Comparative Molecular Field Analysis Model Research Articles

Structural Optimization of Quinazolin-4-One Derivatives as Novel SARS-CoV-2 Mpro Inhibitors by Molecular Simulation

Background: Severe acute respiratory syndrome coronavirus 2 main protease (SARSCoV- 2 Mpro) has been shown to be an effective target for inhibiting novel coronaviruses, which can be used as a crucial breakthrough for developing drugs to treat the coronavirus disease 2019 (COVID-19). Methods: To design novel SARS-CoV-2 Mpro inhibitors, we conducted 3D-QSAR, molecular docking, and molecular dynamics (MD) simulation on 64 quinazolin-4-one derivatives. Results: Comparative molecular field analysis (CoMFA) model (q2 = 0.590, R2 = 0.962), comparative molecular similarity index analysis (CoMSIA) model (q2 = 0.628, R2 = 0.923), and external validation indicated that the stability, reliability, and prediction performance of our constructed model were excellent. We designed 8 inhibitors with stronger antiviral activities through the three-dimensional equipotential field. Molecular docking and MD simulation probed the interactions of compounds and SARS-CoV-2 Mpro. This indicated that amino acid residues, including Met165, Met49, and Cys145, were very important in combination with the compounds. The prediction results of ADME/T and Lipinski’s rule of five indicated that the new compounds had favorable drug-like and pharmacokinetic properties. Conclusion: This study provides new ideas for exploring new drugs against COVID-19

AI-based 3D-QSAR model of FDA-approved repurposed drugs for inhibiting sclerostin

BACKGROUND: Wnt activation promotes bone formation and prevents bone loss. The Wnt pathway antagonist sclerostin and additional anti-sclerostin antibodies were discovered as a result of the development of the monoclonal antibody romosozumab. These monoclonal antibodies greatly increase the risk of cardiac arrest. Three-dimensional quantitative structure-activity relationships (3D-QSAR) predicts biological activities of ligands based on their three-dimensional features by employing powerful chemometric investigations such as artificial neural networks (ANNs) and partial least squares (PLS). OBJECTIVE: In this study, ligand-receptor interactions were investigated using 3D-QSAR Comparative molecular field analysis (CoMFA). Estimates of steric and electrostatic characteristics in CoMFA are made using Lennard-Jones and Coulomb potentials. METHODS: To identify the conditions necessary for the activity of these molecules, fifty Food and Drug Administration (FDA)-approved medications were chosen for 3D-QSAR investigations and done by CoMFA. For QSAR analysis, there are numerous tools available. This study employed Open 3D-QSAR for analysis due to its simplicity of use and capacity to produce trustworthy results. Four tools were used for the analysis on this platform: Py-MolEdit, Py-ConfSearch, and Py-CoMFA. RESULTS: Maps that were generated were used to determine the screen’s r2 (Coefficient of Multiple Determinations) value and q2 (correlation coefficient). These numbers must be fewer than 1, suggesting a good, trustworthy model. Cross-validated (q2) 0.532 and conventional (r2) correlation values of 0.969 made the CoMFA model statistically significant. The model showed that hydroxamic acid inhibitors are significantly more sensitive to the steric field than the electrostatic field (70%) (30%). This hypothesis states that steric (43.1%), electrostatic (26.4%), and hydrophobic (20.3%) qualities were important in the design of sclerostin inhibitors. CONCLUSION: With 3D-QSAR and CoMFA, statistically meaningful models were constructed to predict ligand inhibitory effects. The test set demonstrated the model’s robustness. This research may aid in the development of more effective sclerostin inhibitors that are synthesised using FDA-approved medications.

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.

Study on β-glucosidase activators by 3D-QSAR, molecular docking and molecular dynamics simulation

To investigate the conformational relationship and mechanism of action of β-glucosidase (BGL) activators, a theoretical study of three-dimensional quantitative structure activity relationship, molecular docking, molecular dynamics (MD) simulations, and binding free energy analysis were performed. The results indicate that comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) models had good stability and predictive ability, with r2cv/ r2pred values of 0.540/0.998 and 0.654/0.993, respectively. The derived contour maps of steric, electrostatic, and hydrogen bond donor field further displayed the modified information of these activators. Molecular docking and MD were performed on calcium diascorbate (CaAS) and L-Ascorbyl 2,6-Dipalmitate (L-ADP), which have good activating ability. The study indicates a positive interaction between the activators and the enzyme, and this interaction does not cause irreversible negative effects on the conformation of BGL. The MD results show that the total binding free energies of CaAS and L-ADP with BGL were −19.837 kJ/mol and −37.798 kJ/mol, respectively, and the binding force of the activators with BGL was mainly derived from hydrophobic interaction and hydrogen bonding. This study offers important guidance for the efficient use of BGL and for the research and development of effective activators.

