Comparative Molecular Similarity Indices Analysis Models Research Articles

Novel therapeutic agents for H5N1 influenza virus through 3D-QSAR, molecular docking, ADMET prediction, and DFT based global reactivity descriptors

Avian influenza is a severe respiratory disease that can cause catastrophic outbreaks in domestic poultry and wild birds as well as significant risks to people. This has motivated many researchers to develop new, effective neuraminidase (NA) inhibitors to treat this serious infection. In this context, this study aims to develop new potential NA inhibitors using five computational methods. A three-dimensional quantitative structure-activity relationship (3D-QSAR) comparative molecular similarity indices analysis (CoMSIA) was performed on a set of N-substituted Oseltamivir derivatives as anti-influenza agents. As a result, the best CoMSIA model was robust and predictive (R2 = 0.966, Q2 = 0.772, and Rpred2 = 0.721). Based on the contour map analysis, 17 new NA inhibitors with high-predicted inhibitory activity were developed. Molecular docking was used to discover the binding modes and interactions between the 17 newly designed NA compounds and the corresponding NA protein. Based on the absorption, distribution, metabolism, elimination, and toxicity (ADMET) properties, the compounds C10, C11, C12, C15, C16, and C17 have good drug-likeness and pharmacokinetics properties and could be new promising anti-influenza drugs. The six leading compounds further went through biological activity spectra prediction and quantum method density functional theory (DFT) study, which confirmed the trends and the utility of 3D-QSAR CoMSIA and molecular docking in developing new NA inhibitors.

2D/3D-QSAR Model Development Based on a Quinoline Pharmacophoric Core for the Inhibition of Plasmodium falciparum: An In Silico Approach with Experimental Validation.

Malaria is an infectious disease caused by Plasmodium spp. parasites, with widespread drug resistance to most antimalarial drugs. We report the development of two 3D-QSAR models based on comparative molecular field analysis (CoMFA), comparative molecular similarity index analysis (CoMSIA), and a 2D-QSAR model, using a database of 349 compounds with activity against the P. falciparum 3D7 strain. The models were validated internally and externally, complying with all metrics (q2 > 0.5, r2test > 0.6, r2m > 0.5, etc.). The final models have shown the following statistical values: r2test CoMFA = 0.878, r2test CoMSIA = 0.876, and r2test 2D-QSAR = 0.845. The models were experimentally tested through the synthesis and biological evaluation of ten quinoline derivatives against P. falciparum 3D7. The CoMSIA and 2D-QSAR models outperformed CoMFA in terms of better predictive capacity (MAE = 0.7006, 0.4849, and 1.2803, respectively). The physicochemical and pharmacokinetic properties of three selected quinoline derivatives were similar to chloroquine. Finally, the compounds showed low cytotoxicity (IC50 > 100 µM) on human HepG2 cells. These results suggest that the QSAR models accurately predict the toxicological profile, correlating well with experimental in vivo data.

Design of Biphenyl‐Type Programmed Cell Death‐Ligand 1 Inhibitors Using 3D‐QSAR, Molecular Docking, and Molecular Dynamics Simulation

AbstractBlocking the interaction between programmed cell death‐1 (PD‐1) and programmed cell death‐ligand 1 (PD‐L1) is a crucial immunotherapeutic strategy for cancer. Currently, all inhibitors available on the market that target PD‐1/PD‐L1 are monoclonal antibodies, with no small molecule drugs yet accessible. This study focused on PD‐L1 and conducted three‐dimensional quantitative structure‐activity relationship (3D‐QSAR) research on 39 PD‐L1 inhibitors using Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA). The CoMFA (q2=0.593, r2=0.991) and CoMSIA (q2=0.542, r2=0.971) models were successfully established. Based on these models, a series of structurally novel and potentially active PD‐L1 inhibitors (37 a–37 e) were designed. Among these compounds, 37 c exhibited superior performance, with CoMFA predicting a pIC50 value of 9.204 and CoMSIA predicting a pIC50 value of 8.597. Further molecular docking and molecular dynamics simulations revealed that compound 37 c establishes hydrophobic interactions with BTyr56 and BVal68 of PD‐L1, engages in hydrogen bonds with AAsp122, ALys124, and BTyr123, and forms electrostatic interactions with ALys124 and AMet115. The design of this biphenyl series of inhibitors offers additional options for the development of small molecule inhibitors targeting PD‐L1, with 37 c expected to be a promising inhibitor of PD‐L1.

