9), indicating that the model had a good fit and predicting ability. The scrambling stability test parameters Q2, cross-validated standard error of prediction (cSDEP), and dq2/dr2yy were 0.54, 0.25, and 0.8 (<1.2), respectively, indicating that the model had good stability. The external verification coefficient r2pred was 0.73 (>0.6), and standard error of prediction (SEP) was 0.17, indicating that the model had a good external prediction ability. The contribution rates of the steric fields, electrostatic fields, hydrophobic fields, hydrogen bond donor, and acceptor fields were 10.9%, 19.8%, 32.7%, 13.8%, and 22.8%, respectively. Large volume groups were selected for modification of ciprofloxacin (CIP), and the derivatives with increased double-activity characterization values were screened; the increase ratio ranged from 12.31–19.09%. The frequency of derivatives were positive and total energy, bioaccumulation, and environmental persistence was reduced, indicating that the CIP derivatives had good environmental stability and friendliness. Predicted values and CoMSIA model constructed of single activities showed that the CoMSIA model of double activities had accuracy and reliability. In addition, the total scores of the derivatives docking with the D1 protein, ferredoxin-NADP (H) reductases (FNRs), and DNA gyrase increased, indicating that derivatives can be toxic to green algae by affecting the photosynthesis of green algae. The mechanism behind the bactericidal effect was also explained from a molecular perspective.

In Silico Design of Novel Tetra-Substituted Pyridinylimidazoles Derivatives as c-Jun N-Terminal Kinase-3 Inhibitors, Using 2D/3D-QSAR Studies, Molecular Docking and ADMET Prediction

The c-Jun N-terminal Kinase 3 (JNK3) is a family of protein kinases that plays an important role in the neurodegenerative diseases such as Alzheimer’s disease. Here, we have performed 2D and 3D quantitative structure–activity relationship (2D/3D-QSAR), molecular docking, absorption, distribution, metabolism, excretion and toxicity (ADMET) approaches on a series of tetra-substituted pyrdinylimidazoles derivatives as JNK3 inhibitors. The aim was to design the new potent JNK3 inhibitors with high inhibitory activity. The significant 2D-QSAR models showed a good correlation between observed activity and predicted ones (R2PLS = 0.87 and R2ANN = 0.88) using partial least squares (PLS) and artificial neural network (ANN) methods. The three descriptors vsurf_G, lip-don and SMR_VSA1 that are used in the construction of 2D-QSAR models play a significant role in JNK3 inhibition. Comparative molecular similarity indices analysis (CoMSIA) was used to construct the best 3D-QSAR model using PLS method, showing good correlative and predictive capabilities (R2 = 0.96, Q2 = 0.67 and SEE = 0.14). The steric and H-bond acceptor fields play an important role in the variation of biological activity with four principal components. Molecular docking of selective inhibitors confirms the results obtained by 2D/3D-QSAR and indicate that Met 146 and Met 149 are the main residues that used to stabilize the ligand in the active site of JNK3 protein. Based on those satisfactory results, three new compounds were designed and analyzed by in silico ADMET method.

Design of environmentally friendly neonicotinoid insecticides with bioconcentration tuning and Bi-directional selective toxic effects

A 3D-QSAR model was established with CoMSIA to characterize neonicotinoid insecticides. LgBCF values were used as the dependent variable and the molecular structures of 30 compounds were used as the independent variable. This model was used to design neonicotinoid insecticides with reduced bioconcentration. The contour maps from the 3D-QSAR model were used to evaluate substituted sites and different substituents that significantly affected the bioconcentration of neonicotinoid insecticides. The CoMSIA model showed that neonicotinoid insecticide bioconcentration was strongly affected by steric, electrostatic, hydrophobic and hydrogen bond acceptor fields. Using compound 20 as a template, 105 new substituted derivatives with lower bioconcentrations (reduced by 21.34–77.21%) were designed. The toxicities of these derivatives were evaluated, which showed that 100 of the derivatives retained the original toxicity (increased by 0.10–5.67%). Next, a 2D-QSAR model showed that the decreased bioconcentration of the new neonicotinoid insecticides was mainly caused by the total energy and dipole moment. Homology modeling was used to obtain the genetic recombination AChR in sucking-type pests (i.e. aphid, leafhopper, thrips, and Bemisia tabaci) and bees (i.e. Apis mellifera ligustica and Apis cerana). The neonicotinoid insecticides before and after modification were docked with AChRs to complete the screening of derivatives with bi-directional selective toxic effects. LibDock scores showed that Derivative-5, Derivative-18, Derivative-31, and Derivative-65 had bi-directional selective effects on pests and bees. The effects of the Derivative-18 were the most significant, with toxicity increasing by 14.66% in pests and decreasing by 19.42% in bees. We determined via analysis of amino acid residues that Derivative-18 had more hydrophobic amino acids interacting with pest AChRs, and the mode of action was predominantly hydrogen bonding. Conversely, Derivative-18 had fewer hydrophobic amino acids interacting with bee AChRs, and the mode of action there was more reliant on van der Waals forces with weak binding power.

