Studies on the inhibitory models of pyrazoline derivatives as EGFR kinase inhibitors by 3D-QSAR and molecular docking

The development of EGFR kinase inhibitors attracts much attention of research for treating of cancer in recent years. In this work, based on a dataset composed of 46 EGFR kinase inhibitors, the combination of three-dimensional quantitative structure–activity relationship (3D-QSAR), molecular docking was applied to reveal structural characteristics impacting the inhibitory activity of EGFR, and to provide a better understanding of the binding modes between inhibitors and EGFR kinase. 3D-QSAR models of pyrazoline derivatives were established to reveal how steric, electrostatic, hydrophobic, and H-bond acceptor interactions contribute to inhibitors’ bioactivities, which were unanimous in the docking results. Furthermore, based on the most active compound, several new molecules with high inhibitory activity were obtained.     

Molecular Docking and QSAR Studies on Substituted Acyl(thio)urea and Thiadiazolo [2,3-α] Pyrimidine Derivatives as Potent Inhibitors of Influenza Virus Neuraminidase

Surflex–Dock was employed to dock 36 thiourea and thiadiazolo [2,3-α] pyrimidine derivatives into neuraminidase 1a4g. Molecular docking results showed that hydrogen bonding, electrostatic, and hydrophobic features were important factors affecting inhibitory activities of these neuraminidase inhibitors. Moreover, there was a significant correlation between the predicted binding affinity (total scores) and experimental pIC₅₀ values with correlation coefficient r = 0.846 and p < 0.0001. Hologram quantitative structure–activity relationship, comparative molecular field analysis, and comparative molecular similarity indices analysis were used to develop quantitative structure–activity relationship models. Squared multiple correlation coefficients (r ²) of hologram quantitative structure–activity relationship, comparative molecular field analysis, and comparative molecular similarity indices analysis models were 0.899, 0.878, and 0.865, respectively. Squared cross-validated correlation coefficient (q ²) of hologram quantitative structure–activity relationship, comparative molecular field analysis, and comparative molecular similarity indices analysis models was in turn 0.628, 0.656, and 0.509. In addition, squared multiple correlation coefficients for test set (r ²_test) of hologram quantitative structure–activity relationship, comparative molecular field analysis, and comparative molecular similarity indices analysis models were 0.558, 0.667, and 0.566, respectively. The most active sample ID 2 was taken as a template molecule to design new molecules. Based on the comparative molecular field analysis model, new compounds were designed by LeapFrog. Seven new compounds with improved binding energy and predicted activities were finally obtained.     

Computational Studies of Novel Chymase Inhibitors Against Cardiovascular and Allergic Diseases: Mechanism and Inhibition

To provide a new idea for drug design, a computational investigation is performed on chymase and its novel 1,4‐diazepane‐2,5‐diones inhibitors that explores the crucial molecular features contributing to binding specificity. Molecular docking studies of inhibitors within the active site of chymase were carried out to rationalize the inhibitory properties of these compounds and understand their inhibition mechanism. The density functional theory method was used to optimize molecular structures with the subsequent analysis of highest occupied molecular orbital, lowest unoccupied molecular orbital, and molecular electrostatic potential maps, which revealed that negative potentials near 1,4‐diazepane‐2,5‐diones ring are essential for effective binding of inhibitors at active site of enzyme. The Bayesian model with receiver operating curve statistic of 0.82 also identified arylsulfonyl and aminocarbonyl as the molecular features favoring and not favoring inhibition of chymase, respectively. Moreover, genetic function approximation was applied to construct 3D quantitative structure–activity relationships models. Two models (genetic function approximation model 1 r² = 0.812 and genetic function approximation model 2 r² = 0.783) performed better in terms of correlation coefficients and cross‐validation analysis. In general, this study is used as example to illustrate how combinational use of 2D/3D quantitative structure–activity relationships modeling techniques, molecular docking, frontier molecular orbital density fields (highest occupied molecular orbital and lowest unoccupied molecular orbital), and molecular electrostatic potential analysis may be useful to gain an insight into the binding mechanism between enzyme and its inhibitors.     