Modelling of novel bornoel analogs as Influenza A Virus inhibitors through genetic function approximation, comparative molecular fields, molecular docking, and ADMET/Pharmacokinetic studies

Influenza A Virus (IAV) is a human respiratory pathogen prone to mutations and genome re-assortment leading to global pandemics. In this study, we applied the molecular modelling strategies such as, two-dimensional (2D), three-dimensional (3D)-quantitative structure–activity relationship (QSAR), and molecular docking simulation on a novel series of borneol compounds as influenza inhibitors. The best developed 2D-QSAR models, MLR (Q2 ​= ​0.8735, R2(train) ​= ​0.9096) and ANN [3-2-1] (Q2 ​= ​0.8987, R2(train) ​= ​0.9171) revealed good and acceptable statistical validation metrics for the inhibitory activity predictions. The 3D-QSAR models were generated using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA), which showed CoMFA_S ​+ ​E (Q2 ​= ​0.559, R2(train) ​= ​0.939) and CoMSIA_S ​+ ​E (Q2 ​= ​0.577, R2(train) ​= ​0.941) as the best-observed models in accordance with the model acceptability standards. In addition, the contour maps generated from the CoMFA and CoMSIA models illustrates the steric and electrostatic molecular field relationships with the inhibitory effects of the studied molecules. Moreover, the binding modes of the active ligands were studied through molecular docking simulation with the Human Hemagglutinin (HA) receptor of influenza A virus (A/Puerto Rico/8/34(H1N1)). The studied compounds revealed the formation of H-bonds, CH-bonds, and hydrophobic interactions with the active amino acid residues such as Asn 543, Asn 614, Asn 617, Leu 618, Ser 540, Lys 539, and Lys 621 in the HA binding cavity. The prediction of drug-likeness and ADMET properties of the compounds revealed their good bioavailability and pharmacokinetic profiling. This study may provide a valuable in-silico guideline for discovering novel potent influenza inhibitors.

Structural optimization of pyrrolopyrimidine BTK inhibitors based on molecular simulation.

BTK is a critical regulator involved in the proliferation, differentiation, and apoptosis of B cells. BTK inhibitors can effectively alleviate various diseases such as tumors, leukemia, and asthma. During this study, a range of novel BTK inhibitors were designed using 3D-QSAR, molecular docking, and molecular dynamics (MD) simulation. We selected 41 pyrrolopyrimidine derivatives as BTK inhibitors to structure a 3D-QSAR model. Comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) were adopted to research the connection between the pharmacological activities and chemical structures of the compounds. The CoMFA model (q2= 0.519,R2= 0.971), CoMSIA model (q2= 0.512,R2= 0.990), and external validation demonstrated excellent predictive performance and reliability of the 3D-QSAR model. We designed eight novel molecules with higher inhibitory activities according to the three-dimensional equipotential fields and explored the interactions between the compounds and BTK by molecular docking, which showed that the novel molecules had higher binding affinities with BTK than the template molecule 18. Then, the results of molecular docking were further verified by MD simulation, which showed that amino acid residues such as Leu528, Val416, and Met477 played vital parts in the interaction, and the binding free energy analysis showed that the novel molecules had higher stability with BTK. Finally, the ADME/T properties were predicted for all of the novel compounds, and the results showed that the majority of them had favorable pharmacokinetic properties. Therefore, this study provides strong support for the development of novel BTK inhibitors.

Convergent QSAR Models for the Prediction of Cruzain Inhibitors.

