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 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.     

CoMFA and CoMSIA analysis of protein kinase B (PKBβ) inhibitors using various alignment methods

Protein kinase B (PKB) is considered as a key mediator of proliferation and survival pathways, which involved in the development of several human cancers. PKB is a recognized target for the development of small-molecule inhibitors for the treatment of cancer. In this study a diverse set of 73 PKBβ inhibitors were aligned by three different methods (pharmacophore, docking-based, and rigid body alignment) for CoMFA and CoMSIA analysis. The best 3D QSAR models were obtained using pharmacophore-based alignment. CoMFA and CoMSIA models were found statistically significant with leave-one-out correlation coefficients (q ²) of 0.613 and 0.562 respectively, cross-validated coefficients (r ² _cv ) of 0.609 and 0.558, respectively and conventional coefficients (r ²) of 0.914 and 0.989, respectively. 3D QSAR models were validated by a test set of 12 compounds giving satisfactory predicted correlation coefficients (r ² _pred ) of 0.767 and 0.622 for CoMFA and CoMSIA models, respectively. This study provides valuable clues to design new compounds against PKBβ.     

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.     

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.     

Combined 3D‐QSAR Modeling and Molecular Docking Study on Quinoline Derivatives as Inhibitors of P‐selectin

P‐selectin is a promising target for developing novel atherosclerosis drugs. To understand the structure–activity correlation of quinolines‐based P‐selectin inhibitors, we have carried out a combined molecular docking and three‐dimensional quantitative structure–activity relationship (3D‐QSAR) modeling study. The study has resulted in two types of satisfactory 3D‐QSAR models, including the CoMFA model (r², 0.863; q², 0.589) and CoMSIA model (r², 0.866; q², 0.636), to predict the biological activity of new compounds. The detailed microscopic structures of P‐selectin binding with inhibitors have been studied by molecular docking. We have also developed docking based 3D‐QSAR models (CoMFA with r², 0.934; q², 0.591; CoMSIA with r², 0.896; q², 0.573). The contour maps obtained from the 3D‐QSAR models in combination with the docked binding structures help to better interpret the structure–activity relationship. All of the structural insights obtained from both the 3D‐QSAR contour maps and molecular docking are consistent with the available experimental activity data. The satisfactory results strongly suggest that the developed 3D‐QSAR models and the obtained P‐selectin‐inhibitor binding structures are reasonable for the prediction of the activity of new inhibitors and in future drug design.     

QSAR analysis on PfPK7 inhibitors using HQSAR, CoMFA, and CoMSIA

Plasmodium falciparum protein kinase 7 (PfPK7) is an important drug target for the development of anti-malarial treatment. In this study, hologram quantitative structure–activity relationship (HQSAR), comparative molecular field analysis (CoMFA), and comparative molecular similarity indices analysis (CoMSIA) were performed on a series of imidazopyridazine derivatives of PfPK7 inhibitors. The best HQSAR model was obtained using atoms, connection, donor, and acceptor as fragment distinction parameter with fragment size (4–7) using a hologram length of 353 and 6 components (q ² = 0.770, r ² = 0.964). The receptor-guided alignment has produced better statistical results for both CoMFA (q ² = 0.590, r ² = 0.986) and CoMSIA (q ² = 0.735, r ² = 0.988). The predictive ability of the developed models was further validated by a test set of eight compounds. HQSAR contribution map identified the presence of phenyl ring and cyclohexane moiety makes positive contribution for activity. Furthermore, CoMFA and CoMSIA contour maps suggested that additional bulky groups in cyclohexane moiety would increase the biological activity of PfPK7 inhibitors. Finally, these QSAR models were used to design new virtual molecules for imidazopyridazine derivatives and the results obtained from this study could be useful for further investigations.     

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.     

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.     

Molecular modeling studies on 3,4-dihydroquinazolines as trypanothione reductase inhibitors using 3D-QSAR and docking approaches

The trypanothione reductase (TryR) has been used as a key validated target to guide drug discovery for human African trypanosomiasis (HAT). 3D-QSAR and docking studies were performed on a series of 3,4-dihydroquinazolines as TryR inhibitors to establish a molecular model for new drug design. The CoMFA and CoMSIA models resulted from 53 molecules gave r _cv ² values of 0.591 and 0.574, r ² values of 0.968 and 0.943, respectively. The external validation indicated that CoMSIA model with a valid r _m ² value of 0.864 exhibited better predictive power than CoMFA model. 3D contour maps generated from CoMFA and CoMSIA along with the docking analyses have identified several key features responsible for the activity. A set of analogs were proposed by utilizing the results revealed in the present study, and were predicted with significantly improved potencies in the developed models. The results can be served as a useful guideline for designing novel 3,4-dihydroquinazoline derivatives with improved activity against human African trypanosomes.     

