Comparative Molecular Field Analysis Model Research Articles

3D QSAR Modeling and Molecular Docking Studies on a Series of Triazole Analogues as Antibacterial Agents

The 3D QSAR analysis using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques is performed on novel nalidixic acid based 1,2,4-triazole derivatives suggested earlier as antibacterial agents. The CoMFA and CoMSIA models employed for a training set of 28 compounds gives reliable values of Q2 (0.53 and 0.52, respectively) and R2 (0.79 and 0.85, respectively). The contour maps produced by the CoMFA and CoMSIA models are used to determine a three-dimensional quantitative structure-activity relationship. Based on the 3D QSAR contours new molecules with high predicted activities are designed. In addition, surflex-docking is performed to confirm the stability of predicted molecules in the receptor.

Design of new CD38 inhibitors based on CoMFA modelling and molecular docking analysis of 4‑amino-8-quinoline carboxamides and 2,4-diamino-8-quinazoline carboxamides

ABSTRACTIn this study, based on molecular docking analysis and comparative molecular field analysis (CoMFA) modelling of a series of 71 CD38 inhibitors including 4‑amino-8-quinoline carboxamides and 2,4-diamino-8-quinazoline carboxamides, new CD38 inhibitors were designed. The interactions of the molecules with the greatest and the lowest activities with the nicotinamide mononucleotide (NMN) binding site were investigated by molecular docking analysis. A CoMFA model with four partial least squares regression (PLSR) components was developed to predict the CD38 inhibitory activity of the molecules. The r2 values for the training and test sets were 0.89 and 0.82, respectively. The Q2 values for leave-one-out cross-validation (LOO-CV) and leave-many-out cross-validation (LMO-CV) tests on the training set were 0.65 and 0.64, respectively. The CoMFA model was validated by calculating several statistical parameters. CoMFA contour maps were interpreted, and structural features that influence the CD38 inhibitory activity of molecules were determined. Finally, seven new CD38 inhibitors with greater activity with respect to the greatest active molecules were designed.

Design, synthesis and biological evaluation of novel zanamivir derivatives as potent neuraminidase inhibitors

Neuraminidase (NA) is an important antiviral drug target. Zanamivir is one of the most potent NA inhibitors. In this paper, a series of zanamivir derivatives as potential NA inhibitors were studied by combination of molecular modeling techniques including 3D-QSAR, molecular docking, and molecular dynamics (MD) simulation. The results show that the best CoMFA (comparative molecular field analysis) model has q2 = 0.728 and r2 = 0.988, and the best CoMSIA (comparative molecular similarity indices analysis) model has q2 = 0.750 and r2 = 0.981, respectively. The built 3D-QSAR models show significant statistical quality and excellent predictive ability. Seven new NA inhibitors were designed and predicted. 20 ns of MD simulations were carried out and their binding free energies were calculated. Two designed compounds were selected to be synthesized and biologically evaluated by NA inhibition and virus inhibition assays. One compound (IC50 = 0.670 µM, SI > 149) exhibits excellent antiviral activity against A/WSN/33 H1N1, which is superior to the reference drug zanamivir (IC50 = 0.873 µM, SI > 115). The theoretical and experimental results may provide reference for development of new anti-influenza drugs.

Insights into the key structural features of N1-ary-benzimidazols as HIV-1 NNRTIs using molecular docking, molecular dynamics, 3D-QSAR, and pharmacophore modeling

