3D-QSAR Models Research Articles

Three-Dimensional Quantitative Structure-Activity Relationship Study of Transient Receptor Potential Vanilloid 1 Channel Antagonists Reveals Potential for Drug Design Purposes.

Transient receptor potential vanilloid 1 (TRPV1) was reported to be a putative target for recovery from chronic pain, producing analgesic effects after its inhibition. A series of drug candidates were previously developed, without the ability to ameliorate the therapeutic outcome. Starting from previously designed compounds, derived from the hybridization of antagonist SB-705498 and partial agonist MDR-652, we performed a virtual screening on a pharmacophore model built by exploiting the Cryo-EM 3D structure of a nanomolar antagonist in complex with the human TRPV1 channel. The pharmacophore model was described by three pharmacophoric features, taking advantage of both the bioactive pose of the antagonist and the receptor exclusion spheres. The results of the screening were implemented inside a 3D-QSAR model, correlating with the negative decadic logarithm of the inhibition rate of the ligands. After the validation of the obtained 3D-QSAR model, we designed a new series of compounds by introducing key modifications on the original scaffold. Again, we determined the compounds' binding poses after alignment to the pharmacophoric model, and we predicted their inhibition rates with the validated 3D-QSAR model. The obtained values resulted in being even more promising than parent compounds, demonstrating that ongoing research still leaves much room for improvement.

Design of Novel TRPA1 Agonists Based on Structure of Natural Vasodilator Carvacrol-In Vitro and In Silico Studies.

Considering the escalating global prevalence and the huge therapeutic demand for the treatment of hypertension, there is a persistent need to identify novel target sites for vasodilator action. This study aimed to investigate the role of TRPA1 channels in carvacrol-induced vasodilation and to design novel compounds based on carvacrol structure with improved activities. In an isolated tissue bath experiment, it was shown that 1 µM of the selective TRPA1 antagonist A967079 significantly (p < 0.001) reduced vasodilation induced by 3 mM of carvacrol. A reliable 3D-QSAR model with good statistical parameters was created (R2 = 0.83; Q2 = 0.59 and Rpred2 = 0.84) using 29 TRPA1 agonists. Obtained results from this model were used for the design of novel TRPA1 activators, and to predict their activity against TRPA1. Predicted pEC50 activities of these molecules range between 4.996 to 5.235 compared to experimental pEC50 of 4.77 for carvacrol. Molecular docking studies showed that designed molecules interact with similar amino acid residues of the TRPA1 channel as carvacrol, with eight compounds showing lower binding energies. In conclusion, carvacrol-induced vasodilation is partly mediated by the activation of TRPA1 channels. Combining different in silico approaches pointed out that the molecule D27 (2-[2-(hydroxymethyl)-4-methylphenyl]acetamide) is the best candidate for further synthesis and experimental evaluation in in vitro conditions.

3D-QSAR, Scaffold Hopping, Virtual Screening, and Molecular Dynamics Simulations of Pyridin-2-one as mIDH1 Inhibitors.

Isocitrate dehydrogenase 1 (IDH1) is a necessary enzyme for cellular respiration in the tricarboxylic acid cycle. Mutant isocitrate dehydrogenase 1 (mIDH1) has been detected overexpressed in a variety of cancers. mIDH1 inhibitor ivosidenib (AG-120) was only approved by the Food and Drug Administration (FDA) for marketing, nevertheless, a range of resistance has been frequently reported. In this study, several mIDH1 inhibitors with the common backbone pyridin-2-one were explored using the three-dimensional structure-activity relationship (3D-QSAR), scaffold hopping, absorption, distribution, metabolism, excretion (ADME) prediction, and molecular dynamics (MD) simulations. Comparative molecular field analysis (CoMFA, R2 = 0.980, Q2 = 0.765) and comparative molecular similarity index analysis (CoMSIA, R2 = 0.997, Q2 = 0.770) were used to build 3D-QSAR models, which yielded notably decent predictive ability. A series of novel structures was designed through scaffold hopping. The predicted pIC50 values of C3, C6, and C9 were higher in the model of 3D-QSAR. Additionally, MD simulations culminated in the identification of potent mIDH1 inhibitors, exhibiting strong binding interactions, while the analyzed parameters were free energy landscape (FEL), radius of gyration (Rg), solvent accessible surface area (SASA), and polar surface area (PSA). Binding free energy demonstrated that C2 exhibited the highest binding free energy with IDH1, which was -93.25 ± 5.20 kcal/mol. This research offers theoretical guidance for the rational design of novel mIDH1 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.