Chagas disease is a parasitosis caused by Trypanosoma cruzi. Cruzain, the major cysteine protease from T. cruzi, is an excellent therapeutic target in the search for antichagasic drugs. It is important in the role of cell invasion, replication, differentiation, and metabolism of the parasite. In this work, we developed and assessed multiple quantitative structure-activity relationship (QSAR) models for a set of 61 cruzain inhibitors. These models include two-dimensional (2D) QSAR, three-dimensional (3D) QSAR, such as comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA), and Hologram QSAR (HQSAR). In total, we generated 10 major and 114 minor model variations. Molecular docking was used to successfully align the molecules. All CoMFA and CoMSIA models, which incorporate multiple fields, demonstrated robustness in our analysis. Steric fields exhibited satisfactory convergence in the contour maps, while the electrostatic field converged into a single small region. The HQSAR model taking into consideration only Atoms and Connectivity, with fragment sizes ranging from two to five atoms, was considered the best of the HQSAR variations, despite exhibiting a higher level of deviance. In total, 78 model variations meet the minimum requirements to be considered acceptable. We found that using as few as five descriptors it is possible to obtain robust results with 2D-QSAR. Models such as Random Forest, Tree Ensemble, Linear Regression, and HQSAR are recommended for working with large data sets, while the 3D-QSAR models are intended to study the geometry of the ligands, to optimize them into new and better performing antichagasics. Virtual Screening of a set of hydrazones, guided by the top-performing models, identified promising candidates for experimental validation. Among them, dv007 and dv015 exhibited consistently high predicted pIC50 values (7.26 and 7.24, respectively), making them compelling candidates for further drug development.

3D-QSAR and Molecular Docking Studies of Novel GPR52 Agonists

To study the three-dimensional quantitative structure–activity relationship (3D-QSAR) of a series of GPR52 agonists, comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) were used to establish 3D-QSAR models. The cross-validation coefficient ([Formula: see text]), non-cross validation coefficient ([Formula: see text]) and standard error of estimate (SEE) of the constructed CoMFA model were 0.679, 0.981 and 0.058, respectively. While those for the CoMSIA model were 0.661, 0.846 and 0.110, respectively. The internal and external validation parameters showed that the constructed 3D-QSAR models had good prediction ability and significant statistical reliability. According to the information provided by 3D-QSAR analysis, new compounds were designed, and the molecular docking together with absorption, distribution, metabolism, exclusion, toxicity (ADMET) prediction results showed that the new compounds had good GPR52 agonistic activity.

3D-QSAR, homology modelling of influenza hemagglutinin receptor (StrainA/WS/1933), molecular dynamics, DFT, and ADMET studies for newly designed inhibitors

Influenza pandemic cases are related to high morbidity and mortality rates due to the genetic variability of the influenza strains. This necessitated the need for the search and discovery of more potential anti-influenza agents to avert future outbreaks. The 3D-QSAR studies were built through comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The results showed CoMFA (Rtrain2 = 0.98, Q2 = 0.58) and CoMSIA (Rtrain2 0.96, Q2 = 0.54) models for reliable activity predictions. Using the worthy information obtained from the field contributors of the 3D-QSAR models and the molecular docking studies of the most active compound 4 (template 4), nine (9) compounds were designed (4a-i) with better potency as compared with a reference drug (arbidol). The dynamic stability of the bound ligand (4d) in the binding site of the modelled protein was further justified through molecular dynamics simulations for up to 100 ns. Moreover, the quantum chemical and drug-likeness parameters of the designed compounds predicted good chemical reactivity and pharmacokinetic profiles respectively. Hence, the outcome of this study recommends the development and bioassay testing of these newly designed compounds through in vitro and in-vivo analysis.

In silico approaches to develop new phenyl-pyrimidines as glycogen synthase kinase 3 (GSK-3) inhibitors with halogen-bonding capabilities: 3D-QSAR CoMFA/CoMSIA, molecular docking and molecular dynamics studies

Glycogen synthase kinase 3 (GSK-3) is involved in different diseases, such as manic-depressive illness, Alzheimer’s disease and cancer. Studies have shown that insulin inhibits GSK-3 to keep glycogen synthase active. Inhibiting GSK-3 may have an indirect pro-insulin effect by favouring glycogen synthesis. Therefore, the development of GSK-3 inhibitors can be a useful alternative for the treatment of type II diabetes. Aminopyrimidine derivatives already proved to be interesting GSK-3 inhibitors. In the current study, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) have been performed on a series of 122 aminopyrimidine derivatives in order to generate a robust model for the rational design of new compounds with promising antidiabetic activity. The q 2 values obtained for the best CoMFA and CoMSIA models have been 0.563 and 0.598, respectively. In addition, the r 2 values have been 0.823 and 0.925 for CoMFA and CoMSIA, respectively. The models were statistically validated, and from the contour maps analysis, a proposal of 10 new compounds has been generated, with predicted pIC50 higher than 9. The final contribution of our work is that: (a) we provide an extensive structure–activity relationship for GSK-3 inhibitory pyrimidines; and (b) these models may speed up the discovery of GSK-3 inhibitors based on the aminopyrimidine scaffold. Finally, we carried out docking and molecular dynamics studies of the two best candidates, which were shown to establish halogen-bond interactions with the enzyme. Communicated by Ramaswamy H. Sarma

In silico studies of a novel scaffold of benzoxazole derivatives as anticancer agents by 3D-QSAR, molecular docking and molecular dynamics simulations.

