CoMSIA Analyses Research Articles

3D-QSAR Studies on High-affinity Phosphodiestera.

Recent studies have found that Phosphodiesterase-4 (PDE4) is closely related to the pathogenesis of depression, cognitive impairment and neurological impairment. Our objective is to develop potent inhibitors of the high-affinity phosphodiesterase 4D isoform (PDE4D) that can serve as radioligands for Positron Emission Tomography (PET) imaging, thereby advancing research in the field of neurological diseases. We employed a multi-step approach combining three-dimensional quantitative structure-activity relationship (3D-QSAR) modeling, molecular docking, classification techniques, and CoMSIA analysis to investigate the conformational relationship of highaffinity PDE4D inhibitors as PET ligands. ADMET and Drug-likeness predictions were also conducted. By utilizing these methods, our aim was to identify more potent PDE4D inhibitors. The results showed that the CoMSIA model with the best principal component scores (n=7) had a cross-validated Q2 value of 0.602 and a non-cross-validated R2 value of 0.976. These results affirmed the excellent predictive capability of the established CoMSIA model. Analysis of the generated 3D-QSAR contour plots highlighted specific regions in the molecular structure of the compounds that can be further optimized and modified. Guided by the contour plots, we designed 100 novel PDE4D inhibitors, and molecular docking was performed for the top 4 compounds with high activity. The molecular docking scores were promising, and ADMET and drug similarity predictions yielded satisfactory results. Taking into consideration these factors, compound 51c was determined to be the optimal compound, laying a solid foundation for further research. For the continued development of PDE4D PET radioligand, these models and new compounds' developing methodology offer a theoretical foundation and crucial references.

CoMFA and CoMSIA Analyses of Novel Chromenone Derivatives as Interleukin 5 Inhibitors

Abstract: Major allergic diseases are caused by an increased accumulation of eosinophils driven by interleukin 5 (IL-5). Chromenone analogues have been demonstrated as a promising scaffold for IL-5 inhibitors to control allergic diseases. Therefore, the molecular interaction of IL-5 inhibitors with biological targets is important for better drug discovery. In this study, we analysed in vitro data using 3D-QSAR (Quantitative Structure-Activity Relationships) modelling to find the IL-5 inhibitory activity and molecular interaction. The results of Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Field Similarity Indices (CoMSIA IB) revealed that the in vitro IL-5 inhibitory activity of these chromenone derivatives exhibited strong correlations with steric and electrostatic factors of the molecules (q2 =0.503, r_ncv^2 = 0.997, and r_bs^2 = 0.989 for CoMFA standard model and q2 =0.623, r_ncv^2 = 0.973, and r_bs^2 = 0.990 for CoMSIA model). 3D-QSAR field contour maps could be generated to identify the important structural features of chromenone analogues relevant to the IL-5 inhibitory activity.

In silico research on new sulfonamide derivatives as BRD4 inhibitors targeting acute myeloid leukemia using various computational techniques including 3D-QSAR, HQSAR, molecular docking, ADME/Tox, and molecular dynamics

Acute myeloid leukemia, a serious condition affecting stem cells, drives uncontrollable myeloblast proliferation, leading to accumulation. Extensive research seeks rapid, effective chemotherapeutics. A potential option is a BRD4 inhibitor, known for suppressing cell proliferation. Sulfonamide derivatives probed essential structural elements for potent BRD4 inhibitors. To achieve this goal, we employed 3D-QSAR molecular modeling techniques, including CoMFA, CoMSIA, and HQSAR models, along with molecular docking and molecular dynamics simulations. The validation of the 2D/3D QSAR models, both internally and externally, underscores their robustness and reliability. The contour plots derived from CoMFA, CoMSIA, and HQSAR analyses played a pivotal role in shaping the design of effective BRD4 inhibitors. Importantly, our findings highlight the advantageous impact of incorporating bulkier substituents on the pyridinone ring and hydrophobic/electrostatic substituents on the methoxy-substituted phenyl ring, enhancing interactions with the BRD4 target. The interaction mode of the new compounds with the BRD4 receptor (PDB ID: 4BJX) was investigated using molecular docking simulations, revealing favorable binding energies, supported by the formation of hydrogen and hydrophobic bonds with key protein residues. Moreover, these novel inhibitors exhibited good oral bioavailability and demonstrated non-toxic properties based on ADMET analysis. Furthermore, the newly designed compounds along with the most active one from series 58, underwent a molecular dynamics simulation to analyze their behavior. The simulation provided additional evidence to support the molecular docking results, confirming the sustained stability of the analyzed molecules over the trajectory. This outcome could serve as a valuable reference for designing and developing novel and effective BRD4 inhibitors. Communicated by Ramaswamy H. Sarma