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.     

3D QSAR study of 4H-chromen-1,2,3,4-tetrahydropyrimidine-5-carboxylate derivatives as potential anti-mycobacterial agents

In this study, 3D QSAR (CoMFA and CoMSIA) analysis was performed on 4H-chromen-1,2,3,4-tetrahydropyrimidine-5-carboxylate derivatives as potential anti-mycobacterial agents. ‘Distill’ function in SYBYL X 1.2 was used for alignment of the molecules. The best CoMFA and CoMSIA models were obtained for the training set compounds with leave-one-out correlation coefficients (q ²) of 0.753 and 0.646, cross validated correlation coefficients (r _cv ² ) of 0.714 and 0.619, and conventional coefficients (r ²) of 0.975 and 0.983, respectively. Both the models were validated by a test set of 8 compounds giving satisfactory prediction (r _pred ² ) of 0.788 and 0.663 for CoMFA and CoMSIA models, respectively. The results of the study would provide useful information for the design of new compounds and it would also help in prediction of activity of designed compounds prior to their synthesis.     

Design of novel anaplastic lymphoma kinase (ALK) inhibitors based on predictive 3D QSAR models using different alignment strategies

Anaplastic lymphoma kinase (ALK) is involved in many signaling mechanisms that lead to cell-cycle progression; overexpression of ALK has been found in many types of cancers. ALK is a recognized target for the development of small-molecule inhibitors for the treatment of cancer. In this study, a diverse set of 71 ALK inhibitors were aligned by three different methods (pharmacophore, docking-based, and rigid body (Distill) alignment) for the development of comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models. The best 3D QSAR models were obtained, which used rigid body (Distill) alignment of test and training set molecules. CoMFA and CoMSIA models were found statistically significant with leave-one-out correlation coefficients (q ²) of 0.816 and 0.838, respectively; cross-validated coefficients ( $r_{\text{cv}}^{2}$ ) of 0.812 and 0.837, respectively; and conventional coefficients (r ²) of 0.969 and 0.966, respectively. QSAR models were validated by a test set of 14 compounds giving satisfactory prediction of correlation coefficients ( $r_{\text{pred}}^{2}$ ) of 0.910 and 0.904 for CoMFA and CoMSIA models, respectively. Based on the generated contour maps, we have designed 10 novel ALK inhibitors and predicted their activities. Finally, molecular docking study was performed for designed molecules. The designed compounds showed good potential to be used as ALK inhibitors.     

3D-QSAR studies of chromone derivatives as iron-chelating agent

The metal-chelating activity of a series of 48 chromone compounds, evaluated by ferrous (Fe²⁺) chelating test, were subjected to 3D-QSAR studies using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The best CoMFA model obtained from HF/6-31G* geometry optimization and field fit alignment gave cross-validated r ² (q ²) = 0.582, non-cross-validated r ² = 0.975. The best CoMSIA model gave q ² = 0.617, non-cross-validated r ² = 0.917. The resulted CoMFA and CoMSIA contour maps proposed the Fe²⁺-chelating sites of chromone compounds compared with those of quercetin.     

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。     

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.     

Receptor and ligand-based 3D-QSAR study on a series of pyrazines/pyrrolidylquinazolines as inhibitors of PDE10A enzyme

The 3D-QSAR analysis was performed on the set of 175 potent inhibitors of the PDE10A enzyme. Four separate models were built based on different conformations and superimposition methods. They were generated following next criteria: (I) database alignment based on the conformations most similar to the co-crystalized ligand conformation, (II) database alignment based on the minimum energy conformations, (III) docking alignment based on the docked conformations, and (IV) database alignment based on the docking conformations. The best CoMFA and CoMSIA models, derived from superimposition III, show leave-one-out cross-validated correlation coefficient (q ²) values of 0.673 and 0.707 as well as the non-cross-validated correlation coefficient (r ²) values of 0.936 and 0.924, respectively. In addition, the satisfactory results, based on the bootstrapping analysis and 10- and 50-fold cross-validation, further indicate the highly statistical significance of the models. The external predictive abilities of these models were evaluated using a prediction set of 35 compounds, producing the predicted correlation coefficients. Results were graphically interpreted in terms of field contribution maps. A DISCOtech pharmacophore model was also constructed to light important structural features that could be responsible for the low- or high-inhibition 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.