A combined 3D QSAR and pharmacophore-based virtual screening for the identification of potent p38 MAP kinase inhibitors: an in silico approach

p38 kinase plays a vital role in inflammation mediated by tumor necrosis factor-α and interleukin-1β pathways. Inhibition of p38 kinase provides an effective way to treat inflammatory diseases. 3D-QSAR study was performed to obtain reliable comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models for a series of p38 inhibitors with three different alignment methods (Receptor based, atom by atom matching, and pharmacophore based). Among the different alignment methods, better statistics were obtained with receptor-based alignment (CoMFA: q ² = 0.777, r ² = 0.958; CoMSIA: q ² = 0.782, r ² = 0.927). Superposing CoMFA/CoMSIA contour maps on the p38 active site gave a valuable insight to understand physical factors which are important for binding. In addition, this pharmacophore model was used as a 3D query for virtual screening against NCI database. The hit compounds were further filtered by docking and scoring, and their biological activities were predicted by CoMFA and CoMSIA models.     

Three-dimensional quantitative structure–activity relationship CoMFA/CoMSIA on pyrrolidine-based tartrate diamides as TACE inhibitors

A series of pyrrolidine-based tartrate diamides having selective tumor necrosis factor-α converting enzyme (TACE) inhibitory activity was selected for the three-dimensional quantitative structure–activity relationship (3D-QSAR) studies. Total 76 compounds were selected by considering a high deviation in the biological activity and structural variations. The quality and predictive power of 3D-QSAR, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models for the atom-based, centroid/atom-based, data-based alignments were performed. Various models were developed with the help of these alignments. The best model was developed with data-based alignment. The optimal predictive CoMFA model was obtained with cross-validated r ² = 0.53 with six component, non-cross-validated r ² = 0.94, standard error of estimates 0.23, F-value = 121.98 and optimal CoMSIA model was obtained with cross-validated r ² = 0.53 with five components, non-cross-validated r ² = 0.93, standard error of estimates = 0.24 and F-value = 138.83. These models also showed the best test set prediction with predictive r ² value of 0.65 and 0.73, respectively. Thus, on the basis of predictive power COMSIA model appeared to be the best one. The statistical parameters from these models indicate that the data are being well fitted and also have high predictive ability. Moreover, the resulting 3D-CoMFA/CoMSIA contour maps provide useful guidance for designing of highly active TACE inhibitors.     

Molecular docking and 3D-QSAR studies on checkpoint kinase 1 inhibitors

Checkpoint kinase 1(Chk1) is a promising target for cancer treatment. Here three-dimensional quantitative structure–activity relationship (3D-QSAR) studies were performed on 174 1,4-dihydroindeno[1,2-c]pyrazole inhibitors of Chk1 using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Two satisfactory ligand-based QSAR models were built (CoMFA model: q ² = 0.541, r ² = 0.880, CoMSIA model: q ² = 0.590, r ² = 0.902). The docking-based studies presented a detailed understanding of interaction between the inhibitors and Chk1. The obtained QSAR models are highly predictable (CoMFA model: q ² = 0.567, r ² = 0.891, CoMSIA model: q ² = 0.596, r ² = 0.917). The models were further validated by an external testing set obtaining $ r_{\text{pred}}^{2} $ r pred 2 values 0.896 and 0.923 for CoMFA and CoMSIA, respectively. So our models might be helpful for further modification of 1,4-dihydroindeno[1,2-c]pyrazole derivatives.     

QSAR studies on diclofenac analogues as potent cyclooxygenase inhibitors using CoMFA and CoMSIA

A series of 36 diclofenac analogues were analyzed for structure–activity relationship using CoMFA and CoMSIA. The CoMFA-based equation gave q ² = 0.625 and r ² = 0.973 compared to q ² = 0.773 and r ² = 0.959 for CoMSIA. The CoMSIA and CoMFA contours were analyzed and the structural features contributing to the enhancement of activity were identified. Based on the results obtained from these analysis two compounds were designed which show enhancement in activity compared to the parent compound. The new leads are predicted to be non-toxic through computational methods.     