3D-QSAR assisted design, synthesis and pharmacological evaluation of novel substituted benzamides as procaspase-3 activators and anticancer agents

Novel substituted benzamides were designed using 3D-QSAR analysis, synthesized and screened using in vitro procaspase-3 kinase activation and MTT assays. For the rational design of the novel compounds, we performed 3D-QSAR study and generated statistically validated CoMFA (q2 = 0.736, r2cv = 0.729) and CoMSIA (q2 = 0.751, r2cv = 0.716) models. Generated contour maps using CoMFA and CoMSIA analysis suggested the requirement of steric bulkiness (aromatic ring) along with small electron-withdrawing groups (-Cl, -diCl, -F) for the design of novel compounds. CoMSIA, HBA, and HBD contour maps also suggested the requirement of functional groups (amide and hydrazide) for hydrogen bonding interactions. A total of 40 novel substituted benzamides were designed, and generated 3D-QSAR models were used for prediction of their activity prior to synthesis. ADMET prediction study was also carried out for the selection of better compounds with no toxicity and better physicochemical parameters for the synthesis. Finally, we synthesised and characterised a total of 10 substituted benzamide derivatives. Synthesized compounds were screened for their anticancer activities, in which compound 14c showed IC50 value of 3.13 µM and 6.01 µM against HT-29 and MDA-MB-231, respectively, compound 7c exhibited excellent anticancer activity with an IC50 value of 3.76 µM and 5/63 µM against HT-29 and MDA-MB-231, respectively. Furthermore, a procaspase-3 kinase activation assay was performed, in which both these compounds (7c, EC50 = 3.12 µM, 12b, EC50 = 2.93 µM and 14C EC50 =2.93) demonstrated activation of enzymatic potency and revealed that the anticancer activity of these compounds may be due to activation of the procaspase-3 enzyme. In vitro screening assays confirmed and validated the computational design of novel benzamide derivatives. Compounds 14c and 7c are our lead compounds for further development as anticancer agents based on the induction of apoptosis through procaspase-3 activation.

Profiling the structural determinants of pyrrolidine derivative as gelatinases (MMP-2 and MMP-9) inhibitors using in silico approaches

Quantitative structure activity relationship (QSAR) studies on pyrrolidine derivatives have been established using CoMFA, CoMSIA, and Hologram QSAR analysis to estimate the values (pIC50) of gelatinase inhibitors. When the CoMFA cross-validation value, Q², was 0.625, the training set coefficient of determination, R² was 0.981. In CoMSIA, Q² was 0.749 and R² was 0.988. In the HQSAR, Q² was 0.84 and R² was 0.946. Visualization of these models was performed by contour maps showing favorable and unfavorable regions for activity, while visualization of HQSAR model was performed by a colored atomic contribution graph. Based on the results obtained of external validation, the CoMSIA model was statistically more significant and robust and was selected as the best model to predict new, more active inhibitors. To study the modes of interactions of the predicted compounds in the active site of MMP-2 and MMP-9, a simulation of molecular docking was realized. A combined study of MD simulations and calculation of free binding energy, were also carried out to validate the results obtained on the best predicted and most active compound in dataset and the compound NNGH as control compound. The results confirm the molecular docking results and indicate that the predicted ligands were stable in the binding site of MMP-2 and MMP-9.