Strategic development and validation of Isoquinoline-Derived inhibitors targeting ROCK1 kinase

Rho-associated coiled-coil containing kinases (ROCKs) have emerged as pivotal downstream effectors of small guanosine triphosphatases. In recent years, isoquinoline derivatives have garnered substantial attention as potent inhibitors of ROCK1 (RKIs). To delve deeper into their pharmacological properties and to identify potential inhibitors, we conducted an in-depth exploration of a series of RKIs derived from isoquinoline derivatives. This exploration encompassed a multifaceted approach, combining three-dimensional quantitative structure–activity relationship modeling, induced fit docking, and virtual screening. Our comparative molecular field analysis model (q2 = 0.609, R2 = 0.991, ONC = 6, and r2pred = 0.973) and the most robust comparative molecular similarity indices analysis model (q2 = 0.712, R2 = 0.998, ONC = 7, and r2pred = 0.89) exhibited commendable predictive accuracy, bolstered by robust validation parameters. To gain deeper insights into the mechanism of action of isoquinoline-based RKIs, we harnessed precise docking techniques, including induced fit docking, structural interaction fingerprints, and binding pose metadynamics simulations. Employing the PRRR_2 pharmacophore, we meticulously screened Enamine's HLL-460 compound library, unearthing thousands of isoquinoline compounds that aligned with the desired profile. Subsequently, we identified three promising compounds (DS01, DS02, and DS03) through Glide docking, each assessed at varying precision levels. These compounds underwent comprehensive evaluation for their drug-like properties, leveraging QikProp and SwissADME, ultimately unveiling their potential as novel and efficacious RKIs. Furthermore, bolstered by molecular dynamics simulations and validated through activity assays, the bioactivity of the screened molecules DS02, and DS03 was corroborated. DS02 and DS03, previously unreported for their activity against ROCK1, delivered exceptionally promising results. This comprehensive study not only elucidates validated strategies for drug development but also imparts invaluable insights into the design of potent RKIs.

Synthesis, evaluation, and computational chemistry of novel selenenyl sulfides as 3C protease inhibitors with strong cell-based antiviral activity

The 3C protease (3Cpro) is an important enzyme for developing therapeutic medicines against some severe viral infections. In order to discover new classes of antiviral agents, 42 novel selenenyl sulfides were prepared and characterized by 1H NMR, 13C NMR, 77Se NMR and HRMS. The target compounds demonstrated promising inhibitions against 3Cpro from either enterovirus 71 (EV71) or coxsackievirus B3 (CVB3), as well as desirable cell-based antiviral activity against these viruses. Among them, 11f displayed the most potent IC50 value of 0.28 ± 0.05 μM against EV71-3Cpro, and the strongest IC50 value of 0.72 ± 0.15 μM against CVB3, with a selectivity index of 51.5 against Hela cell, superior to the control drug rupintrivir. In addition, molecular docking was undertaken to predict a possible binding mode for compound 11f, and a comparative molecular field analysis (CoMFA) model was subsequently generated to understand the structure-activity relationships. Overall, the present research has provided some new insights to design a distinct family of antiviral compounds.

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.

Potent VEGFR-2 inhibitors for resistant breast cancer: a comprehensive 3D-QSAR, ADMET, molecular docking and MMPBSA calculation on triazolopyrazine derivatives