Design and prediction of new acetylcholinesterase inhibitor via quantitative structure activity relationship of huprines derivatives

Acetylcholinesterase (AChE) is an important enzyme in the pathogenesis of Alzheimer’s disease (AD). Comparative quantitative structure-activity relationship (QSAR) analyses on some huprines inhibitors against AChE were carried out using comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), and hologram QSAR (HQSAR) methods. Three highly predictive QSAR models were constructed successfully based on the training set. The CoMFA, CoMSIA, and HQSAR models have values of r ² = 0.988, q ² = 0.757, ONC = 6; r ² = 0.966, q ² = 0.645, ONC = 5; and r ² = 0.957, q ² = 0.736, ONC = 6. The predictabilities were validated using an external test sets, and the predictive r ² values obtained by the three models were 0.984, 0.973, and 0.783, respectively. The analysis was performed by combining the CoMFA and CoMSIA field distributions with the active sites of the AChE to further understand the vital interactions between huprines and the protease. On the basis of the QSAR study, 14 new potent molecules have been designed and six of them are predicted to be more active than the best active compound 24 described in the literature. The final QSAR models could be helpful in design and development of novel active AChE inhibitors.     

Binding site exploration of CCR5 using in silico methodologies: a 3D-QSAR approach

Chemokine receptor 5 (CCR5) is an important receptor used by human immunodeficiency virus type 1 (HIV-1) to gain viral entry into host cell. In this study, we used a combined approach of comparative modeling, molecular docking, and three dimensional quantitative structure activity relationship (3D-QSAR) analyses to elucidate detailed interaction of CCR5 with their inhibitors. Docking study of the most potent inhibitor from a series of compounds was done to derive the bioactive conformation. Parameters such as random selection, rational selection, different charges and grid spacing were utilized in the model development to check their performance on the model predictivity. Final comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models were chosen based on the rational selection method, Gasteiger-Hückel charges and a grid spacing of 0.5 Å. Rational model for CoMFA (q ² = 0.722, r ² = 0.884, Q ² = 0.669) and CoMSIA (q ² = 0.712, r ² = 0.825, Q ² = 0.522) was obtained with good statistics. Mapping of contour maps onto CCR5 interface led us to better understand of the ligand–protein interaction. Docking analysis revealed that the Glu283 is crucial for interaction. Two new amino acid residues, Tyr89 and Thr167 were identified as important in ligand–protein interaction. No site directed mutagenesis studies on these residues have been reported.     

喹啉酮类小分子p53-MDM2结合抑制剂3D-QSAR研究

目的 设计、合成高活性的小分子p53-MDM2结合抑制剂,建立具有预测能力的3D-QSAR模型。方法 采用分子模拟软件Sybyl,利用比较分子场方法(CoMFA)、比较分子相似性指数法(CoMSIA),选择已报道的具有p53-MDM2结合抑制活性的一类有相同母核的21个异喹啉酮衍生物作为训练集,7个作为预测集进行3D-QSAR模型的建立和验证。结果 模型具有较高q²(q²_CoMFA=0.545,q²_CoMSIA=0.528)和r2(r²_CoMFA=0.984,r²_CoMSIA=0.972)值,表明2组模型具有较高的拟和能力及预测能力。结论 该模型具有较高的预测能力,为设计、合成高活性的小分子p53-MDM2结合抑制剂提供了理论依据。     

Design of novel focal adhesion kinase inhibitors using 3D-QSAR and molecular docking