N1-ary-benzimidazol (NABZ) derivatives, an important class of HIV-1 nonnucleoside reverse transcriptase inhibitors (NNRTIs), have been considered as one of the most successful agents for treating with AIDS at present. However, their three-dimensional quantitative structure–activity relationship (3D-QSAR) and mechanism of action in the HIV-1 reverse transcriptase (RT) have not been well understood. In this paper, 38 NABZs were firstly docked into the binding pocket of the HIV-1 RT to elucidate their interaction mechanism, and molecular dynamics simulations were then carried out to confirm the reliability of the docking results. The docking-based 3D-QSAR models were generated using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods to give insights into the key structural features of NABZs for their activity. Both models yield satisfactory statistical parameters of the internal and external validation, and the CoMSIA model (Q2 = 0.670, R2 = 0.987, and $$ {r}_{\mathrm{pred}}^2 $$ = 0.954) showed slightly better predictive ability than the CoMFA model (Q2 = 0.613, R2 = 0.985, and $$ {r}_{\mathrm{pred}}^2 $$ = 0.939). The graphical contours demonstrated that the sulfonyl linker was a significant bridge for binding to the HIV-1 RT. The constructed pharmacophore with eight key features further verified the docking and 3D-QSAR results, indicating that the hydrogen-bond acceptor groups at the C4-positon of the arylacetamide moiety were important for the anti-HIV-1 activity, in addition to 3,5-dimethylphenyl, benzimidazole, and arylacetamide moieties. These studies might provide significant insights into the key structural features for designing potent HIV-1 NNRTIs.

2D & 3D-QSAR Study on Novel Piperidine and Piperazine Derivatives as Acetylcholinesterase Enzyme Inhibitors.

Acetylcholinesterase enzyme (AChE) is the main target in Alzheimer's disease therapy and designing of novel AChE inhibitors is a great deal of attention. In this study, 2D-QSAR and 3D-QSAR models were generated using stepwise multiple linear regressions (SW-MLR) and comparative molecular field analysis (CoMFA) respectively. It was found that CoMFA model with r2 of 0.947 for the training set and r2 of 0.816 for the test set is more favorable than model which is established by SW-MLR method with r2 =0.825 and r2pred =0.778 in 2D-QSAR. In addition, obtaining models were validated by cross validation with cut off values of q2 > 0.5 and r2pred > 0.6.

Extraction, identification and structure-activity relationship of antioxidant peptides from sesame (Sesamum indicum L.) protein hydrolysate

To elucidate the sequence, origin and structure-activity relationship of antioxidant peptides from sesame protein, sesame protein was hydrolysed by a dual-enzyme system comprised alcalase and trypsin, then this hydrolysate was fractionated by ultrafiltration and preparative HPLC. Subsequently, peptides in the high antioxidant activity fraction were identified by nano liquid chromatography-electrospray ionization-tandem mass spectrometry (Nano-LC-ESI-MS/MS), finally the structure-activity relationship of antioxidant peptide with the strongest activity in the sesame peptides was illustrated by comparative molecular field analysis (CoMFA). The results showed that seven novel antioxidant peptides were discovered, their sequences were as follows, RDRHQKIG, TDRHQKLR, MNDRVNQGE, RENIDKPSRA, SYPTECRMR, GGVPRSGEQEQQ and AGEQGFEYVTFR. The SYPTECRMR was the hydrolysate of 2S albumin, the others derived from 11S globulin. The SYPTECRMR whose IC50 Values of DPPH and ABTS were 0.105 mg/mL and 0.004 mg/mL respectively exhibited the highest antioxidant activity among the seven sesame peptides. The active site of SYPTECRMR tended to locate on Cys6 and Met8. A positive correlation between Cys6, Met8, the bulky C-terminal amino acid residue (Arg9), the negative charged group around sulphur-containing amino acids and the antioxidant activity of SYPTECRMR was observed from the CoMFA model. The results presented herein suggested that sesame protein hydrolysates have potential applications in functional food due to their high antioxidant activity, the CoMFA model could provide insight into the structure-activity relationship of antioxidant peptide, which is useful to screen, identify and design the novel antioxidant peptide.