More people are being diagnosed with resistant breast cancer, increasing the urgency of developing new effective treatments. Several lines of evidence suggest that blocking the kinase activity of VEGFR-2 reduces angiogenesis and slows tumor growth. In this study, we developed novel VEGFR-2 inhibitors based on the triazolopyrazine template by using comparative molecular field analysis (CoMFA) and molecular similarity indices (CoMSIA) models for 3D-QSAR analysis of 23 triazolopyrazine-based compounds against breast cancer cell lines (MCF -7). Both CoMFA (Q2 = 0.575; R2 = 0.936, Rpred2 = 0.956) and CoMSIA/SE (Q2 = 0.575; R2 = 0.936, Rpred2 = 0.847) results demonstrate the robustness and stability of the constructed model. Six novel compounds with potent inhibitory activity were carefully designed, and screening of ADMET properties revealed their good oral bioavailability and ability to diffuse through various biological barriers. When compared with the most active molecule in the data set and with Foretinib (breast cancer drug), molecular docking revealed that the six designed compounds had strengthened affinity (−8.9 to −10 kcal/mol) to VEGFR-2. Molecular Dynamics Simulations and MMPBSA calculations were applied to the selected compound T01 with the highest predicted inhibitory activity, confirming its stability in the active pocket of VEGFR-2 over 100 ns. The present results provided the basis for the chemical synthesis of new compounds with improved inhibitory properties against the breast cancer cell line (MCF -7).

Design of novel small molecules derived from styrylpyridine as potent HDAC1 inhibitors for the treatment of gastric cancer using 3D-QSAR, drug similarity, ADMET prediction, molecular docking, and molecular dynamics studies

Histone deacetylase (HDAC) dysregulation plays an important role in cancer progression and is an important therapeutic target for anticancer drug development. A series of molecules derived from styrylpyridine have HDAC inhibitory activity for the treatment of gastric cancer and could have great potential as drugs against gastric cancer.The objective of this work is to modify the styrylpyridine backbone in order to design new inhibitors with high activity and favorable pharmacokinetic properties for drug discovery. Based on the three-dimensional quantitative structure- activity study, robust and reliable comparative molecular field analysis and comparative molecular similarity index analysis models were developed and validated, then used to design seven new molecules and predict in silico their biological activity.As a result, the seven newly designed molecules have a higher biological activity than the synthesized template molecule N21. The designed molecules are submitted to tests for drug-like properties, pharmacokinetics properties, and molecular docking. These tests enabled us to select the two newly-designed molecules T5 and T6 as the best HDAC1 inhibitors, compared with the model molecule N21. Subsequently, molecular dynamics simulations were carried out to study the stability of styrylpyridine derivatives in the active site of HDAC1.The designed molecules T5 and T6 have the potential to be drugs for treating gastric cancer. The synthesis, in vitro and in vivo evaluation of the biological activity of newly designed molecules T5 and T6 are interesting proposal for the conception of new drugs to treat gastric cancer.

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.

Comparative Molecular Field Analysis (CoMFA), Molecular Docking and ADMET Study on Thiazolidine-4-carboxylic acid Derivatives as New Neuraminidase Inhibitors

The objective of this research was to create a 3D-QSAR CoMFA model for a set of twenty-five neuraminidase inhibitors containing thiazolidine-4-carboxylic acid derivatives and to identify a new potent neuraminidase inhibitor for the treatment of influenza. The statistical parameters of the generated model are excellent: Q2 = 0.708, R2 = 0.997. The external validation results were (r2 0 = 0.922, K= 1.016, R2 pred = 0.674, r2 m= 0.778) indicating that the constructed model has good predictive power. Based on the contour map of the CoMFA model, we were able to propose six novel compounds with higher neuraminidase inhibitory activity than the most active compound. The six proposed molecules were submitted to molecular docking to analyse the bindings formed between the newly designed molecules and the neuraminidase. All of the proposed molecules were found to be more stable on the active site of neuraminidase than the reference molecule (1SJ). SwissADME was used to estimate the pharmacokinetic properties of each proposed molecule, while ProToxII and VEGA QSAR were used to investigate any potential toxicity. Finally, a reaction mechanism for synthesizing the six proposed compounds was described, which could potentially be explored further in the search for novel neuraminidase inhibitors. In conclusion, this study has identified potential candidates for the development of more effective neuraminidase inhibitors for the treatment of influenza.