Identification and molecular mechanism of novel ACE inhibitory peptides from broccoli protein

Many natural angiotensin-converting enzyme inhibitory (ACEI) peptides have been widely studied and used to prevent and control hypertension. In this study, peptideomics and 3D-QSAR models combined with experimental verification were used to rapidly screen ACEI peptides in broccoli protein hydrolysates, followed by molecular docking and intracellular antihypertensive activity analysis. The results showed that two novel peptides FVLPLR and LPWYR were obtained, and their IC50 values for inhibiting ACE activity in vitro were 3.06 μM and 19.05 μM, respectively. Molecular docking results showed that FVLPLR and LPWYR bind to ACE through hydrogen bonding, electrostatic interaction, and hydrophobic interaction. Both peptides can promote HUVEC cells to produce vasodilator factor NO. These results indicated that broccoli protein could be used as an excellent source of ACE inhibitors.

In Silico Analysis for Exploring the Potential Inhibitors Against Breast Cancer (MCF7) Using Curcumin Analogue Compounds

Breast cancer is one of the most problematic diseases in the world. Currently, there are no potential vaccines for the treatment of this disease. Therefore, finding effective compounds, such as curcumin analogs, is crucial to inhibit breast cancer. Forty-five synthesized curcumin analogs were tested on the MCF7 cell line using the MTT assay. It was shown that nine curcumin compounds, Cpd 5, Cpd 9, Cpd 17, Cpd 18, Cpd 21, Cpd 25, Cpd 28, Cpd 32, and Cpd 45, had better inhibitory activities against breast cancer. Furthermore, in silico analysis was developed using 2D and 3D QSAR models with high predictive ability, with an r2 value of 0.834. In addition, based on molecular docking, molecular dynamics, and pharmacophore results, it was shown that these nine compounds had the lowest binding free energy and were also stable during the simulation. The presence of methoxy groups, hydrogen bond donors, and aromatic ring features are the main factors that enhance the biological activity of curcumin analogs. Therefore, these compounds could serve as references for the next stage of drug design.

4D-QSAR, ADMET properties, and molecular dynamics simulations for designing N-substituted urea/thioureas as human glutaminyl cyclase inhibitors

Human glutaminyl cyclase (hQC) inhibitors have great potential to be used as anti- Alzheimer's disease (AD) agents by reducing the toxic pyroform of β-amyloid in the brains of AD patients. The four-dimensional quantitative structure activity relationship (4D-QSAR) model of N-substituted urea/thioureas was established with satisfying predictive ability and statistical reliability (Q2 = 0.521, R2 = 0.933, R2prep = 0.619). By utilizing the developed 4D-QSAR model, a set of new N-substituted urea/thioureas was designed and evaluated for their Absorption Distribution Metabolism Excretion and Toxicity (ADMET) properties. The results of molecular dynamics (MD) simulations, Principal component analysis (PCA), free energy landscape (FEL), dynamic cross-correlation matrix (DCCM) and molecular mechanics generalized Born Poisson-Boltzmann surface area (MM-PBSA) free energy calculations, revealed that the designed compounds were remained stable in protein binding pocket and compounds b ∼ f (−35.1 to −44.55 kcal/mol) showed higher binding free energy than that of compound 14 (−33.51 kcal/mol). The findings of this work will be a theoretical foundation for further research and experimental validation of urea/thiourea derivatives as hQC inhibitors.

3D-QSAR model-oriented optimization of Pyrazole β-Ketonitrile derivatives with diphenyl ether moiety as novel potent succinate dehydrogenase inhibitors.

