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.     

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.     

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.     

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.     

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.     

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.     

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.     

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.     

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.     

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.     

Exploring the crucial structural elements required for tricyclic quinoline analogs as protein kinase CK2 inhibitors by a combined computational analysis

Protein kinase casein kinase 2 (CK2) is involved in a variety of important cellular physiological processes, and aberrant CK2 activity is associated with a wide variety of human diseases. 5-(3-Chlorophenylamino)benzo[c][2,6]naphthyridine-8-carboxylic acid (CX-4945) represents the first orally bioavailable and highly selective small molecule inhibitor of CK2. In the present work, a series of tricyclic quinolone analogs were studied by utilizing a combination of three-dimensional quantitative structure activity relationship, molecular docking, and molecular dynamics (MD) simulation methods. CoMFA and CoMSIA analyses were done using ligand-based and receptor-based (RB) alignment schemes, and the RB CoMSIA model (q ² = 0.647, r ² = 0.934, r _pred ²  = 0.74) including the steric, electronic, and hydrogen bond donor fields shows good correlative and predictive ability. By combining the contour maps of RB CoMSIA model with the binding pocket of human CK2α, the crucial structural elements responsible for inhibitory activity are investigated. Also, the mechanism of how different isomers influence the binding affinity is elucidated from the MD simulation. All these results are in good accordance and complementary to each other, and may provide the rational clues to design more potent CK2 inhibitors.     

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.     

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.     

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 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.     

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。     

Structural features of GABAA receptor antagonists: pharmacophore modeling and 3D-QSAR studies

Pharmacophore modeling, comparative molecular field analysis (CoMFA), and comparative molecular similarity indices analysis (CoMSIA) studies have been carried out on 5-(4-piperidyl)-3-isoxazolol (4-PIOL) analogs as GABA_A receptor antagonists in this study. The best pharmacophore hypothesis generated by PHASE was ADHPR.6, which comprised a hydrogen bond acceptor (A), a hydrogen bond donor (D), a hydrophobic group (H), a positively charged group (P), and an aromatic ring (R). The pharmacophore model provided a good alignment for the further 3D-QSAR analyses, which presented a good R ² value of 0.943, 0.930, and 0.916 for atom-based QSAR model, CoMFA model, and CoMSIA model, respectively. All QSAR models presented good statistical significance and predictivity, the corresponding Q ² values for each 3D-QSAR model are 0.794, 0.569, and 0.637, respectively. Both pharmacophore and CoMSIA results showed that the hydrophobic sites are the key structural feature for GABA_A receptor antagonists with high activities.