Study on the Anti‐Endometrial Cancer Activity of a Series of 4,6‐Diaryl‐2‐pyrimidinamine Derivatives against Endometrial Carcinoma Ishikawa Cell and Their Molecular Design

AbstractEndometrial cancer is a thorny issue that has affected the health of women all over the world. It is also an important public issue which needed to be solved in developing countries. In this article, our team studied on the anti‐endometrial cancer activity of a series of 4,6‐diaryl‐2‐pyrimidinamine derivatives by using CoMFA (comparative molecular field analysis) and CoMSIA (comparative molecular similarity indices analysis) models and designed new small molecules with anti‐endometrial cancer activity. Our group divided randomly a series of 4,6‐diaryl‐2‐pyrimidinamine derivatives into a training set and a test set to build and test the CoMFA and CoMSIA models. The Q2 ( cross‐validated correlation coefficient) and R2 (non‐cross‐validated correlation coefficient) of the CoMFA model and the CoMSIA model were 0.562 and 0.993, 0.55 and 0.958. The result indicated that the model had strong stability and good predictability. Based on the 3D contour maps, we designed several molecules with high anti‐endometrial cancer activity against endometrial carcinoma ishikawa cell.

Investigating the binding mechanism of (4-Cyanophenyl)glycine derivatives as reversible LSD1 by 3D-QSAR, molecular docking and molecular dynamics simulations

Lysine specific demethylase 1 (LSD1) have been regard as an important drug target on the therapy of cancer and leukemia. However, the development of effective reversible LSD1 inhibitors was facing many challenges. In this work, we carried out a molecular modeling study on (4-Cyanophenyl)glycine derivatives as reversible LSD1 inhibitors using 3D-QSAR, molecular docking and molecular dynamics simulations. Molecular docking study revealed the possible binding mechanism of these inhibitors with LSD1. We used comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) to generate 3D-QSAR models. The results showed that our CoMFA model had q2 of 0.785, r2 = 0.994 and r2pred of 0.92, while the best CoMSIA model had q2 of 0.746, r2 = 0.985 and r2pred of 0.86. Molecular dynamics simulations validated the rationality of docking results and predicted the detailed interactions between the ligands and LSD1. An important hydrogen bond network was discovered though MD simulation. Some key residues (FAD, Asp555, Gln358, Tyr761, Lys661 and Trp695) were pointed out after the binding free energy calculation using MM-PBSA method. We hope these result could provide useful information for our further design of potent and selective LSD1 inhibitors.

Molecular design of 1,3,5,7-TetraCN derivatives with reduced bioconcentration using 3D-QSAR modeling, full factorial design, and molecular docking

Three-dimensional quantitative structure–activity relationship (3D-QSAR) models were established for polychlorinated naphthalenes (PCNs) using the logarithms of experimental bioconcentration factors of 10 PCNs in fish as the dependent variable and their molecular structures as the independent variable. The models were used to design PCN derivatives with lower bioconcentration than the parent compounds. The contour maps from the 3D-QSAR models were used to evaluate sites and different substituents for monosubstitution. A full factorial experiment based on the target molecule (1,3,5,7-tetraCN, CN-42) was used to evaluate sites for disubstitution. The established 3D-QSAR models had good predictive abilities for the remaining 65 PCN congeners. Comparative molecular field analysis and comparative molecular similarity indices analysis models showed that PCN bioconcentration was dominated by the electrostatic field. Taking CN-42 as a template, 15 new monosubstituted compounds and 19 new disubstituted compounds with lower bioconcentration than CN-42 were designed using the 3D-QSAR model and full factorial design, respectively. Furthermore, the persistent organic pollutant properties and the practicability of these compounds were evaluated using Gaussian 09. The new compounds were less toxic than CN-42 but showed similar migration, stability, insulation, and flame retardancy. Molecular docking was used to screen for CN-42 derivatives that showed lower bioconcentration than CN-42 in specific tissues.

Towards the In-silico Design of New HSP90 Inhibitors: Molecular Docking and 3D-QSAR CoMFA Studies of Tetrahydropyrido [4, 3-d] Pyrimidine Derivatives as HSP90 Inhibitors.