Structural Insights on Hyp-Gly-Containing Peptides as Antiplatelet Compounds through Topomer CoMFA and CoMSIA Analysis

Increasing evidence has shown collagen hydrolysate involves a variety of bioactivities. In our previous study, multiple antiplatelet peptides containing Hyp/Pro-Gly were identified in collagen hydrolysates from Salmo salar and silver carp skin and exhibited anti-thrombosis activity without bleeding risks in vivo. However, the relationship between structure and activity remains unknown. We performed 3D-QSAR studies on 23 Hyp/Pro-Gly-containing peptides in which 13 peptides were reported before. CoMFA, Topomer CoMFA and CoMSIA analyses were used to generate the QSAR models. Topomer CoMFA analysis showed a q2 value of 0.710, an r2 value of 0.826, an r2pred value of 0.930, and the results showed that Hyp instead of Pro was more important for improving the antiplatelet activity. CoMSIA analysis showed a q2 value of 0.461, an r2 value of 0.999, and an r2pred value of 0.999. Compared with the electrostatic field and hydrogen bond donor field, the steric field, hydrophobic field and hydrogen bond receptor field have great influence on the activity of antiplatelet peptides. The predicted peptide EOGE exhibited antiplatelet activity induced by ADP, and inhibited thrombus formation (300 μmol/kg bw) without bleeding risks. Combined results of these studies indicate that OG-containing peptides had a potential to be developed into an effective specific medical food in the prevention of thrombotic diseases.

Combination of 2D and 3D-QSAR studies on DAPY and DANA derivatives as potent HIV-1 NNRTIs

With the increasing patients suffered from AIDS, highly active antiretroviral therapy is considered as the most effective way to control this disease, leading to an urgent need for new potent drug discovery. Recently, a series of NNRTIs belonging to DAPY and DANA family was reported as promising anti-HIV agents. The CoMFA and CoMSIA analysis were conducted to build up 3D-QSAR models with satisfactory statistical parameters (q2 = 0.887 and R2 = 0.988 for CoMFA model, q2 = 0.901 and R2 = 0.990 for the optimal CoMSIA model). In order to perform further validation of the 3D-QSAR models, a SMILES-based software CORAL was applied to build up the 2D-QSAR model, which also provided essential structural information for further modifications. All three models showed excellent predictability. Combination of analysis of 3D contour maps derived from CoMFA and CoMSIA models and analysis of 2D molecular fragments derived from CORAL software gave insights into the design of novel anti-HIV agents.

Structural insights on 2-phenylquinazolin-4-one derivatives as tankyrase inhibitors through CoMFA, CoMSIA, topomer CoMFA and HQSAR studies

Tankyrase is an emerging target of 17 membered poly(ADP-ribose)polymerase enzyme family for the treatment of Wnt driven cancers. TNKS are associated with many cellular functions; Wnt–β-catenin signaling, telomere homeostasis, mitotic spindle formation etc. The inhibition of cell growth could be achieved by the inhibition of TNKS through Wnt-signaling mechanism. To develop novel lead molecules as tankyrase inhibitors, we performed 3D-QSAR studies on 37 reported 2-phenylquinazolin-4-one derivatives. Training set contained 29 while test set contained 8 molecules. Four different techniques, CoMFA, CoMSIA, Topomer CoMFA and HQSAR were used to generate the QSAR models. Distill alignment based CoMFA analysis showed q2 value of 0.605, r2ncv value of 0.958 and r2pred value of 0.553. Optimized CoMSIA analysis (SEHD) showed q2 value of 0.580, r2ncv value of 0.953 and r2pred value of 0.723. Topomer CoMFA analysis showed q2 value of 0.684, r2 (conventional correlation coefficient) value of 0.955 and r2pred value of 0.721. HQSAR analysis showed q2, r2 and r2pred values of 0.833, 0.951 and 0.753, respectively. Combined results of all studies provided significant output in terms of substitutions required for activity and can be used for the design of novel quinazolinone derivatives as potent tankyrase inhibitors.

Design of new 3, 5-disubstituted indole as hematological anticancer agents using 3D-QSAR, molecular docking and drug-likeness studies

The World Health Organization (WHO) considers cancer as the deadliest disease, due to the increase in death in the 21 century. In the search for new therapeutic molecules, the scientific researches consider the proviral integration moloney (Pim) kinases as promising therapeutic targets for the treatment of hematological cancers. A series of thirty-four 3,5-disubstituted indole derivatives as potent Pim1 kinase inhibitors were studied using 3D-QSAR (CoMFA and CoMSIA) and molecular docking. CoMFA analysis showed Q2 value of 0.56, R2 value of 0.86 and rtest2 value of 0.78, while CoMSIA analysis showed a Q2 value of 0.73, R2 value of 0.93 and rtest2 value of 0.80. The models were generated using 28 compounds as training set and 6 compounds as test set. Furthermore, the contour maps acquired from the CoMSIA and CoMFA models were used to rationalize the principal structural requirement responsible for theactivity. As results, four new compounds were designed in silico. Furthermore, the newly designed compound X1 and the most active compound 28 were subjected to molecular docking study in order to validate their stability. Molecule docking shows that compound X1 has greater stability than compound 28. The newly compounds were evaluated for in silico toxicity properties and verified the five Lipinski rules for wet-lab applicability.