Succinate dehydrogenase inhibitor (SDHI) fungicides play important roles in the control of plant fungal diseases. However, they are facing serious challenges from issues with resistance and cross-resistance, primarily attributed to their frequent application and structural similarities. There is an urgent need to design and develop SDHI fungicides with novel structures. Aiming to discover novel potent SDHI fungicides, 31 innovative pyrazole β-ketonitrile derivatives with diphenyl ether moiety were rationally designed and synthesized, which were guided by a 3D-QSAR model from our previous study. The optimal target compound A23 exhibited not only outstanding in vitro inhibitory activities against Rhizoctonia solani with a half-maximal effective concentration (EC50) value of 0.0398 μg mL-1 comparable to that for fluxapyroxad (EC50 = 0.0375 μg mL-1), but also a moderate protective efficacy in vivo against rice sheath blight. Porcine succinate dehydrogenase (SDH) enzymatic inhibitory assay revealed that A23 is a potent inhibitor of SDH, with a half-maximal inhibitory concentration of 0.0425 μm. Docking study within R. solani SDH indicated that A23 effectively binds into the ubiquinone site mainly through hydrogen-bonds, and cation-π and π-π interactions. The identified β-ketonitrile compound A23 containing diphenyl ether moiety is a potent SDH inhibitor, which might be a good lead for novel fungicide research and optimization. © 2024 Society of Chemical Industry.

Identification of Novel Thiazolidinedione Based Pancreatic Lipase Inhibitors for the Management of Obesity Using In Silico Approach

AbstractAccumulation of excessive fat in the body is an indication of obesity, which is becoming an alarming health concern around the world. Pancreatic lipase has been found to have a promising role in the digestion of dietary fats thereby inhibition of pancreatic lipase can be a therapeutic strategy for the management of obesity. Literature showed the anti‐pancreatic lipase potential of thiazolidinediones thereby the current study is designed to identify substituted thiazolidinedione‐based molecules as pancreatic lipase inhibitors. A 3D QSAR model with R2 value of 0.7283 and Stability of 0.843 was generated to determine the nature of substitution necessary for the inhibitory activity. Based on the generated 3D QSAR, the activity of 352 novel‐designed molecules was predicted and further screened through pharmacokinetic parameters. These compounds were docked into the binding site of the pancreatic lipase which led to the identification of 14 hit molecules with dock score between −9.523 to −7.604 kcal/mol. The molecular simulation studies and free energy calculation of the hit compounds i. e. 87 and 80 revealed good stability and binding in the active site of pancreatic lipase. The current study provides 14 hit molecules, which can be synthesized and further evaluated to explore their anti‐pancreatic lipase inhibitory activity.

Developing of pharmacophore and three-dimensional structure-activity relationship models of VCP/p97 ligands and their synthesis and biological evaluation

VCP/p97 is a valosine-containing protein mediating the ubiquitin-proteasome degradation pathway and many cancer pathways, which was considered as a potential drug target. The identification of ligands targeting p97 protein is challenging. Here we report the development of the optimal pharmacophore DHRRR_3 based on the N-benzylpyrimidin-4-amine derivatives, cyclopentanes and pyrimidine derivatives of previous works. A robust 3D-QSAR model with desirable prediction rate in internal and external verification was constructed, where R2, Q2, and Pearson-R were 0.72, 0.76, and 0.95, respectively. Consistent to molecular docking result, THR688 and ASP478 interacted with lead compound CB5339, which was essential for VCP/p97 inhibition activity. Additionally, three novel VCP/p97 inhibitors D1-D3 were designed based on the theoretical models and then chemically synthesized. Biological results showed that molecule D1 (IC50 34 nM) was more active than CB5339 (IC50 44 nM) in inhibition of VCP/p97. Molecule D2 was as active as CB5339 against the viability of both HCT-116 and RPMI-8226 cell lines. The pharmacokinetic properties and binding free energies of synthesized compounds were theoretically estimated. And the results were consistent with the experimental ones, which indicated the high prediction accuracy of the developed models and could provide theoretical guidance for the design of VCP/p97 inhibitors.

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 and evaluation of piperidine carboxamide derivatives as potent ALK inhibitors through 3D-QSAR modeling, artificial neural network and computational analysis