HSP90 is necessary for the conformational maturation of proteins, proteins disaggregation, folding newly synthesized peptides and the refolding of denatured proteins. The inhibition of HSP90 leads to proteasomal degradations of client proteins that finally kill cancer cells. In this research, molecular docking and comparative molecular field analysis (CoMFA) were used to investigate the interactions of tetrahydropyrido[4,3-d]pyrimidine derivatives with the N-terminal domain binding site of the HSP90 and predicting their inhibitory activities. A CoMFA model with five components and q2 of 0.81 was developed. R2 for training and test sets were 0.96 and 0.79, respectively. Based on extracted Contour maps for this CoMFA model, three new inhibitors with greater pIC50 with respect to the greatest active molecule in the data-set were designed by modifying molecule m45. Molecule m45 and designed inhibitors were docked to the N-terminal domain binding site of the HSP90. Designed inhibitors obtained lower binding energy with respect to m45. Based on extracted CoMFA contour maps, bulky substituents are favored for the R1 group and in R3 group, short and bulky substituents increase the activity of molecules. Less bulky and longer substituents are favored for R2. The molecular docking analysis of compound m45 with the N-terminal domain binding site of the HSP90 show hydroxyl group on phenyl ring is necessary to form hydrogen bonding with hydrophilic residues in binding site and a conserved water molecule. Molecule m45 has Pi-Sigma interaction with phenyl ring in the side chain of Phenylalanine 138 via isopropyl substituent on meta position of the phenyl ring. Also, Molecule m45 forms carbon-hydrogen bond with oxygen atoms at the side chain of Aspartic acid 54 and Asparagine 51 via its dimethylamine group. Others are Van der Waals interactions.

Insights into the interaction and binding mode of a set of antifungal azoles as inhibitors of potential fungal enzyme-based targets.

Azole-containing compounds are a kind of chemical entities of natural and synthetic origin having a wide-range of activities. They are therefore considered as important moieties for fungicide development, mostly due to the possible action on several enzyme-based targets. As part of our research on fungicidal agents, the relationship between the ligand-enzyme affinities of several synthetic azole-containing compounds against a set of fungal enzyme-based targets was in silico evaluated through molecular docking. The affinity values of the test compounds were mostly higher than those of the respective test controls. Binding modes between enzymes and test compounds were firstly investigated through Vina scores and ligand-residue interactions. Furthermore, statistically relationships among docking scores were successfully found by multivariate analysis. They were mostly correlated with reported MIC80 values, so it denoted an evident discrimination of the test compounds. Strong electron withdrawing groups on phenylacrylamide moiety were responsible for establishing stronger complexes with the enzyme targets, being trichodiene synthase and α-L-fucosidase the most important ones. Moreover, stability of a set of representative protein/ligand complexes was also analyzed by 10ns molecular dynamics simulations (MD). Significant differences into the MD runs were detected and directly correlated to docking performances. Finally, docking affinity scores and HOMO-LUMO energy gaps resulted well predicted by comparative molecular field analysis (CoMFA) models, demonstrating the structure type is particularly associated with those calculated properties and these results were thus consistent with the respective validation parameters.

Comparative molecular field analysis and hologram quantitative structure activity relationship studies of pyrimidine series as potent phosphodiesterase 10A inhibitors