In silico screening-based discovery of benzamide derivatives as inhibitors of Rho-associated kinase-1 (ROCK1)

As a pivotal node in modulating various cell behaviors, Rho-associated kinase-1 (ROCK1) has attracted significant attention as a promising therapeutic target in a variety of diseases. Benzamide has been widely reported as a ROCK1 inhibitors in recent years. To better understand its pharmacological properties and to explore its potential inhibitors, a series of ROCK1 inhibitors derived from N-methyl-4-(4-pyrazolidinyl) benzamides (MPBs) were investigated by using three-dimensional quantitative structure-activity relationship (3D-QSAR) models, pharmacophore models, molecular docking, and molecular dynamics (MD) simulation. The comparative Molecular Field Analysis (CoMFA) model (q2 = 0.616, R2 = 0.972, ONC = 4, and r2 pred = 0.983) and the best Comparative Molecular Similarity Indices Analysis (CoMSIA) model (q2 = 0.740, R2 = 0.982, ONC = 6, and r2 pred = 0.824) exhibited reliable predictability with satisfactory validation parameters. In the subsequent virtual screening, VS03 and VS05 were identified to have superior predicted activities and higher docking scores, meanwhile they demonstrated to be reasonably stable in the binding pocket through MD simulations. These results provide a significant theoretical direction for the rational design and development of novel ROCK1 inhibitors. Communicated by Ramaswamy H. Sarma

Computational Prediction of 3,5-Diaryl-1H-Pyrazole and spiropyrazolines derivatives as potential acetylcholinesterase inhibitors for alzheimer disease treatment by 3D-QSAR, molecular docking, molecular dynamics simulation, and ADME-Tox

The efficacy of 40 synthesized variants of 3,5-diaryl-1H-pyrazole and spiropyrazoline’ derivatives as acetylcholinesterase inhibitors is verified using a quantitative three-dimensional structure-activity relationship (3D-QSAR) by comparative molecular field analysis (CoMFA) and molecular similarity index analysis (CoMSIA) models. In this research, different field models proved that CoMSIA/SE model is the best model with high predictive power compared to several models (Qved2 = O.65; R2 = 0.980; R2test = 0.727). Also, contour maps produced by CoMSIA/SE model have been employed to prove the key structural needs of the activity. Consequently, six new compounds have been generated. Among these compounds, M4 and M5 were the most active but remained toxic and had poor absorption capacities. While the M1, M2, M3 and M6 remained highly active while respecting ADMET's characteristics. Molecular docking results showed compound M2 better with acetylcholinesterase than compound 22. The interactions are classical hydrogen bonding with residues TYR:124, TYR:72, and SER:293, which play a critical role in the biological activity as AChE inhibitors. MD results confirmed the docking results and showed that compound M2 had satisfactory stability with (ΔGbinding = −151.225 KJ/mol) in the active site of AChE receptor compared with compound 22 (ΔGbinding = −133.375 KJ/mol). In addition, both compounds had good stability regarding RMSD, Rg, and RMSF. The previous results show that the newly designed compound M2 is more active in the active site of AChE receptor than compound 22. Communicated by Ramaswamy H. Sarma

Design of novel anti-cancer agents targeting COX-2 inhibitors based on computational studies

The overexpression of cyclooxygenase-2 (COX-2) was clearly associated with carcinogenesis, and COX-2 as a possible target has long been exploited for cancer therapy. A group of 29 derivatives of 1, 5-diarylpyrazole was used to study its structural requirements using three-dimensional quantitative structure–activity relationship (3D-QSAR), the density functional theory method, molecular docking, and molecular dynamics. Four 3D-QSAR models were developed, and the predictive capability of the four selected models was also successfully tested using different validation methods. The contribution contours of the comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) models effectively illustrate the relationships between the various chemical characteristics and their biological activities. Using the density functional theory method with the 6-31G (d, p) basis set and the Becke, 3-parameter, Lee-Yang-Parr (B3LYP) function to evaluate chemical reactivity properties, the results obtained from energy gaps of 3.431, 3.446, and 2.727 ev for molecules numbers 21, 22, and 23 indicate that these three molecules have good chemical stability and reactivity and select the most reactive regions in the three molecules studied. Molecular docking results revealed that the active sites of the COX-2 protein (PDB code: 3PGH) were residues ARG222, THR212, HIS386, HIS207, TYR148, and ASP382, in which the most active ligands and now ligands can inhibit the COX-2 enzyme. Based on the various results obtained by molecular modeling, four new compounds (N1, N2, N3, and N4) were proposed with significant predicted activity by different 3D-QSAR models. A molecular docking study and molecular dynamics simulations of the proposed new molecules (N1 and N2) and the most active molecule over 100 ns revealed that all three molecules establish multiple hydrogen interactions with several residues and also exhibit frequent stability throughout the simulation period. As a result, it is strongly recommended to consider the two newly proposed molecules, N1 and N4, as promising candidates for novel anti-cancer agents specifically designed to target COX-2 inhibition.