Tumor stands as one of the principal contributors to global mortality. As research into tumor treatments advances, tumor inhibitors emerge as pivotal milestones in tumor therapy. Among these inhibitors, Anaplastic Lymphoma Kinase (ALK), a receptor tyrosine kinase, is critical owing to its close association with tumor cell proliferation and growth, which renders it a critical therapeutic target. This work systematically explores the relationship between the chemical structures of 36 piperidine carboxamide derivatives and their efficacy in inhibiting Karpas-299 tumor cell activity by employing a rigorous 3D-QSAR modeling approach. A robust Topomer CoMFA model was generated and was meticulously validated through ANN neural network analysis (q2 = 0.597, r2 = 0.939, F = 84.401, N = 4, SEE = 0.268). Based on the model, 60 new compounds with desirable inhibitory activities were successfully designed. Combined with Lipinski’s rule and ADMET criteria alongside molecular docking and dynamics simulations, a lead compound with high inhibitory activity and good drug-likeness was selected. Further computational analyses, encompassing free energy landscape and binding free energy calculations, provided compelling evidence of the stable binding conformation of the lead compound and the superior affinity with the target protein at the active site, underscoring its potential therapeutic utility. In summary, this investigation offers valuable insights and methodological guidance for advancing tumor therapy and underscores the promise of piperidine carboxamide derivatives as prospective ALK inhibitors.

Molecular docking and MD simulation studies of 4-thiazol-N-(pyridin-2-yl)pyrimidin-2-amine derivatives as novel inhibitors targeted to CDK2/4/6

PurposeNowadays, cyclin-dependent kinase 4/6 (CDK4/6) inhibitors have been approved for treating metastatic breast cancer and have achieved inspiring curative effects. But some discoveries have indicated that CDK 4/6 are not the requisite factors in some cell types because CDK2 partly compensates for the inhibition of CDK4/6. Thus, it is urgent to design CDK2/4/6 inhibitors for significantly enhancing their potency. This study aims to explore the mechanism of the binding of CDK2/4/6 kinases and their inhibitors to design novel CDK2/4/6 inhibitors for significantly enhancing their potency in different kinds of cancers.Materials and methodsA series of 72 disparately functionalized 4-substituted N-phenylpyrimidin-2-amine derivatives exhibiting potent inhibitor activities against CDK2, CDK4 and CDK6 were collected to apply to this research. The total set of these derivatives was divided into a training set (54 compounds) and a test set (18 compounds). The derivatives were constructed through the sketch molecule module in SYBYL 6.9 software. A Powell gradient algorithm and Tripos force field were used to calculate the minimal structural energy and the minimized structure was used as the initial conformation for molecular docking. By the means of 3D-QSAR models, partial least squares (PLS) analysis, molecular dynamics (MD) simulations and binding free energy calculations, we can find the relationship between structure and biological activity.ResultsIn this study, we used molecular docking, 3D-QSAR and molecular dynamics simulation methods to comprehensively analyze the interaction and structure–activity relationships of 72 new CDK2/4/6 inhibitors. We used detailed statistical data to reasonably verify the constructed 3D-QSAR models for three receptors (q2 of CDK2 = 0.714, R2pred = 0.764, q2 = 0.815; R2pred of CDK4 = 0.681, q2 = 0.757; R2pred of CDK6 = 0.674). MD simulations and decomposition energy analysis validated the reasonability of the docking results and identified polar interactions as crucial factors that influence the different bioactivities of the studied inhibitors of CDK2/4/6 receptors, especially the electrostatic interactions of Lys33/35/43 and Asp145/158/163. The nonpolar interaction with Ile10/12/19 was also critical for the differing potencies of the CDK2/4/6 inhibitors. We concluded that the following probably enhanced the bioactivity against CDK2/4/6 kinases: (1) electronegative groups at the N1-position and electropositive and moderate-sized groups at ring E; (2) electrogroups featured at R2; (3) carbon atoms at the X-position or ring C replaced by a benzene ring; and (4) an electrogroup as R4.ConclusionPrevious studies, to our knowledge, only utilized a single approach of 3D-QSAR and did not integrate this method with other sophisticated techniques such as molecular dynamics simulations to discover new potential inhibitors of CDK2, CDK4, or CDK6. So we applied the intergenerational technology, such as 3D-QSAR technology, molecular docking simulation techniques, molecular dynamics simulations and MMPBSA19/MMGBSA20-binding free energy calculations to statistically explore the correlations between the structure with biological activities. The constructed 3D-QSAR models of the three receptors were reasonable and confirmed by the excellent statistical data. We hope the results obtained from this work will provide some useful references for the development of novel CDK2/4/6 inhibitors.