The inhibition of phosphodiesterase (PDE) 10A enzyme as an alternative approach is shown to have potential in the current treatment of schizophrenia. In the present work, the inhibition activity of 87 structurally diverse pyrimidine derivatives as PDE10A inhibitors were subjected to 2‐dimensional quantitative structure activity relationship (2D‐QSAR) and 3D‐QSAR studies using hologram QSAR and comparative molecular field analysis (CoMFA), respectively. The best CoMFA model gave a noncross‐validated correlation coefficient, () = 0.977, SEtrain = 0.247 and predictive correlation coefficient () = 0.957, and SEtest = 0.358. For the hologram QSAR (HQSAR) model, the similar parameters were , SEtrain = 0.565, , and SEtest = 0.872. The leave‐one‐out (LOO) cross‐validated r2 (q2) were 0.78 and 0.8 for the CoMFA and HQSAR models, respectively. These statistics indicate that the CoMFA model is more satisfactory than the HQSAR. Analysis of CoMFA steric and electrostatic contour maps strongly demonstrate that the presence of bulky group at the end of R1 substituent, especially heterocyclic aromatic rings, increase the inhibitory activities, and electronegative groups, such as alkoxide at the R2 position, enhance the inhibitory activities of studied chemicals. The results of this study serves as a guide for the possible modifications and designing of novel pyrimidine derivatives as new potent PDE10A inhibitors.

Investigations of Structural Requirements for BRD4 Inhibitors through Ligand- and Structure-Based 3D QSAR Approaches.

The bromodomain containing protein 4 (BRD4) recognizes acetylated histone proteins and plays numerous roles in the progression of a wide range of cancers, due to which it is under intense investigation as a novel anti-cancer drug target. In the present study, we performed three-dimensional quantitative structure activity relationship (3D-QSAR) molecular modeling on a series of 60 inhibitors of BRD4 protein using ligand- and structure-based alignment and different partial charges assignment methods by employing comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) approaches. The developed models were validated using various statistical methods, including non-cross validated correlation coefficient (r2), leave-one-out (LOO) cross validated correlation coefficient (q2), bootstrapping, and Fisher’s randomization test. The highly reliable and predictive CoMFA (q2 = 0.569, r2 = 0.979) and CoMSIA (q2 = 0.500, r2 = 0.982) models were obtained from a structure-based 3D-QSAR approach using Merck molecular force field (MMFF94). The best models demonstrate that electrostatic and steric fields play an important role in the biological activities of these compounds. Hence, based on the contour maps information, new compounds were designed, and their binding modes were elucidated in BRD4 protein’s active site. Further, the activities and physicochemical properties of the designed molecules were also predicted using the best 3D-QSAR models. We believe that predicted models will help us to understand the structural requirements of BRD4 protein inhibitors that belong to quinolinone and quinazolinone classes for the designing of better active compounds.

A 3D-QSAR Study on the Antitrypanosomal and Cytotoxic Activities of Steroid Alkaloids by Comparative Molecular Field Analysis.

As part of our research for new leads against human African trypanosomiasis (HAT), we report on a 3D-QSAR study for antitrypanosomal activity and cytotoxicity of aminosteroid-type alkaloids recently isolated from the African medicinal plant Holarrhena africana A. DC. (Apocynaceae), some of which are strong trypanocides against Trypanosoma brucei rhodesiense (Tbr), with low toxicity against mammalian cells. Fully optimized 3D molecular models of seventeen congeneric Holarrhena alkaloids were subjected to a comparative molecular field analysis (CoMFA). CoMFA models were obtained for both, the anti-Tbr and cytotoxic activity data. Model performance was assessed in terms of statistical characteristics (R2, Q2, and P2 for partial least squares (PLS) regression, internal cross-validation (leave-one-out), and external predictions (test set), respectively, as well as the corresponding standard deviation error in prediction (SDEP) and F-values). With R2 = 0.99, Q2 = 0.83 and P2 = 0.79 for anti-Tbr activity and R2 = 0.94, Q2 = 0.64, P2 = 0.59 for cytotoxicity against L6 rat skeletal myoblasts, both models were of good internal and external predictive power. The regression coefficients of the models representing the most prominent steric and electrostatic effects on anti-Tbr and for L6 cytotoxic activity were translated into contour maps and analyzed visually, allowing suggestions for possible modification of the aminosteroids to further increase the antitrypanosomal potency and selectivity. Very interestingly, the 3D-QSAR model established with the Holarrhena alkaloids also applied to the antitrypanosomal activity of two aminocycloartane-type compounds recently isolated by our group from Buxus sempervirens L. (Buxaceae), which indicates that these structurally similar natural products share a common structure–activity relationship (SAR) and, possibly, mechanism of action with the Holarrhena steroids. This 3D-QSAR study has thus resulted in plausible structural explanations of the antitrypanosomal activity and selectivity of aminosteroid- and aminocycloartane-type alkaloids as an interesting new class of trypanocides and may represent a starting point for lead optimization.