Focal adhesion kinase (FAK) plays a primary role in regulating the activity of many signaling molecules. Increased FAK expression has been implicated in a series of cellular processes, including cell migration and survival. Inhibiting the activity of FAK for cancer therapy is currently under investigation. Hence, FAK and its inhibitors have been the subject of intensive research. To understand the structural factors affecting inhibitory potency, molecular docking and 3D-QSAR modeling were studied in this project. CoMFA and CoMSIA methods were used for deriving 3D-QSAR models, which were trained with 78 compounds and then were evaluated for predictive ability with additional 19 compounds. Two substructure-based 3D-QSAR models, including CoMFA model (r ² = 0.930; q ² = 0.629) and CoMSIA model (r ² = 0.952; q ² = 0.586), had a good quality to predict the biological activities of new compounds. Meanwhile, using the flexible docking strategy, two docking-based 3D-QSAR models (CoMFA with r ² = 0.914; q ² = 0.594; CoMSIA with r ² = 0.914; q ² = 0.524) were also constructed. The structure–activity relationship has been illustrated clearly by the contour maps gained from the 3D-QSAR models in combination with the docked binding structures. All the results indicated that it might be useful in the rational design of novel FAK inhibitors.     

Pharmacophore-based 3D-QSAR of HIF-1 inhibitors

(Aryloxyamino)benzoic acids and nicotinic/isonicotinic acids represent an important new class of small molecules that inhibit the activation of Hypoxia-Inducible Factor (HIF)-1. In order to understand the factors affecting inhibitory potency of HIF-1 inhibitors, 3 dimensional-quantitative structure activity relationship (3D-QSAR) studies were performed. Since no receptor structure are available, the pharmacophore-based alignment was used for comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The CoMFA and CoMSIA models gave reasonable statistics (CoMFA: q² = 0.564, r²=0.945; CoMSIA: q² = 0.575, r²=0.929). Both CoMFA and CoMSIA results indicate that the steric interaction is a major factor, while CoMSIA suggests importance of hydrogen bonding. These findings about steric and H-bonding effects can be useful to design new inhibitors. Equally contributed in this work.     

Quantitative Structure Activity Relationship between Diazabicyclo[4.2.0]octanes Derivatives and Nicotinic Acetylcholine Receptor Agonists

Three dimensional quantitative structure activity relationship between diazabicyclo[4.2.0]octanes and nicotinic acetylcholine receptor (hα4β2 and hα3β4) agonists was studied using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). From 11 CoMFA and CoMSIA models, CoMSIA with steric and electrostatic fields gave the best predictive models (q²=0.926 and 0.945, r²_ncv=0.983 and 0.988). This study can be used to develop potent hα4β2 receptor agonists with low activity on hα3β4 subtype.     

3D QSAR studies on 5-(2-methylbenzimidazol-1-yl)-N-alkylthiophene-2-carboxamide derivatives as P. falciparum dihydroorotate dehydrogenase (PfDHODH) inhibitors

Malaria is currently one of the world’s most severe endemic diseases, responsible for majority of morbidity and mortality. A large number of drugs are available for its treatment; however, the development of resistance has become more widespread with most of the frontline drug therapies. Inhibitors of PfDHODH have proven efficacy for the treatment of malaria. 3D QSAR studies on some 5-(2-methylbenzimidazol-1-yl)-N-alkylthiophene-2-carboxamide derivatives as PfDHODH inhibitors were performed by comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods to rationalize the structural requirements responsible for the inhibitory activity of these compounds. The alignment strategy was used for these compounds by means of Distill function defined in SYBYL x 1.2. The best CoMFA and CoMSIA models obtained for the training set were statistically significant with q ² of 0.669 and 0.727, cross-validated coefficient (r ² _cv) of 0.603 and 0.698, and conventional coefficients (r ²) of 0.971 and 0.966, respectively. Both the models were validated by an external test set of five compounds giving satisfactory prediction (r ² _pred) of 0.799 and 0.815 for CoMFA and CoMSIA models, respectively. Further the robustness of the model was verified by bootstrapping analysis. Generated CoMFA and CoMSIA models provide useful information for the design of novel inhibitors with better PfDHODH inhibitory activity.     