Evaluation of antibacterial, cytotoxicity, and apoptosis activity of novel chromene-sulfonamide hybrids synthesized under solvent-free conditions and 3D-QSAR modeling studies

In this study, eleven novel chromene sulfonamide hybrids were synthesized by a convenient method in accordance with green chemistry. At first, chromene derivatives (1–9a) were prepared through the multi-component reaction between aryl aldehydes, malononitrile, and 3-aminophenol. Then, synthesized chromenes were reacted with appropriate sulfonyl chlorides by grinding method to give the corresponding chromene sulfonamide hybrids (1–11b). Synthesized hybrids were obtained in good to high yield and characterized by IR, 1HNMR, 13CNMR, CHN and melting point techniques. In addition, the broth microdilution assay was used to determine the minimal inhibitory concentration of newly synthesized chromene-sulfonamide hybrids. The MTT test was used to determine the cytotoxicity and apoptotic activity of the newly synthesized compounds against fibroblast L929 cells. The 3D‑QSAR analysis confirmed the experimental assays, demonstrating that our predictive model is useful for developing new antibacterial inhibitors. Consequently, molecular docking studies were performed to validate the findings of the 3D-QSAR analysis, confirming the potential binding interactions of the synthesized chromene-sulfonamide hybrids with the target enzymes. Molecular docking studies were employed to support the 3D-QSAR predictions, providing insights into the binding interactions between the newly synthesized chromene-sulfonamide hybrids and their target bacterial enzymes, thereby reinforcing the potential efficacy of these compounds as antibacterial agents. Also, some of the experimental outcomes supported or conflicted with the pharmacokinetic prediction (especially about compound carcinogenicity). The performance of ADMET predictor results was assessed. The work presented here proposes a computationally driven strategy for designing and discovering a new sulfonamide scaffold for bacterial inhibition.

Unveiling Novel HIV-1 Protease Inhibitors through an Integrated Analysis of 3D-QSAR, Molecular Docking, and Binding Free Energy

Introduction: HIV-1, the primary causative agent of AIDS, remains a formidable and lethal virus globally, claiming the lives of millions over the past four decades since its discovery. Recent research has underscored the potential of HIV-1 protease as a therapeutic target, offering a promising strategy for inhibiting viral replication within the body. Method: In light of this, we have curated an extensive database comprising 193 derivatives of Darunavir (DRV), an HIV-1 protease inhibitor. Simultaneously, we have developed a comprehensive set of 3D-QSAR models to elucidate the structure-activity relationships of these 193 derivative inhibitors. Employing various computational simulation techniques, including Comparative Molecular Field Analysis (CoMFA), Comparative Similarity Indices Analysis (CoMSIA), and molecular docking, we have unveiled the fundamental three-dimensional structural features influencing their biological activity. Result: Results indicate that the optimal CoMSIA model (Q2 = 0.500, R2 ncv = 0.882, R2 pred = 0.797) surpasses other models, demonstrating superior predictive capability. Furthermore, docking results suggest that DRV derivatives maintain stable conformations within the binding cavity due to synergistic interactions, such as hydrogen bonding and non-bonded interactions. Drawing insights from the best computational models, we have designed five DRV derivatives with significant HIV-1 protease inhibitory activity through local modification, with theoretical calculations indicating favorable pharmacokinetic properties and synthetic feasibility for the newly proposed molecules. Conclusion: It is hoped that the findings and conclusions obtained herein may furnish theoretical underpinning and directional guidance for the design, optimization, and experimental synthesis of DRV derivative compounds for pharmaceutical purposes. conclusion: In conclusion, this research identifies key residues, including Asp25, Gly27, Asp29, Asp30, Asp124, and Asp129, as significant for ligand binding. The information derived from the in silico models contributes to the design of five newly proposed DRV derivative compounds as promising HIV-1 protease inhibitors, surpassing the inhibitory activity of compound 171. The study holds potential for optimizing Darunavir derivatives in the development of anti-HIV-1 protease drugs.