Computer aided drug design based on 3D-QSAR and molecular docking studies of 5-(1H-indol-5-yl)-1,3,4-thiadiazol-2-amine derivatives as PIM2 inhibitors: a proposal to chemists.

PIM2 kinase plays a crucial role in the cell cycle events including survival, proliferation, and differentiation in normal and neoplastic neuronal cells. Thus, it is regarded as an essential target for cancer pharmaceutical. Design of novel 5-(1H-indol-5-yl)-1,3,4-thiadiazol-2-amine derivatives with enhanced PIM2 inhibitory activity. A series of twenty-five PIM2 inhibitors reported in the literature containing 5-(1H-indol-5-yl)-1,3,4-thiadiazol-2-amines scaffold was studied by using two computational techniques, namely, three-dimensional quantitative structure activity relationship (3D-QSAR) and molecular docking. The comparative molecular field analysis (CoMFA) and comparative molecular similarity indexes analysis (CoMSIA) studies were developed using nineteen molecules having pIC50 ranging from 8.222 to 4.157. The best generated CoMFA and CoMSIA models exhibit conventional determination coefficients R2 of 0.91 and 0.90 as well as the Leave One Out cross-validation determination coefficients Q2 of 0.68 and 0.62, respectively. Moreover, the predictive ability of those models was evaluated by the external validation using a test set of six compounds with predicted determination coefficients Rtest 2 of 0.96 and 0.96, respectively. Besides, y-randomization test was also performed to validate our 3D-QSAR models. The most and the least active compounds were docked into the active site of the protein (PDB ID: 4 × 7q) to confirm those obtained results from 3D-QSAR models and elucidate the binding mode between this kind of compounds and the PIM2 enzyme. These satisfactory results are not offered help only to understand the binding mode of 5-(1H-indol-5-yl)-1,3,4-thiadiazol series compounds into this kind of targets, but provide information to design new potent PIM2 inhibitors.

Exploration of structural and physicochemical properties of small molecules to inhibit NMDA functionality

The N-methyl-D-aspartate (NMDA) is the family of glutamate receptor, which is involved in controlling synaptic plasticity and memory function; but overactivation of this receptor results to excess intracellular calcium formation, triggers neuronal injury and also involves in several pathologies. Both ligand- and structure-based quantitative structure-activity relationship (QSAR), pharmacophore, docking and simulation studies have been performed on a set of structurally diverse inhibitors to explore prime molecular structural features involve for specific binding to NMDA, and vis-a-vis inhibiting enzyme activity. 3D QSAR studies, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models showed the importance of steric, electrostatic and hydrophobic features; while hydrogen bond acceptor and hydrophobic features are depicted as important pharmacophore features of the molecule. Molecular docking and simulation studies corroborated the consequence of the features obtained from ligand-based Bayesian model (AUROCcv = 0.878); 3D QSAR CoMFA (R2 = 0.895, se = 0.513, Q2 = 0.602, R2pred = 0.673); CoMSIA (R2 = 0.877, se = 0.555, Q2 = 0.615, R2pred = 0.727); hologram QSAR (Q2 = 0.812, R2 = 0.941, R2pred = 0.772), and pharmacophore models (Q2 = 0.926, R2 = 0.927, R2pred = 0.621). Presence of aromatic ring, hetero and halogen atoms along with alkyl group of molecular scaffold shows their importance for binding affinity to NMDA receptor. Stability of the complex is adjudged by both docking and simulation studies.