3D-QSAR study on VEGFR kinase inhibition of aminopyrazolopyridine urea derivatives by CoMFA and CoMSIA

Three-dimensional quantitative structure–activity relationship has been performed on 28 aminopyrazolopyridine ureas derivatives to correlate their chemical structures with their observed VEGFR kinase inhibitory activity. The studies include comparative molecular field analysis (CoMFA), CoMFA region focusing and comparative molecular similarity indices analysis (CoMSIA). An alignment rule for the compounds was defined using Distill in SYBYL. Data set was divided into training and test sets using diversity to validate the models. The constructed CoMFA, CoMFA region-focusing and CoMSIA models produced statistically significant results with the cross-validated correlation coefficients (q ²) of 0.858, 0.884, and 0.794, noncross-validated correlation coefficients (r ²) of 0.990, 0.991, and 0.930 and predicted correlation coefficients \((r_{\text{pred}}^{2} )\) of 0.796, 0.785, and 0.910, respectively. These results ensure the CoMFA and CoMSIA models as a tool to guide the design of series of new potent VEGFR kinase inhibitors.     

新型二芳基三嗪类抗锥体虫病化合物的三维定量构效关系研究

目的 建立具有预测能力的新型二芳基三嗪类抗锥体虫病化合物三维定量构效关系（3D-QSAR）模型。方法 通过对具有抗锥体虫活性的二芳基三嗪类化合物库进行结构分析,利用比较分子场分析法（CoMFA）和比较分子相似性指数分析法（CoMSIA）,建立3D-QSAR模型。结果 模型具有较高q²（q_CoMFA²=0.697,q_CoMSIA²=0.561）和r²（r_CoMFA²=0.998,r_CoMSIA²=0.966）值,表明2组模型具有较高的拟和能力及预测能力。结论 建立的CoMFA和CoMSIA模型均具有良好的预测能力,为设计更高活性的新型二芳基三嗪类抗锥体虫病化合物提供了理论依据和研究方向。     

Predictive 3D-QSAR and HQSAR model generation of isocitrate lyase (ICL) inhibitors by various alignment methods combined with docking study

Isocitrate lyase (ICL) is one of the most important targets in the treatment of Mycobacterium tuberculosis. In this study a diverse set of 2-benzanilide derivatives were aligned by two different methods for CoMFA, CoMSIA, and HQSAR analysis. The best CoMFA model was obtained with the internal validation value (q ²) of 0.730 and conventional coefficient (r ²) of 0.944. Various CoMSIA models were generated and cross-validated. The best cross-validation coefficient (q ²) value was found to be statistically satisfactory (0.688). Both the models were validated by test set of 10 compounds with satisfactory prediction value of (r ² _pred ) 0.725 and 0.631 for CoMFA and CoMSIA, respectively. Cross-validation coefficient value (q ²) of 0.694 and r ² of 0.856 were obtained for HQSAR study. The docking study reveals that large hydrophobic pockets occupy R substitutions of these compounds. An electronically negative surface is observed near R₁ substitution. The results of the 3D-QSAR analysis corroborate with the molecular docking results, and our findings will serve as a basis for further development of better allosteric inhibitors of ICL inhibitors against M. tuberculosis.     

Molecular modeling study of HIV-1 gp120 attachment inhibitors

The viral glycoprotein 120 (gp120) is a glycoprotein exposed on viral surface. The gp120 is essential for virus entry into cells as it plays a vital role in seeking out specific cell surface receptors for entry. In this article, we performed docking and three-dimensional quantitative structure activity relationship (3D-QSAR) study on a series of 48 indole glyoxamide derivatives as gp120 inhibitors. Docking study revealed that the inhibitor docked deeply into the gp120 cavity rather than Phe43 of cluster of differentiation 4 (CD4). 3D-QSAR methodologies, comparative molecular field analysis (CoMFA), and comparative molecular similarity indices analysis (CoMSIA) were utilized to rationalize the structural variations with their inhibitory activities. The docked pose of the most potent molecule (43) was used to determine the structures of other molecules. The CoMFA yielded a model with cross-validated correlation coefficient of (q ²) 0.73 and non-cross-validated correlation coefficient of (r ²) 0.89 with optimum number of components (N?=?3). The CoMSIA models were obtained with the combination of various parameters. Final model was computed with steric, hydrophobic- and hydrogen-bond acceptor (SHA) parameters with reasonable statistics (q ²?=?0.80, r ²?=?0.94 and N?=?5). The predictive power of developed CoMFA and CoMSIA models were assessed by test set (nine molecules). The predictive r _pred ² for CoMFA and CoMSIA model was found to be 0.93 and 0.74, respectively. The generated contour maps were plotted onto the gp120 active site to correlate structural variations with their biological activity in protein environment. Contour map analyses showed the importance of 4-F substitution of indole ring, which made essential electronic interaction with the crucial residue (Trp427). The 3D models could explain nicely the structure–activity relationships of indole glyoxamide analogs. This would give proper guidelines to further enhance the activity of novel inhibitors.     