2D-QSAR and 3D-QSAR simulations for the reaction rate constants of organic compounds in ozone-hydrogen peroxide oxidation

Synergistic oxidation of ozone (O3) and hydrogen peroxide (H2O2) is an effective water treatment for the elimination of organic pollutants. In this study, 23 organic compounds were conducted to study the reaction rate constants during O3-H2O2 oxidation. Then, two- and three-dimensional quantitative structure-activity relationship (QSAR) models were established to investigate the factors influencing the reaction rate constants by using multiple linear regression method and comparative molecular similarity index analysis (CoMSIA) method, respectively. Both of the two models showed good performance on predicting the reaction rate constants, the associated statistical indices of 2D-QSAR and 3D-QSAR models were R2 = 0.898 and 0.952, q2 = 0.841 and 0.951, Qext2 = 0.968 and 0.970, respectively. But varied in the influence factors, as for the 2D-QSAR model, three quantum chemical parameters, included dipole moment, the largest change of charge in each atom during the nucleophilic attack, the maximum positive partial charge on a hydrogen atom linked with a carbon atom affected the reaction rate. While in the 3D-QSAR model, the electrostatic field played the most important role in evaluating the reaction rate with the contribution of 35.8%, followed by hydrogen bond acceptor and hydrophobic fields with the contribution of 24.9% and 23.2%, respectively. These two models provided predictive tools to study the influencing factors for the degradation of organics and might potentially be applied for estimating the removal properties of unknown organics in O3-H2O2 oxidation process.

3D-QSAR modeling of maximum steady-state fluxes of some substituted benzenes and quinolone derivatives through polydimethylsiloxane membrane

The maximum steady-state flux of 79 compounds (substituted benzenes, and quinolones and their derivatives) with a wide range of polarity through a PDMS membrane was predicted using a comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) methods. Moreover, the contribution of partial atomic charge to mass transport phenomena was further verified by the correlation of atomic charge to apparent permeability through polydimethylsiloxane (PDMS) membranes. The obtained results indicated superiority of CoMSIA model over CoMFA model. The best CoMSIA model is developed based on the combination of electrostatic and hydrophobic and H-bond acceptor fields (CoMSIA-EHA). The contour maps of electrostatic and hydrophobic and H-bond acceptor fields of CoMSIA model provide an interpretable and logical relationship between chemicals structure and their fluxes, which give useful insights for designing new compounds with higher penetration through the membranes.

QSAR Studies on NTF1836 Analogues as Mycothiol Biosynthesis Inhibitors

Background: Mycothiol (MSH), a thiol compound found in Mycobacterium tuberculosis is required for the growth and survival of bacteria. This unusual compound is found only in actinomycetes. MSH biosynthesis involves many enzymes, one of them is MshC an essential enzyme for M. tuberculosis. Methods: NTF1836 were the first MSH biosynthesis inhibitors identified. QSAR studies were performed on a novel series of NTF1836 as an inhibitor of MshC (enzyme involved in the synthesis of mycothiol). The series of molecules were subjected to CoMFA, CoMSIA and HQSAR studies. Results: The resulted model exhibited good correlation coefficient (r2) of 0.98, 0.87, 0.81 and cross validated correlation coefficient (q2) of 0.76, 0.60, and 0.61 for CoMFA, CoMSIA and HQSAR methods respectively. Contour map analysis revealed the importance of steric, electrostatic, donor, acceptor and hydrophobic fields in determining the activity. Conclusion: The results of our structure activity relationships study reveals key contribution and can serve as a basis for future drug development of antitubercular agent.

Docking and quantitative structure-activity relationship of bi-cyclic heteroaromatic pyridazinone and pyrazolone derivatives as phosphodiesterase 3A (PDE3A) inhibitors.

PDE3s belong to the phosphodiesterases family, where the PDE3A isoform is the major subtype in platelets involved in the cAMP regulation pathway of platelet aggregation. PDE3A inhibitors have been designed as potential antiplatelet agents. In this work, a homology model of PDE3A was developed and used to obtain the binding modes of bicyclic heteroaromatic pyridazinones and pyrazolones. Most of the studied compounds adopted similar orientations within the PDE3A active site, establishing hydrogen bonds with catalytic amino acids. Besides, the structure-activity relationship of the studied inhibitors was described by using a field-based 3D-QSAR method. Different structure alignment strategies were employed, including template-based and docking-based alignments. Adequate correlation models were obtained according to internal and external validations. In general, QSAR models revealed that steric and hydrophobic fields describe the different inhibitory activities of the compounds, where the hydrogen bond donor and acceptor fields have minor contributions. It should be stressed that structural elements of PDE3A inhibitors are reported here, through descriptions of their binding interactions and their differential affinities. In this sense, the present results could be useful in the future design of more specific and potent PDE3A inhibitors that may be used for the treatment of cardiovascular diseases.