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.

An explorative study on potent Gram-negative specific LpxC inhibitors: CoMFA, CoMSIA, HQSAR and molecular docking

Pathogenic Gram-negative bacteria are responsible for nearly half of the serious human infections. Hologram quantitative structure–activity relationships (HQSAR), comparative molecular field analysis (CoMFA), and comparative molecular similarity index analysis (CoMSIA) were implemented on a group of 32 of potent Gram-negative LpxC inhibitors. The most effective HQSAR model was obtained using atoms, bonds, donor, and acceptor as fragment distinction. The cross-validated correlation coefficient (q2), non-cross-validated correlation coefficient (r2), and predictive correlation coefficient (r2Pred) for test set of HQSAR model were 0.937, 0.993, and 0.892, respectively. The generated models were found to be statistically significant as the CoMFA model had (r2 = 0.967, q2 = 0.804, r2Pred = 0.827); the CoMSIA model had (r2 = 0.963, q2 = 0.752, r2Pred = 0.857). Molecular docking was employed to validate the results of the HQSAR, CoMFA, and CoMSIA models. Based on the obtained information, six new LpxC inhibitors have been designed.

Synthesis, evaluation and CoMFA/CoMSIA study of nitrofuranyl methyl N-heterocycles as novel antitubercular agents

A series of novel nitrofuranyl methyl N-heterocycles based on the structure of IIIM-MCD-211 were designed and synthesized. Compounds 6d, 8b and 12a show excellent activity against MTB H37Rv strain (MIC: 0.031–0.062 μg/mL) roughly comparable to INH and IIIM-MCD-211. In addition, a three-dimensional quantitative structure-activity relationship (3D-QSAR) study was performed on the above mentioned chemical series employing comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) techniques. The developed CoMFA and CoMSIA models display high external predictability (r2pred of 0.954 and 0.935, respectively) and good statistical robustness. More importantly, the newly designed compounds 16a and 16b (MIC: <0.016 μg/mL) based on the two models, as expected, were found to be more active than 12a and IIIM-MCD-21. Design and synthesis of more potent nitrofuranyl methyl N-heterocycles as anti-TB agents are currently in progress.

Molecular Modeling Study for the Design of Novel Peroxisome Proliferator-Activated Receptor Gamma Agonists using 3D-QSAR and Molecular Docking.

Type 2 diabetes is becoming a global pandemic disease. As an important target for the generation and development of diabetes mellitus, peroxisome proliferator-activated receptor γ (PPARγ) has been widely studied. PPARγ agonists have been designed as potential anti-diabetic agents. The advanced development of PPARγ agonists represents a valuable research tool for diabetes therapy. To explore the structural requirements of PPARγ agonists, three-dimensional quantitative structure–activity relationship (3D-QSAR) and molecular docking studies were performed on a series of N-benzylbenzamide derivatives employing comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), and surflex-dock techniques. The generated models of CoMFA and CoMSIA exhibited a high cross-validation coefficient (q2) of 0.75 and 0.551, and a non-cross-validation coefficient (r2) of 0.958 and 0.912, respectively. The predictive ability of the models was validated using external validation with predictive factor (r2pred) of 0.722 and 0.682, respectively. These results indicate that the model has high statistical reliability and good predictive power. The probable binding modes of the best active compounds with PPARγ active site were analyzed, and the residues His323, Tyr473, Ser289 and Ser342 were found to have hydrogen bond interactions. Based on the analysis of molecular docking results, and the 3D contour maps generated from CoMFA and CoMSIA models, the key structural features of PPARγ agonists responsible for biological activity could be determined, and several new molecules, with potentially higher predicted activity, were designed thereafter. This work may provide valuable information in further optimization of N-benzylbenzamide derivatives as PPARγ agonists.