New molecular insights into dual inhibitors of tankyrase as Wnt signaling antagonists: 3D-QSAR studies on 4H-1,2,4-triazole derivatives for the design of novel anticancer agents

Genetic mutations in APC or CNTBB1 gene with aberrant canonical Wnt/β-catenin pathway are responsible for more than 90% of colorectal carcinogenesis. Tankyrases (TNKS) are known to downregulate Wnt signaling by stabilizing AXIN protein through poly(ADP ribose)polymerization or PARSylation process and subsequently, promoting degradation of intracellular β-catenin. Tankyrase enzymes are modulated by a range of known inhibitors that bind individually to any of the nicotinamide or induced adenosine pockets or as dual binding antagonists. Hence, for designing dual tankyrase inhibitors as Wnt signaling antagonist; we carried out 3D-QSAR studies using a data set of 51 molecules of reported 3,4,5-trisubstituted 4H-1,2,4-triazole derivatives. These reported 51 molecules were divided into a training set (39 molecules) and test set (12 molecules), aligned and subjected to generate CoMFA, CoMSIA, and HQSAR models. CoMFA analysis showed q2 value of 0.694, r2ncv value of 0.991 and r2pred value of 0.641. Optimized CoMSIA analysis (SEHA) showed q2 value of 0.624, r2ncv value of 0.909 and r2pred value of 0.850. Both internal and external validations were performed for generated models of CoMFA and CoMSIA (SEHA) and satisfactory results were obtained. HQSAR analysis showed q2, r2, and r2pred values of 0.781, 0.901, and 0.811, respectively. Applicability domain was also found to be satisfactory with all compounds falling within the range and no outlier was observed. Contour maps from all studies provided significant results with identification of desired spatial arrangement of different atoms or functional groups in a molecule. Triazole ring system-based molecules were reported as potent tankyrase inhibitors. These noteworthy results were employed for the design of different triazole derivatives as potent tankyrase inhibitors, wherein a series of 20 different molecules were designed for evaluation of their potentials as novel tankyrase inhibitors.

Biological evaluation and SAR analysis of novel covalent inhibitors against fructose-1,6-bisphosphatase

Fructose-1,6-bisphosphatase (FBPase) is an attractive target for affecting the GNG pathway. In our previous study, the C128 site of FBPase has been identified as a new allosteric site, where several nitrovinyl compounds can bind to inhibit FBPase activity. Herein, a series of nitrostyrene derivatives were further synthesized, and their inhibitory activities against FBPase were investigated in vitro. Most of the prepared nitrostyrene compounds exhibit potent FBPase inhibition (IC50 < 10 μM). Specifically, when the substituents of F, Cl, OCH3, CF3, OH, COOH, or 2-nitrovinyl were installed at the R2 (meta-) position of the benzene ring, the FBPase inhibitory activities of the resulting compounds increased 4.5–55 folds compared to those compounds with the same groups at the R1 (para-) position. In addition, the preferred substituents at the R3 position were Cl or Br, thus compound HS36 exhibited the most potent inhibitory activity (IC50 = 0.15 μM). The molecular docking and site-directed mutation suggest that C128 and N125 are essential for the binding of HS36 and FBPase, which is consistent with the C128-N125-S123 allosteric inhibition mechanism. The reaction enthalpy calculations show that the order of the reactions of compounds with thiol groups at the R3 position is Cl > H > CH3. CoMSIA analysis is consistent with our proposed binding mode. The effect of compounds HS12 and HS36 on glucose production in primary mouse hepatocytes were further evaluated, showing that the inhibition was 71% and 41% at 100 μM, respectively.

CoMFA, CoMSIA and HQSAR Analysis of 3-aryl-3-ethoxypropanoic Acid Derivatives as GPR40 Modulators.