The vascular endothelial growth factor receptor-2 kinases (VEGFR-2) expressed on tumor cells and vessels are attractive targets for cancer treatment. Potent inhibitors for the VEGFR-2 receptor are novel strategies to develop anti-cancer drugs. In this work, template ligand-based 3D-QSAR studies were performed on a series of benzoxazole derivatives toward different cell lines (HepG2, HCT-116 and MCF-7). Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques were used to generate 3D-QSAR models. Good predictability was derived for the optimal CoMFA models (HepG2: Rcv2 = 0.509, Rpred2 = 0.5128; HCT-116: Rcv2 = 0.574, Rpred2 = 0.5597; MCF-7: Rcv2 = 0.568, Rpred2 = 0.5057) and CoMSIA models (HepG2: Rcv2 = 0.711, Rpred2 = 0.6198; HCT-116: Rcv2 = 0.531, Rpred2 = 0.5804; MCF-7: Rcv2 = 0.669, Rpred2 = 0.6577). In addition, the contour maps derived from CoMFA and CoMSIA models were also generated to illustrate the relationship between different fields and the inhibitory activities. Moreover, molecular docking and molecular dynamics (MD) simulations were also conducted to understand the binding modes and the potential interactions between the receptor and the inhibitors. Some key residues (Leu35, Val43, Lys63, Leu84, Gly117, Leu180 and Asp191) were pointed out for stabilizing the inhibitors in the binding pocket. The binding free energies for the inhibitors agreed well with the experimental inhibitory activity and indicated that steric, electrostatic and hydrogen bond interactions are the main driving force for inhibitor-receptor binding. Overall, a good consistency between theoretical 3D-SQAR and molecular docking and MD simulation studies would provide directions for the design of new candidates, avoiding time-consuming and costly synthesis and biological evaluations. On the whole, the results derived from this study could expand the understanding of benzoxazole derivatives as anticancer agents and would be of great help in lead optimization for early drug discovery of highly potent anticancer activity targeting VEGFR-2.

Integration of machine learning in 3D-QSAR CoMSIA models for the identification of lipid antioxidant peptides.

The comparative molecular similarity indices analysis (CoMSIA) method is a widely used 3D-quantitative structure-activity relationship (QSAR) approach in the field of medicinal chemistry and drug design. However, relying solely on the Partial Least Square algorithm to build models using numerous CoMSIA indices has, in some cases, led to statistically underperforming models. This issue has also affected 3D-CoMSIA models constructed for the ferric thiocyanate (FTC) dataset from linoleic antioxidant measurements. In this study, a novel modeling routine has been developed incorporating various machine learning (ML) techniques to explore different options for feature selection, model fitting, and tuning algorithms with the ultimate goal of arriving at optimal 3D-CoMSIA models with high predictivity for the FTC activity. Recursive Feature Selection and SelectFromModel techniques were applied for feature selection, resulting in a significant improvement in model fitting and predictivity (R2, RCV2, and R2_test) of 24 estimators. However, these selection methods did not fully address the problem of overfitting and, in some instances, even exacerbated it. On the other hand, hyperparameter tuning for tree-based models resulted in dissimilar levels of model generalization for four tree-based models. GB-RFE coupled with GBR (hyperparameters: learning_rate = 0.01, max_depth = 2, n_estimators = 500, subsample = 0.5) was the only combination that effectively mitigated overfitting and demonstrated superior performance (RCV2 of 0.690, R2_test of 0.759, and R2 of 0.872) compared to the best linear model, PLS (with RCV2 of 0.653, R2_test of 0.575, and R2 of 0.755). Therefore, it was subsequently utilized to screen potential antioxidants among a range of Tryptophyllin L tripeptide fragments, leading to the synthesis and testing of three peptides: F-P-5Htp, F-P-W, and P-5Htp-L. These peptides exhibited promising activity levels, with FTC values of 4.2 ± 0.12, 4.4 ± 0.11, and 1.72 ± 0.15, respectively.