3D-QSAR and molecular docking studies on substituted isothiazole analogs as inhibitors against MEK-1 kinase

MEK-1 and MEK-2 are dual-specificity kinases and important components in the mitogen-activated protein kinase pathway. These enzymes are crucial for normal cell survival and are also expressed in several types of cancers, making them important targets for drug design. We have applied an integrated in silico approach that combines comparative molecular field analysis, comparative molecular similarity indices analysis, and molecular docking to study the structural determinants for the recognition of substituted isothiazole analogs as allosteric inhibitors against MEK-1 kinase. The best 3D-QSAR models for comparative molecular field analysis and comparative molecular similarity indices analysis were selected based on statistical parameters. 3D contour maps suggested that bulky or long-chain substitutions at the X position on the core part decrease the inhibitory activity, and the presence of a hydrogen bond donor substitution enhances the activity. The bulky and electronegative substitutions at the Y position on the core part enhance the activity of the inhibitors. Molecular docking studies reveal a large and hydrophobic pocket that accommodates the Y substitution and a polar pocket that accommodates substitutions on the X position and forms hydrogen bonding interactions with MEK-1 kinase. 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 MEK-1 kinase against several cancers.     

Insight into the structural determinants of imidazole scaffold-based derivatives as p38 MAP kinase inhibitors by computational explorations

P38 kinase plays a vital role in the inflammation mediated by tumor necrosis factor-α and interleukin-1β pathways, and thus the inhibitors of p38 kinase provide effective approach for the treatment of inflammatory diseases. Presently, a combined study of three-dimensional quantitative structure-activity relationship, molecular docking and molecular dynamics (MD) was undertaken to explore the structural insights of 174 2-thioimidazole compounds influencing the p38α inhibitory activities. Both the ligand-based resultant comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) models exhibited satisfactory predictability (with Q(2)=0.475, R(2)(ncv)=0.774, R(2)(pre)=0.668 and Q(2)=0.504, R(2)(ncv)=0.745, R(2)(pre)=0.709, respectively). Furthermore, good consistency was observed between the 3D-QSAR models, docking and MD results. Our findings are: i) hydrogen bonding and steric size of the molecules play crucial roles in the mechanisms of action that a medium-sized bulky substituent on the 2-position, an electropositive H-bond donor substituent on the 6-position of the pyridine ring are favorable for increasing the inhibition activity; ii) 2- Thioimidazole derivatives may bind to the p38α kinase with a "lobster" active conformation, which is fixed by four hydrogen bonds they formed with the adjacent residues (Lys53, Gly110, Met109 and Ala157) and two hydrophobic interactions (in hydrophobic pockets I and II respectively) in p38α binding site. These models and the derived information may afford valuable clues for design of new potent p38α inhibitors.     

3D-QSAR and molecular docking studies on 3-anilino-4-arylmaleimide derivatives as glycogen synthase kinase-3β inhibitors

Glycogen synthase kinase-3, a serine/threonine kinase, is a fascinating enzyme with diverse biological actions in intracellular signaling systems, making it an emerging target for diseases such as diabetes mellitus, cancer, chronic inflammation, bipolar disorders, and Alzheimer's disease. It is important to inhibit glycogen synthase kinase-3 selectively, and the net effect of the glycogen synthase kinase-3 inhibitors thus should be target specific, over other phylogenetically related kinases such as CDK-2. In the present work, we have carried out three-dimensional quantitative structure-activity relationship studies on novel class of 3-anilino-4-arylmaleimide derivatives to have improved cellular activity. Docked conformation of the most active molecule in the series, which shows desirable interactions in the receptor, was taken as template for the alignment of the molecules. Statistically significant CoMSIA (r2(cv)=0.614, r2(ncv)=0.948) and comparative molecular field analysis (r2(cv) =0.652, r2(ncv)=0.958) models were generated using 57 molecules in training set. The predictive ability of CoMSIA and comparative molecular field analysis models was determined using a test set of 17 molecules, which gave predictive correlation coefficients (r2(pred)) of 0.87 and 0.82, respectively, indicating good predictive power. Based on the information derived from CoMSIA and comparative molecular field analysis contour maps, we have identified some key features that explain the observed variance in the activity and have been used to design new anilinoarylmaleimide derivatives. The designed molecules showed better binding affinity in terms of estimated docking scores with respect to the already reported systems, hence suggesting that newly designed molecules can be more potent and selective toward glycogen synthase kinase-3β inhibition.     