Human GPR40 receptor, also known as free fatty-acid receptor 1, is a Gprotein- coupled receptor that binds long chain free fatty acids to enhance glucose-dependent insulin secretion. In order to improve the resistance and efficacy, computational tools were applied to a series of 3-aryl-3-ethoxypropanoic acid derivatives. A relationship between the structure and biological activity of these compounds, was derived using a three-dimensional quantitative structure-activity relationship (3D-QSAR) study using CoMFA, CoMSIA and two-dimensional QSAR study using HQSAR methods. Building the 3D-QSAR models, CoMFA, CoMSIA and HQSAR were performed using Sybyl-X software. The ratio of training to test set was kept 70:30. For the generation of 3D-QSAR model three different alignments were used namely, distill, pharmacophore and docking based alignments. Molecular docking studies were carried out on designed molecules using the same software. Among all the three methods used, Distill alignment was found to be reliable and predictive with good statistical results. The results obtained from CoMFA analysis q2, r2cv and r2 pred were 0.693, 0.69 and 0.992 respectively and in CoMSIA analysis q2, r2cv and r2pred were 0.668, 0.648 and 0.990. Contour maps of CoMFA (lipophilic and electrostatic), CoMSIA (lipophilic, electrostatic, hydrophobic, and donor) and HQSAR (positive & negative contribution) provided significant insights i.e. favoured and disfavoured regions or positive & negative contributing fragments with R1 and R2 substitutions, which gave hints for the modifications required to design new molecules with improved biological activity. 3D-QSAR techniques were applied for the first time on the series 3-aryl-3- ethoxypropanoic acids. All the models (CoMFA, CoMSIA and HQSAR) were found to be satisfactory according to the statistical parameters. Therefore such a methodology, whereby maximum structural information (from ligand and biological target) is explored, gives maximum insights into the plausible protein-ligand interactions and is more likely to provide potential lead candidates has been exemplified from this study.

3D-QSAR and Docking Studies on Pyrimidine Derivatives as CSF-1R Inhibitors

Background: Colony Stimulating Factor-1 Receptor (CSF-1R) is associated with malignancy, invasiveness and poor prognosis of tumors, and pyrimidine derivatives are considered as a novel class of CSF-1R inhibitor. Methods: To explore the relationship between the structures of substituted pyrimidine derivatives and their inhibitory activities against CSF-1R, CoMFA and CoMSIA analyses, and molecular docking studies were performed on a dataset of forty-four compounds. Results: We found in CoMFA model including steric and electrostatic fields for the training set, the cross-validated q2 value was 0.617 and the non-cross-validated r2 value was 0.983. While, the crossvalidated q2 value was 0.637 and the non-cross-validated r2 value was 0.984 in CoMSIA Model which include steric, electrostatic and hydrophobic fields. 3D equipotential maps generated from CoMFA and CoMSIA along with the docking binding structures provided enough information about the structural requirements for better activity. Conclusion: The data generated from the present study helped us to predict the activity of new inhibitors and further design some novel and potent CSF-1R inhibitors.

Insights into the c-Jun N-terminal kinase 3 (JNK3) inhibitors: CoMFA, CoMSIA analyses and molecular docking studies

JNK3, a protein kinase of the MAPK family that is potently activated by a variety of environmental stress and pro-inflammatory cytokines, has been recognized as an important therapeutic target for several neurodegenerative diseases. However, due to the long development cycle and the cost of R&D, no related drugs have been released. By applying the CoMFA and CoMSIA methods, QSAR models that explore the structure-activity relationship of the JNK3 inhibitors are established and validated, the parameters are satisfactory (q2 = 0.774, r2 = 0.991 for CoMFA model and q2 = 0.666, r2 = 0.990 for CoMSIA model) and base on which a series of novel molecules are designed. Molecular docking is conducted to verify the potential of the new compounds, the results shows promising activity. We speculated that the series of compounds S1–S11 might have some therapeutic activity in the c-jnk3 pathway, particularly the “S8” may prove to be the best one, which provided useful guidance for the design of new and efficient subtype selective JNK3 inhibitors.