Combined computational approaches for developing new anti-Alzheimer drug candidates: 3D-QSAR, molecular docking and molecular dynamics studies of liquiritigenin derivatives

Butyrylcholinesterase is an acetylcholine-degrading enzyme involved in the memorization process, which is becoming an interesting target for the symptomatic treatment of Alzheimer's disease. In the present investigation, the structure–activity relationship of a set of Liquiritigenin derivatives recently revealed to be Butyrylcholinesterase inhibitors was studied basing on comparative field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMISA). As a result, performant models with high predictive capability have been developed (CoMFA model: R2 = 0.91, Q2 = 0.62, R2pred = 0.85; CoMISA model: R2 = 0.92, Q2 = 0.59, R2pred = 0.83) and implemented to design new Liquiritigenin derivatives with improved activity. Besides, the affinity of the designed derivatives towards the active site of Butyrylcholinesterase, was confirmed by molecular docking and molecular dynamics studies. Moreover, they exhibited good pharmacokinetics properties. Accordingly, the outcomes of the present investigations can provide important direction for the development of new anti-Alzheimer's drug candidates.

Combined 3D-QSAR, molecular docking and dynamics simulations studies to model and design TTK inhibitors.

Tyrosine threonine kinase (TTK) is the key component of the spindle assembly checkpoint (SAC) that ensures correct attachment of chromosomes to the mitotic spindle and thereby their precise segregation into daughter cells by phosphorylating specific substrate proteins. The overexpression of TTK has been associated with various human malignancies, including breast, colorectal and thyroid carcinomas. TTK has been validated as a target for drug development, and several TTK inhibitors have been discovered. In this study, ligand and structure-based alignment as well as various partial charge models were used to perform 3D-QSAR modelling on 1H-Pyrrolo[3,2-c] pyridine core containing reported inhibitors of TTK protein using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) approaches to design better active compounds. Different statistical methods i.e., correlation coefficient of non-cross validation (r2), correlation coefficient of leave-one-out cross-validation (q2), Fisher's test (F) and bootstrapping were used to validate the developed models. Out of several charge models and alignment-based approaches, Merck Molecular Force Field (MMFF94) charges using structure-based alignment yielded highly predictive CoMFA (q2 = 0.583, Predr2 = 0.751) and CoMSIA (q2 = 0.690, Predr2 = 0.767) models. The models exhibited that electrostatic, steric, HBA, HBD, and hydrophobic fields play a key role in structure activity relationship of these compounds. Using the contour maps information of the best predictive model, new compounds were designed and docked at the TTK active site to predict their plausible binding modes. The structural stability of the TTK complexes with new compounds was confirmed using MD simulations. The simulation studies revealed that all compounds formed stable complexes. Similarly, MM/PBSA method based free energy calculations showed that these compounds bind with reasonably good affinity to the TTK protein. Overall molecular modelling results suggest that newly designed compounds can act as lead compounds for the optimization of TTK inhibitors.

Design of novel quinoline derivatives as antibreast cancer using 3D-QSAR, molecular docking and pharmacokinetic investigation.

Breast cancer has been one of the most challenging women's cancers and leading cause of mortality for decades. There are several studies being conducted all the time to find a cure for breast cancer. Quinoline derivatives have shown their potential as antitumor agents in breast cancer therapy. In this work, three-dimensional quantitative structure-activity relationships (3D-QSAR) and molecular docking with aromatase enzyme (Protein Data Bank: 3S7S) studies were performed to suggest the current scenario of quinoline derivatives as antitumor agents and to refine the path of these derivatives to discover and develop new drugs against breast cancer. For developing the 3D-QSAR model, comparative molecular similarity indices analysis (CoMSIA) and comparative molecular field analysis (CoMFA) were included. To attain the high level of predictability, the best CoMSIA model was applied. External validation utilizing a test set has been used in order to validate the predictive capabilities of the built model. According to the findings, electrostatic, hydrophobic and hydrogen bond donor, and acceptor fields had a significant impact on antibreast cancer activity. Thus, we generated a variety of novel effective aromatase inhibitors based on prior findings and we predicted their inhibitory activity using the built model. In addition, absorption, distribution, metabolism, elimination and toxicity properties were employed to explore the effectiveness of new drug candidates.