Machine learning-based QSAR modeling, molecular docking, dynamics simulation studies for cytotoxicity prediction in MDA-MB231 triple-negative breast cancer cell line

Triple-negative breast cancer (TNBC) is the most aggressive cancer type that tests negative for progesterone, estrogen, and HER2 protein receptors. Therefore, TNBC is unlikely to respond either to drugs that target HER2 receptors or to hormonal therapy drugs. In the present study, 2D-QSAR model was developed to predict the inhibitory concentration (IC50) of terpene derivatives against human breast cancer metastatic cell line MDA-MB231, using V-Life MDS v4.5, module. The model was developed using the forward stepwise multiple linear regression method, with a regression coefficient (r2) of 0.86, and a cross-validated r2 (q2) of 0.8448. Molecular descriptors namely electronegativity (Epsilon-3), carbon atoms separated through five bond distances (T_C_C_5), Sum of Electrotopological state indices of –CH2 group (SssCH2count), Dipole moment of coordinate-z (Zcomp Dipole), and Distance between highest positive and negative electrostatic potential on van-der Waals surface area (Most +ve & -ve Potential Distance) were found to be the most potent contributing chemical descriptors for cytotoxicity against TNBC MDA-MB231 cell line. Additionally, binding affinities and interaction patterns revealed that the proposed compounds exhibit good binding affinities and substantial stability with c-Met-and β-tubulin receptors, as assessed by docking studies. Molecular dynamics simulations (100 ns) and binding free energy calculations were also performed using the MMGBSA method. The pharmacokinetic and eADME/T analysis of predicted compounds were assessed through Discovery Studio software. These findings may be of immense importance in the optimization and development of dual inhibitory potent anti-cancer inhibitors against the MDA-MB231 TNBC cell line. Moreover, this novel QSAR based prediction model/method is implemented in R-package and software tool developed for virtual screening purposes and available online for download through the public repository GitHub.

Use of Pharmacophore Modeling, 3D-atom-based QSAR, ADMET, Docking, and Molecular Dynamics Studies for the Development of Psoralen-based Derivatives as Antifungal Agents

Background:: The mortality and morbidity rates in patients caused by fungi are ex-tremely high. 3-4 % of species of fungi like Candida and Aspergillus are responsible for &gt;99% of invasive fungal infections. Aim:: The goal of the current work was to use several In-silico methods, such as Pharmacophore modeling and 3D-QSAR, to design New chemical entities (NCEs) that have antifungal activity. Material &amp; Method:: A dataset of 40 Psoralen derivatives was taken from available literature, and then, the pharmacophore hypothesis and 3D-QSAR model development were generated using Schrodinger 2023-1 software. After designing a library of 36 compounds, they were sub-jected to ADMET prediction. Screened compounds from the ADMET study were docked with 14 alpha demethylase CYP51 (PDB ID: 3LD6) using Schrödinger software. Molecular dynam-ics (MD) simulation studies were performed on PDB-3LD6 using Desmond-v7.2. Results &amp; Discussion:: The top-ranked hypothesis, AHRRR_1, was taken into consideration when designing the library of potential NCEs.In order to check the drug likeliness of the com-pounds, all 36 designed NCEs were subjected to ADMET prediction using the QikProp tool. The majority of compounds have a good partition coefficient index (less than five). Qplog HERG value was found to be less, making them safer and less toxic. C- 4, 6, 9, 13, 15, 22, 24, 27, 31, and 33 have shown compliance with Lipinski’s rule with zero violations. Compounds C-9, C-13, C-22, C-24, and C-27 have shown better docking scores than the standard Ketocon-azole. Compounds C-9, 24, and 27 have shown a greater number of hydrophobic and hydrogen bond interactions in comparison with the other compounds. Compounds 9, 24, and 27 showed good stability after 100ns molecular simulation simulations. result: The top ranked hypothesis AHRRR_1 was taken into consideration when designing the library of potential NCE&amp;#039;s.In order to check the drug likeliness of the compounds, all 36 designed NCE&amp;#039;s were subjected to ADMET prediction using QikProp tool. The majority of compounds have good partition coefficient index (less than five), and Qplog HERG value was found to be less, making them safer and less toxic.C- 4, 6, 9, 13, 15, 22, 24, 27, 31, 33 have shown compliance to Lipinski’s rule with zero violations. The majority of the compounds had greater binding affinity than the standard Ketoconazole. Three compounds C-9, 24 and 27 have the good hydrophobic and hydrogen bonding interactions required for antifungal activity.The Compounds 9, 24 and 27 showed good stability after 100ns molecular simulation simulations. Conclusion:: In the current work, the application of insilico methods such as pharmacophore hypothesis, 3D QSAR, ADMET study, docking, and simulation studies have helped to optimize Psoralen pharmacophore for potential antifungal activity. Therefore, the outcomes of the present study could provide insights into the discovery of new potential alpha demethylase inhibitors with improved selectivity and activity against fungal infections. other: Nil

3D-QSAR Modeling on 2-Pyrimidine Carbohydrazides as Utrophin Modulators for the Treatment of Duchenne Muscular Dystrophy by Combining CoMFA, CoMSIA, and Molecular Docking Studies.