Novel Coumarin-furo[2,3-d]pyrimidinone hybrid derivatives as anticancer agents: Synthesis, biological evaluation and molecular docking

A series of coumarin-furo[2,3-d]pyrimidinone hybrid derivatives were synthesized, characterized by HR-MS, 1H NMR and 13C NMR. All synthesized compounds were evaluated for antiproliferative activities against hepatic carcinoma (HepG2) and cervical carcinoma (Hela) cell lines in vitro, and results shown that most of the compounds exhibited potent antitumor activity. Moreover, compound 3i, 8d and 8i were selected to induce apoptosis in HepG2 cells, and it displayed a significant concentration-dependent. Further, transwell migration assay was used to detect the most potent compound 8i, and the results revealed that 8i can significantly inhibit HepG2 cells migration and invasion. In addition, kinase activity assay showed compound 8i may be a multi-target inhibitor, which 8i has an inhibition rate of 40-20% on RON, ABL, GSK3α and so on ten different kinases at the concentration 1 μmol/L. At the same time, molecular docking studies revealed the possible binding modes of compounds 3i, 8d and 8i with kinase recepteur d'origine nantais (RON). A comparative molecular field analysis (CoMFA) model was established from 3D-QSAR study that guide us to a more bulkly and electro-positive Y group at the C-2 position of furo[2,3-d]pyrimidinone ring was preferable for the bioactivity improvement of our compounds. Our preliminary research indicated that the coumarin skeleton introducing to the furo[2,3-d]pyrimidine system had a significantly influence on the biological activities.

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.

Structural identification of novel pyrimidine derivatives as epidermal growth factor receptor inhibitors using 3D QSAR, molecular docking, and MMGBSA analysis: a rational approach in anticancer drug design

Non-small cell lung cancer (NSCLC) has evolved into the deadliest in the present scenario. The progression of NSCLC is mainly due to the dysregulation of the tyrosine kinase family's epidermal growth factor receptor (EGFR). Thus, EGFR has been widely studied as a major target in the treatment of NSCLC, but the lack of selectivity and drug resistance limit the use of existing therapeutic agents. Considering the urgent necessity for the advanced development of EGFR inhibitors, we have implemented a three-dimensional structure-activity relationship (3D QSAR), molecular docking, and MMGBSA studies on a series of pyrimidine derivatives. In the 3D QSAR, the comparative molecular field analysis model (CoMFA) was obtained with a correlation coefficient (r<sup>2</sup>) = 0.698, cross-validated correlation coefficient (q<sup>2</sup>) = 0.541, and predictive r<sup>2</sup> (r<sup>2</sup><sub>pred</sub>) = 0.509. The comparative molecular similarity indices analysis (CoMSIA) model also displayed similar results with r<sup>2 </sup>= 0.72, q<sup>2 </sup>= 0.586, and r<sup>2</sup><sub>pred</sub>= 0.495. The statistical parameters fulfill the acceptability criteria of the models. Docking studies revealed the binding interactions of the pyrimidine derivatives with double mutant EGFR<sup>L858R/T790M</sup>. Docking scores of the top two selected compounds 29 and 34 were 92.99 and 92.13, respectively. Analyzing 3D QSAR contour plots and docking results reviewed some important structural attributes of EGFR <sup>L858R/T790M</sup> selective inhibitors, which directed the designing of some new molecules. The designed compounds showed good predictive activity and exhibited higher binding interactions with EGFRL858R/T790M than the reference ligand gefitinib. Moreover, to evaluate the binding of selected top hits from docking and designed compounds, MMGBSA (Molecular Mechanics-Generalized Born Surface Area) analysis was performed, which revealed that the designed compound (N7) showed a good binding affinity with EGFR<sup>L858R/T790M</sup> (dG = -68.59 kcal/mol) as compared to other compounds. Further, in silico ADME predictions revealed the drug-likeness of the designed compounds. Thus, this work will guide researchers in future developments of pyrimidine derivatives as EGFR inhibitors.

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