2-苯氧茚酮类乙酰胆碱酯酶抑制剂的3D-QSAR研究

目的研究2-苯氧茚酮类乙酰胆碱酯酶抑制剂的三维定量构效关系。方法采用比较分子力场分析法(CoMFA)和比较分子相似性指数分析法(CoMSIA)对结构与活性的关系进行研究。结果CoMFA模型表明立体场和静电场对活性的贡献分别为0.805和0.195;CoMSIA模型阐明疏水场和氢键场对活性也有一定的影响。结论两种3D-QSAR模型都显示出相当高的预测能力,CoMFA和CoMSIA的交叉验证值q2分别为0.881和0.918,通过对两种3D-QSAR模型等势图的分析,可为开展进一步的药物设计和结构优化提供理论指导和依据。     

Multitargeted molecular docking study of plant-derived natural products on phosphoinositide-3 kinase pathway components

Phosphoinositide-3 kinase (PI3K) signaling pathway comprises of a cornucopia of protein molecules capable of regulating numerous cellular events, including cell survival, cell cycle regulation, angiogenesis, and apoptosis. Deregulation of PI3K downstream signaling is a phenomenon commonly seen in various types of cancer and also held responsible for poor prognosis and resistance to chemotherapy. Targeting PI3K signaling pathway has become a new and promising strategy in combating cancer. In the present study, PI3K signaling components PI3K, PDK1, Akt, and mTOR were chosen and 51 natural compounds along with 17 reference compounds were selected as ligand with the aid of PubMed published literature search. Ligands were docked to protein molecules by using Maestro 9.3 (Schrödinger Inc.). It was discovered in this study that compounds myricetin, quercetin, morin, luteolin, and emodin yielded excellent dock score with the proteins concluded with the help of docking free energy. The remarkable feature of these compounds are their various pharmacodynamics and pharmacokinetic characteristics, many of which are in accordance with the “Lipinski’s Rule of five”. The docking study carried out is an endeavor to portray the docking of these compounds with the proteins, to summarize the various Gscore, hydrogen bond, electrostatic bond, and to chart out various factors that are decisive for and also govern the protein–ligand interactions.     

An in silico approach to discover the best molecular modeling strategy for designing novel CDK4 inhibitors

Designing of new potential specific inhibitors for CDK4 is very important in cancer therapy. The imperative features that can inhibit CDK4 were identified and refined by docking under standard precision mode, extra precision mode, extra precision mode with core constraints, and induced fit using extra precision protocol. Statistically significant correlation was observed when docking experiments in the extra precision mode carried out by applying core constraints in the ligand molecule which forms two specific hydrogen bonds with Val 96 of CDK4. Importance of solvent molecule was understood by molecular dynamic simulation studies. The presence of a water molecule which forms a hydrogen bond with Asp158 increased the binding affinity between the ligand and CDK4. Hydrogen bonding interactions, pi–pi stacking interactions, and salt bridge formation were the key interactions contributing to the increased inhibitory value. All these findings were supported by the high correlation between pIC50 and glide emodel values. Binding energy determinations under various conditions also support these findings. Thus, extra precision docking with ligand constraints through the insertion of water molecule effectively classified the actives and inactives of given set of molecules and can be used for virtual screening of new molecules for promising biological activity.     

Docking and three-dimensional quantitative structure-activity relationship (3D QSAR) analyses of nonsteroidal progesterone receptor ligands

We report a docking and comparative molecular similarity indices analysis (CoMSIA) study of progesterone receptor (PR) ligands with an emphasis on nonsteroids including tanaproget. The ligand alignment generation, a critical part of model building, comprised two stages. First, thorough conformational sampling of docking poses within the PR binding pocket was made with the program GOLD. Second, a strategy to select representative poses for CoMSIA was developed utilizing the FlexX scoring function. After manual replacement of five poses where this approach had problems, a significant correlation (r(2) = 0.878) between the experimental affinities and electrostatic, hydrophobic, and hydrogen bond donor properties of the aligned ligands was found. Extensive model validation was made using random-group cross-validations, external test set predictions (r(pred)(2) = 0.833), and consistency check between the CoMSIA model and the PR binding site structure. Robustness, predictive ability, and automated alignment generation make the model a potential tool for virtual screening.     