CoMFA, CoMSIA, Topomer CoMFA, HQSAR, molecular docking and molecular dynamics simulations study of triazine morpholino derivatives as mTOR inhibitors for the treatment of breast cancer

mTOR has become a promising target for many types of cancer like breast, lung and renal cell carcinoma. CoMFA, CoMSIA, Topomer CoMFA and HQSAR were performed on the series of 39 triazine morpholino derivatives. CoMFA analysis showed q2 value of 0.735, r2cv value of 0.722 and r2pred value of 0.769. CoMSIA analysis (SEHD) showed q2 value of 0.761, r2cv value of 0.775 and r2pred value of 0.651. Topomer CoMFA analysis showed q2 value of 0.693, r2 (conventional correlation coefficient) value of 0.940 and r2pred value of 0.720. HQSAR analysis showed q2,r2and r2pred values of 0.694, 0.920 and 0.750, respectively. HQSAR analysis with the combination of atomic number (A), bond type (B) and atomic connections showed q2 and r2 values of 0.655 and 0.891, respectively. Contour maps from all studies provided significant insights. Molecular docking studies with molecular dynamics simulations were carried out on the highly potent compound 36. Furthermore, four acridine derivatives were designed and docking results of these designed compounds showed the same interactions as that of the standard PI-103 which proved the efficiency of 3D-QSAR and MD/MS study. In future, this study might be useful prior to synthesis for the designing of novel mTOR inhibitors.

Quinazoline Derivatives as Anticancer Agents: QSAR, Molecular Docking and in silico Pharmacokinetic Prediction

Abstract: Context: Molecular target specific treatment for the cancer is different from the conventional chemotherapy and radiotherapy in terms of selectivity and specificity towards the cancer cells. Amongst the different molecular targets for cancer, EGFR tyrosine kinase is considered more promising molecular target for discovery and development of the novel anticancer agents. EGFR overexpression and deregulation or mutation are observed in the different kinds of epithelial cancer namely non-small-cell lung cancer, colon cancer, breast cancer etc. Aim: To carry out structure based drug design (SBDD) and ligand based drug design (LBDD) approaches on quinazoline derivatives as EGFR inhibitors. Methods: A set of 25 compounds was used to correlate the structural parameter(s) with its inhibitory effect on EGFR using 3D-QSAR (CoMFA and CoMSIA) technique. Further, the molecular docking simulation was carried out to recognize the interaction of ligands with the active site of EGFR. All the compounds from the dataset were aligned using the distill alignment method. Post alignment of the dataset, CoMFA and CoMSIA analysis were executed. Moreover, pharmacokinetic (ADMET) predictions of all the quinazoline derivatives were carried out using admetSAR tool. Results: CoMFA and CoMSIA both the models were found statistically significant with values of the cross-validation correlation coefficient (q2) as 0.757 and 0.524 respectively and conventional correlation coefficient (r2) as 0.925 and 0.855 respectively. Further, the molecular docking simulation revealed that all the quinazoline derivatives were situated at the same place as that of the standard drug erlotinib with reasonable penetration at the active site of the EGFR. Conclusion: Results of this work provide the information regarding the structure activity relationship and important structure requirements for the interaction of compounds at the active site of the receptor. This information provides a hint for the design of novel analogs as EGFR tyrosine kinase inhibitors. Key words: ErbB, EGFR, 3D-QSAR, Contour maps, Molecular docking, ADMET.

New molecular insights into the tyrosyl-tRNA synthase inhibitors: CoMFA, CoMSIA analyses and molecular docking studies

Drug resistance caused by excessive and indiscriminate antibiotic usage has become a serious public health problem. The need of finding new antibacterial drugs is more urgent than ever before. Tyrosyl-tRNA synthase was proved to be a potent target in combating drug-resistant bacteria. In silico methodologies including molecular docking and 3D-QSAR were employed to investigate a series of newly reported tyrosyl-tRNA synthase inhibitors of furanone derivatives. Both internal and external cross-validation were conducted to obtain high predictive and satisfactory CoMFA model (q2 = 0.611, r2pred = 0.933, r2m = 0.954) and CoMSIA model (q2 = 0.546, r2pred = 0.959, r2m = 0.923). Docking results, which correspond with CoMFA/CoMSIA contour maps, gave the information for interactive mode exploration. Ten new molecules designed on the basis of QSAR and docking models have been predicted more potent than the most active compound 3-(4-hydroxyphenyl)-4-(2-morpholinoethoxy)furan-2(5H)-one (15) in the literatures. The results expand our understanding of furanones as inhibitors of tyrosyl-tRNA synthase and could be helpful in rationally designing of new analogs with more potent inhibitory activities.