Research on the Anti-tumor Activity of a Novel Aminopeptidase Inhibitor Based on 3D QSAR Model

Background: Aminopeptidase N (APN) is a type II transmembrane zinc ion-dependent metalloprotease. It is closely related to many processes of tumor occurrence and development, such as the formation of new blood vessels and tumor metastasis. Recent studies have shown that APN is a member of the family of surface markers of liver cancer stem cells. Therefore, APN small molecule inhibitors may have multiple compound functions, exerting multiple anti-tumor effects at multiple stages of cancer occurrence and development. Methods: Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) approaches were employed in the study. Results: Both internal and external cross-validations were conducted to obtain high predictive and satisfactory CoMFA model (q2 = 0.627, r2 = 0.995, SEE = 0.043) and CoMSIA model (q2 = 0.575, r2 = 0.998, SEE = 0.031) values. The statistical results obtained from CoMFA and CoMSIA models were found to be credible and having remarkable predictive power. Conclusion: The results of 3D-QSAR are reliable and significant with high predictive (q2) ability, and a lower value of the standard error of estimation indicates a good correlation between predicted and observed activity. All these results have revealed many useful structural insights to improve the activity of the newly designed APN small molecule inhibitors.

3D-QSAR, molecular docking and ADMET studies of thioquinazolinone derivatives against breast cancer

Breast cancer is a deadly disease and the second largest cause of mortality on a worldwide platform. Despite the availability of several cancer treatments, life expectancies stay relatively poor. Consequently, the medicinal chemistry community prioritizes the quick discovery of novel anticancer drugs. In recent years, computational approaches have been widely used to accelerate the drug development process. In light of this, in the current work, we performed three-dimensional quantitative structure-activity relationships (3D-QSAR) and molecular docking analyses on thioquinazolinone derivatives with aromatase enzyme (PDB: 3S7S). External validation was used to validate the prediction capabilities of the generated model. The best CoMSIA (comparative molecular similarity indices analysis) model exhibited the significant values of Q2, R2andRpred2. These findings suggested that the electrostatic, hydrophobic and hydrogen bond donor and acceptor fields have a significant effect on inhibition of breast cancer. Thus, a number of innovative potent aromatase inhibitors were designed and their biological activities were predicted based on the best model. Furthermore, molecular docking studies were carried out for the designed compounds against breast cancer. Additionally, ADMET proprieties were used to evaluate drug-likeness of these novel drug candidates. The most active compounds found by these computational studies could be helpful for synthesis and testing as prospective future anti-cancer treatments.

3D-QSAR and Docking Studies on Pyrimidine Derivatives of Second-Generation ALK Inhibitors

AbstractAnaplastic lymphoma kinase (ALK) is a promising target for the treatment of non-small cell lung cancer. Under crizotinib treatment, drug resistance and progressive disease appeared after the point mutations arising in the kinase domain of ALK. Second-generation ALK inhibitors can solve the deficiencies of the first generation, especially the drug resistance in cancer chemotherapy. Ceritinib (LDK378), a pyrimidine derivative, for example, can inhibit the activity of ALK with an IC50 value of 40.7 nmol/L, and can experience disease progression after initial treatment with crizotinib. Unfortunately, clear structure–activity relationships have not been identified to date, impeding the rational design of future compounds possessing ALK inhibition activity. To explore interesting insights into the structures of pyrimidine derivatives that influence the activities of the second-generation ALK inhibitors, three-dimensional quantitative structure–activity relationship (3D-QSAR) and molecular docking were performed on a total of 45 derivatives of pyrimidine. Comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) techniques were used to generate 3D-QSAR models. CoMFA and CoMSIA were performed using the Sybyl X 2.0 package. Molecular docking analysis was performed using the Surflex-Dock module in SYBYL-X 2.0 package. We found in the CoMFA model that the non-cross-validated r2 value was 0.998, the cross-validated q 2 value was 0.663, and the F statistic value was 2,401.970, while the r2 value was 0.988; q 2 value was 0.730, and F value was 542.933 in CoMSIA models, suggesting the good predictability of the CoMFA and CoMSIA models. 3D contour maps and docking results suggested that different groups on the core parts of the compounds could enhance the biological activities. Based on these results, the established 3D-QSAR models and the binding structures of ALK inhibitors obtained favor the prediction of the activity of new inhibitors and will be helpful in the reasonable design of ALK inhibitors in the future.