3D-QSAR and docking studies of piperidine carboxamide derivatives as ALK inhibitors

Anaplastic lymphoma kinase (ALK) is an important and attractive target for the design of new anticancer drugs. In the present study, quantitative structure–activity relationship (QSAR) models of piperidine carboxamide derivatives against ALK were developed by CoMFA and CoMSIA approaches. Both the CoMFA and CoMSIA models yielded significant statistical results. The results of the QSAR model indicated the importance of steric, electrostatic, and hydrophobic properties in the potent ALK inhibitors. Furthermore, molecular docking of the most active compound 25 with the active site of ALK was also investigated. The outcomes of this study may result in a better understanding of the inhibition mechanism of ALK and aided in the development of new and more potent anticancer drugs.     

Docking and quantitative structure–activity relationship studies for imidazo[1,2-a]pyrazines as inhibitors of checkpoint kinase-1

We have performed docking of imidazo[1,2-a]pyrazines complexed with checkpoint kinase1 (Chk1) to better understand the structural requirements and preferred conformations of these inhibitors. The study was performed on a selected set of 33 compounds with variation in structure and activity. In addition, the predicted inhibitor concentrations (IC₅₀) of the imidazo[1,2-a]pyrazines as Chk1 inhibitors were obtained by comparative molecular similarity analysis (CoMSIA). The best CoMSIA model included electrostatic and hydrophobic fields, had a good Q ² value of 0.589, and adequately predicted the compounds contained in the test set. Furthermore, plots of the CoMSIA fields allowed conclusions to be drawn for the selection of suitable inhibitors.     

Ligand-based virtual screening and molecular docking studies to identify the critical chemical features of potent cathepsin D inhibitors

Cathepsin D is a major component of lysosomes and plays a major role in catabolism and degenerative diseases. The quantitative structure-activity relationship study was used to explore the critical chemical features of cathepsin D inhibitors. Top 10 hypotheses were built based on 36 known cathepsin D inhibitors using HypoGen/Discovery Studio v2.5. The best hypothesis Hypo1 consists of three hydrophobic, one hydrogen bond acceptor lipid, and one hydrogen bond acceptor features. The selected Hypo1 model was cross-validated using Fischer's randomization method to identify the strong correlation between experimental and predicted activity value as well as the test set and decoy sets used to validate its predictability. Moreover, the best hypothesis was used as a 3D query in virtual screening of Scaffold database. Subsequently, the screened hit molecules were filtered by applying Lipinski's rule of five, absorption, distribution, metabolism, and toxicity, and molecular docking studies. Finally, 49 compounds were obtained as potent cathepsin D inhibitors based on the consensus scoring values, critical interactions with protein active site residues, and predicted activity values. Thus, we suggest that the application of Hypo1 could assist in the selection of potent cathepsin D leads from various databases. Hence, this model was used as a valuable tool to design new candidate for cathepsin D inhibitors.     

Comparative molecular similarity indices analysis for predicting the antihyperglycemic activity of thioglitazones

The three-dimensional (3D) quantitative structure-activity relationship method, comparative molecular similarity indices analysis (CoMSIA), was applied to a series of thioglitazones (or thiazolidinediones) to derive structure-activity relationships of their antihyperglycemic activity. Evaluation of 39 aligned compounds from the training set served to establish the various models. Predictions were obtained with CoMSIA steric, electrostatic, hydrophobic, and hydrogen bond acceptor and donor fields (q ² = 0.51, r ² = 0.942). The developed model was validated by an external test set of 11 compounds, with the satisfactory predictive r ² value of 0.849. The information obtained from the present CoMSIA 3D contour map can be utilized for the design of specific PPAR-γ agonists.