Design of 2-Nitroimidazooxazine Derivatives as Deazaflavin-Dependent Nitroreductase (Ddn) Activators as Anti-Mycobacterial Agents Based on 3D QSAR, HQSAR, and Docking Study with In Silico Prediction of Activity and Toxicity.

Deazaflavin-dependent nitroreductase (Ddn) is an emerging target in the field of anti-tuberculosis agents. In the present study, 2-nitroimidazooxazine derivatives as Ddn activators were aligned for CoMFA, CoMSIA and HQSAR analysis. The best CoMFA and CoMSIA model were generated with leave-one-out correlation coefficients (q2) of 0.585 and 0.571, respectively. Both the CoMFA and CoMSIA models were also validated by a test set of 11 compounds with satisfactory [Formula: see text] value of 0.701 and 0.667, respectively. Results of 3D QSAR and HQSAR study were used for the designing of novel and potent nitroimidazooxazine derivatives as Ddn activators. 21 novel compounds were designed, and docked into the Ddn enzyme. In docking study compound ng11 showed interaction with key amino acid residues such as Tyr65 and Tyr133, and also showed better ADMET compatibility. The ADMET prediction, docking study and the predicted activity of novel designed compounds revealed that compound ng11 showed good potential as Ddn activators for the treatment of tuberculosis.

Advantages and limitations of classic and 3D QSAR approaches in nano-QSAR studies based on biological activity of fullerene derivatives.

In this contribution, the advantages and limitations of two computational techniques that can be used for the investigation of nanoparticles activity and toxicity: classic nano-QSAR (Quantitative Structure–Activity Relationships employed for nanomaterials) and 3D nano-QSAR (three-dimensional Quantitative Structure–Activity Relationships, such us Comparative Molecular Field Analysis, CoMFA/Comparative Molecular Similarity Indices Analysis, CoMSIA analysis employed for nanomaterials) have been briefly summarized. Both approaches were compared according to the selected criteria, including: efficiency, type of experimental data, class of nanomaterials, time required for calculations and computational cost, difficulties in the interpretation. Taking into account the advantages and limitations of each method, we provide the recommendations for nano-QSAR modellers and QSAR model users to be able to determine a proper and efficient methodology to investigate biological activity of nanoparticles in order to describe the underlying interactions in the most reliable and useful manner.Electronic supplementary materialThe online version of this article (doi:10.1007/s11051-016-3564-1) contains supplementary material, which is available to authorized users.

Prediction of stability for polychlorinated biphenyls in transformer insulation oil through three-dimensional quantitative structure-activity relationship pharmacophore model and full factor experimental design

Based on the obtained data of half-lives(t1/2) for 31 polychlorinated biphenyl congeners(PCBs), 3D quantitative structure-activity relationship(QSAR) pharmacophore was used to establish a 3D QSAR model to predict the t1/2 values of the remaining 178 PCBs, using the structural parameters as independent variables and lgt1/2 values as the dependent variable. Among this process, the whole data set(31 compounds) was divided into a training set(24 compounds) for model generation and a test set(7 compounds) for model validation. Then, the full factor experimental design was used to research the potential second-order interactional effect between different substituent positions, obtaining the final regulation scheme for PCB. At last, a 3D QSAR pharmacophore model was established to validate the reasonable regulation targeting typical PCB with respect to half-lives and thermostability. As a result, the cross-validation correlation coefficient(q2) obtained by the 3D QSAR model was 0.845(>0.5) and the coefficient of determination(r2) obtained was 0.936(>0.9), indicating that the models were robust and predictive. CoMSIA analyses upon steric, electrostatic and hydrophobic fields were 0.7%, 85.9%, and 13.4%, respectively. The electrostatic field was determined to be a primary factor governing the t1/2. From CoMSIA contour maps, t1/2 increased when substituents possessed electropositive groups at the 2′-, 3-, 3′-, 5- and 5′- positions and electronegative groups at the 3-, 3′-, 5-, 6- and 6′- positions, which could increase the PCB stability in transformer insulation oil. Modification of two typical PCB congeners(PCB-77 and PCB-81) showed that the lgt1/2for three selected modified compounds increased by 13%(average ratio) compared with that of each congener and the thermostability of them were higher, validating the reasonability of the regulatory scheme obtained from the 3D QSAR model. These results are expected to be beneficial in predicting t1/2 values of PCB homologues and derivatives and in providing a theoretical foundation for further elucidation of the stability of PCBs.