Insight into the structural requirements of antimicrobial peptides by multiple validated 3D-QSAR approaches

ABSTRACT Antibacterial peptides (AMPs) are a kind of peptide material produced by the organism to resist the invasion of exogenous pathogens. Several antimicrobial peptides in different structures have been reported. To elucidate the relationship between structure and activity. A molecular modelling study on the nonapeptides using three-dimensional quantitative structure–activity relationship (3D-QSAR) was carried out. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods were employed for the identification of significant features of antimicrobial peptides. CoMFA and CoMSIA models all gave best parametric results ( = 0.563, = 0.654 for CoMFA; = 0.656, = 0.8045 for CoMSIA), which suggested that the derived models are highly robust and predictive. In addition, the derived contour maps would provide structural features to improve binding activity, and indicated that steric, electrostatic, and hydrophobic fields play vital roles in improving the activity. The derived models and information may help to provide an insight into the structural requirements for more potent peptides and facilitate the design and optimisation of novel antimicrobial peptides with promising enhanced activity. Furthermore, some potential candidate antimicrobial peptides have been proposed.

3D-QSAR, ADME-Tox In Silico Prediction and Molecular Docking Studies for Modeling the Analgesic Activity against Neuropathic Pain of Novel NR2B-Selective NMDA Receptor Antagonists

A new class of selective antagonists of the N-Methyl-D-Aspartate (NMDA) receptor subunit 2B have been developed using molecular modeling techniques. The three-dimensional quantitative structure–activity relationship (3D-QSAR) study, based on comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) models, indicate that steric, electrostatic and hydrogen bond acceptor fields have a key function in the analgesic activity against neuropathic pain. The predictive accuracy of the developed CoMFA model (Q2 = 0.540, R2 = 0.980, R2 pred = 0.613) and the best CoMSIA model (Q2 = 0.665, R2 = 0.916, R2 pred = 0.701) has been successfully examined through external and internal validation. Based on ADMET in silico properties, L1, L2 and L3 ligands are non-toxic inhibitors of 1A2, 2C19 and 2C9 cytochromes, predicted to passively cross the blood–brain barrier (BBB) and have the highest probability to penetrate the central nervous system (CNS). Molecular docking results indicate that the active ligands (L1, L2 and L3) interact specifically with Phe176, Glu235, Glu236, Gln110, Asp136 and Glu178 amino acids of the transport protein encoded as 3QEL. Therefore, they could be used as analgesic drugs for the treatment of neuropathic pain.

Binding Studies and Lead Generation of Pteridin-7(8H)-one Derivatives Targeting FLT3.

Ligand modification by substituting chemical groups within the binding pocket is a popular strategy for kinase drug development. In this study, a series of pteridin-7(8H)-one derivatives targeting wild-type FMS-like tyrosine kinase-3 (FLT3) and its D835Y mutant (FL3D835Y) were studied using a combination of molecular modeling techniques, such as docking, molecular dynamics (MD), binding energy calculation, and three-dimensional quantitative structure-activity relationship (3D-QSAR) studies. We determined the protein–ligand binding affinity by employing molecular mechanics Poisson–Boltzmann/generalized Born surface area (MM-PB/GBSA), fast pulling ligand (FPL) simulation, linear interaction energy (LIE), umbrella sampling (US), and free energy perturbation (FEP) scoring functions. The structure–activity relationship (SAR) study was conducted using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA), and the results were emphasized as a SAR scheme. In both the CoMFA and CoMSIA models, satisfactory correlation statistics were obtained between the observed and predicted inhibitory activity. The MD and SAR models were co-utilized to design several new compounds, and their inhibitory activities were anticipated using the CoMSIA model. The designed compounds with higher predicted pIC50 values than the most active compound were carried out for binding free energy evaluation to wild-type and mutant receptors using MM-PB/GBSA, LIE, and FEP methods.