3D-QSAR and molecular docking studies of hydroxamic acids as peptide deformylase inhibitors

Comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), and molecular docking study were conducted on hydroxamic acids as potent peptide deformylase (PDF) inhibitors. The optimal CoMFA model gave statistically significant results with q ² and r ² values of 0.568 and 0.956, respectively. The optimal CoMSIA model with combination of steric, hydrophobic and H-bond donor fields resulted in the best results with q ² and r ² values of 0.722 and 0.958, respectively. These two models were validated by an external test set of eight compounds with satisfactory predictive r ² values of 0.810 and 0.820, respectively. The contour plots of molecular fields indicated that electrostatic and bulky groups substituted at the R₁ position, and electropositive and small substituted at R₂ position were favorable for the inhibitory activity. In addition, FlexX docking was employed to investigate the binding mode between PDF and its inhibitors. It was found that hydrogen bond interactions might be an important factor for binding affinity of inhibitors in the hydrophobic cavity. Based on the optimal CoMSIA model and FlexX docking, a series of PDF inhibitors with high predictive activities have been designed. This work might provide valuable information in designing more promising PDF inhibitors.     

3D-QSAR research of curcumin derivatives

Curcumin exhibits a great ability in various biological and pharmacological activities. Evaluation of curcumin derivatives served to establish the three-dimensional quantitative structure–activity relationship (3D-QSAR) model which was validated by the evaluation of a serial of 22 compounds. Two favorable 3D-QSAR models (CoMFA with q ² = 0.539, R ² = 0.981; CoMSIA with q ² = 0.451, R ² = 0.907) had been developed to predict the biological activity of curcumin derivatives, and external metric q _pred ² (CoMFA with 0.79; CoMSIA with 0.78) and r _m ² _(overall) (CoMFA with 0.71; CoMSIA with 0.56) were applied to evaluate the ability of prediction. Comparing the results obtained from both standard models, we found that reducing the carbon chains of curcumin (S2 and A1 zones) could increase its MCF-7 cytotoxicity; exchanging acceptor/donor substituent on A2 and A4, A3 and D3 zones could turnover its cytotoxicity of MCF-7. These results help with understanding the specific activity of curcumin compounds and designing new specific MCF-7 inhibitors.     

3D-QSAR study using CoMFA and CoMSIA methods for a series of histone H3 phosphorylation inhibitors

Three-dimensional quantitative structure–activity relationship (3D-QSAR) analysis of inhibitory activities for a series of pyrrolotriazine derivatives against histone H3 phosphorylation (pHH3) was performed using comparative of molecular field analysis (CoMFA) and comparative of molecular similarities indices analysis (CoMSIA) techniques. 62 derivatives were used to establish and validate two models by considering a high deviation in biological activities and structural variations. Optimum CoMFA and CoMSIA models obtained from the training set were statistically significant with cross-validated correlation coefficients q ² of 0.551 and 0.621, and conventional correlation coefficients (r ²) of 0.999 and 0.995, respectively. The predicted correlation coefficients of test set (R ²) for CoMFA and CoMSIA were 0.835 and 0.918, respectively. Two models obtained provide guidelines to trace the features that really matter chiefly with respect to the design of novel pyrrolotriazine derivatives.