The 3D-QSAR models were developed using CoMFA and CoMSIA techniques to investigate essential molecular fields, optimization strategies, and structure-activity relationships for utrophin-modulating compounds. The data set (71 molecules) was divided into two training and test sets using the hierarchical clustering approach. The training set was aligned based on the most active compound. The built and optimized models based on the PLS approach provided acceptable results. The results were q 2 = 0.528 and r 2 = 0.776 for CoMFA and q 2 = 0.600 and r 2 = 0.811 for CoMSIA models. According to the statistical results, it was found that both the CoMFA models with and without regional focusing and also the CoMSIA model have good estimation ability. Molecular docking was also performed with high-activity compounds (as ligands) and target receptors (protein), and its results, together with the results of 3D-QSAR, give new insights for the design of compounds with higher biological activity. Finally, based on the overall results, the design of new compounds with higher utrophin modulation activity was carried out.

An in silico study on human carcinogenicity mechanism of polybrominated biphenyls exposure

Polybrominated biphenyls (PBBs) are associated with an increased risk of thyroid cancer; however, relevant mechanistic studies are lacking. In this study, we investigated the mechanisms underlying PBB-induced human thyroid cancer. Molecular docking and molecular dynamics methods were employed to investigate the metabolism of PBBs by the cytochrome P450 enzyme under aryl hydrocarbon receptor mediation into mono- and di-hydroxylated metabolites. This was taken as the molecular initiation event. Subsequently, considering the interactions of PBBs and their metabolites with the thyroxine-binding globulin protein as key events, an adverse outcome pathway for thyroid cancer caused by PBBs exposure was constructed. Based on 2D quantitative structure activity relationship (2D-QSAR) models, the contribution of amino acid residues and binding energy were analyzed to understand the mechanism underlying human carcinogenicity (adverse effect) of PBBs. Hydrogen bond and van der Waals interactions were identified as key factors influencing the carcinogenic adverse outcome pathway of PBBs. Analysis of non-bonding forces revealed that PBBs and their hydroxylation products were predominantly bound to the thyroxine-binding globulin protein through hydrophobic and hydrogen bond interactions. The key amino acids involved in hydrophobic interactions were alanine 330, arginine 381 and lysine 270, and the key amino acids involved in hydrogen bond interactions were arginine 381 and lysine 270. This study provides valuable insights into the mechanisms underlying human health risk associated with PBBs exposure.

Potent inhibition of human and rat 17β-hydroxysteroid dehydrogenase 1 by curcuminoids and the metabolites: 3D QSAR and in silico docking analysis

ABSTRACT Curcumin, an extensively utilized natural pigment in the food industry, has attracted considerable attention due to its potential therapeutic effects, such as anti-tumorigenic and anti-inflammatory activities. The enzyme 17β-Hydroxysteroid dehydrogenase 1 (17β-HSD1) holds a crucial position in oestradiol production and exhibits significant involvement in oestrogen-responsive breast cancers and endometriosis. This study investigated the inhibitory effects of curcuminoids, metabolites, and analogues on 17β-HSD1, a key enzyme in oestradiol synthesis. Screening 10 compounds, including demethoxycurcumin (IC50, 3.97 μM) and dihydrocurcumin (IC50, 5.84 μM), against human and rat 17β-HSD1 revealed varying inhibitory potencies. These compounds suppressed oestradiol secretion in human BeWo cells at ≥ 5–10 μM. 3D-Quantitative structure-activity relationship (3D-QSAR) and molecular docking analyses elucidated the interaction mechanisms. Docking studies and Gromacs simulations suggested competitive or mixed binding to the steroid or NADPH/steroid binding sites of 17β-HSD1. Predictive 3D-QSAR models highlighted the importance of hydrophobic regions and hydrogen bonding in inhibiting 17β-HSD1 activity. In conclusion, this study provides valuable insights into the inhibitory effects and mode of action of curcuminoids, metabolites, and analogues on 17β-HSD1, which may have implications in the field of hormone-related disorders.