3D QSAR Research Articles

Discovery of Small‐Molecules as Potential GSK‐3β Inhibitors for the Treatment of Alzheimer's Disease Using Pharmacophore‐Based Virtual Screening

AbstractGlycogen synthase kinase‐3β (GSK‐3β) plays pivotal role in regulating diverse range of cellular functions. It plays negative role in cellular signaling pathways and believed to involved in the pathologies of various diseases like neurological disorders, type II diabetes, inflammation, cardiac hypertrophy, cancer and bipolar disorders. GSK‐3β is proposed as a promising target for drug discovery in treating these disease conditions. A number of structurally different chemical scaffolds including pyrimidinone derivatives have been identified as potential GSK‐3β inhibitors. In the current study, PHASE module of Schrödinger 3D QSAR was used on about 157 pyrimidinone derivatives for the development of statistically significant PLS model. Consecutively, the best pharmacophore hypothesis with features like three hydrogen bond acceptors (A1‐3), two hydrophobic regions (H1 and H2), and one aromatic ring (R1), was selected to screen ZINC and PUBCHEM databases. Molecules with matching pharmacophoric features and ROF were subjected to structure‐based virtual screening (HTVS→SP→XP) and MMGBSA. Two hits from each library were selected for MD simulation studies that showed good pharmacokinetic properties, binding score (−6.8–−8.8 kcal/mol) and ▵GMMGBSA (−55.80–−58.1 kcal/mol) as compared to the reference molecule, NP‐12. Similarly, MD simulation, and MMPBSA identified three compounds i. e. ZINC67743231, ZINC01582756, and PUBCHEM11553018 displaying stability in the binding pocket and demonstrated better binding affinity of −22.07, −27.33, and −30.61 kcal/mol, respectively, within the active site of GSK‐3β. DFT studies also demonstrated the stability of these three lead compounds with a high energy gap between HOMO and LUMO ranging between 0.14546 and 0.1718 eV.

Development and experimental validation of 3D QSAR models for the screening of thyroid peroxidase inhibitors using integrated methods of computational chemistry

The intricate network of glands and organs that makes up the endocrine system. Hormones are used to regulate and synchronize the nervous and physiological systems. The agents which perturbate an endocrine system are called endocrine disruptors and they can eventually affect cellular proliferation and differentiation in target tissues. A subclass of endocrine disruptors known as thyroid disruptors (TDs) or thyroid disrupting chemicals (TDCs) influence the hypothalamo-pituitary-thyroid axis or directly interfere with thyroid function by binding to thyroid hormone receptors. Thyroid hormone levels in circulation are now included in more test guidelines (OECD TG 441, 407, 408, 414, 421/422, 443/416). Although these might be adequate to recognize thyroid adversity, they are unable to explain the underlying mechanism of action. Thyroid peroxidase (TPO) and sodium iodide symporter (NIS), two proteins essential in the biosynthesis of thyroid hormones, are well-accepted molecular targets for inhibition. The screening of a large number of molecules using high throughput screening (HTS) requires a minimum quantity of sample, cost, and time consuming. Whereas 3-dimensional quantitative structure-activity relationship (3D-QSAR) analysis can screen the TDCs before synthesizing a compound. In the present study, the human TPO (hTPO) and NIS (hNIS) structures were modelled using homology modeling and the quality of the structures was validated satisfactorily using MD simulation for 100ns. Further, 190 human TPO inhibitors with IC50 were curated from Comptox and docked with the modelled structure of TPO using D238, H239 and D240 centric grid. The binding conformation of a molecule with low binding energy was used as a reference and the rest other molecules were aligned after generating the possible conformers. The activity-stratified partition was performed for aligned molecules and training set (139), test set (51) were defined. The machine learning models such as k Nearest Neighbor (kNN) and Random Forest (RF) models were built and validated using external experimental dataset containing 10 molecules. Among the 10 molecules, all 10 molecules were identified as TPO inhibitors and demonstrated 100 % accuracy qualitatively. To confirm the selective TPO inhibition all 10 molecules were docked with the modelled structure of hNIS and the results have demonstrated the selective TPO inhibition.

Revisiting methotrexate and phototrexate Zinc15 library-based derivatives using deep learning in-silico drug design approach.

Introduction: Cancer is the second most prevalent cause of mortality in the world, despite the availability of several medications for cancer treatment. Therefore, the cancer research community emphasized on computational techniques to speed up the discovery of novel anticancer drugs. Methods: In the current study, QSAR-based virtual screening was performed on the Zinc15 compound library (271 derivatives of methotrexate (MTX) and phototrexate (PTX)) to predict their inhibitory activity against dihydrofolate reductase (DHFR), a potential anticancer drug target. The deep learning-based ADMET parameters were employed to generate a 2D QSAR model using the multiple linear regression (MPL) methods with Leave-one-out cross-validated (LOO-CV) Q2 and correlation coefficient R2 values as high as 0.77 and 0.81, respectively. Results: From the QSAR model and virtual screening analysis, the top hits (09, 27, 41, 68, 74, 85, 99, 180) exhibited pIC50 ranging from 5.85 to 7.20 with a minimum binding score of -11.6 to -11.0 kcal/mol and were subjected to further investigation. The ADMET attributes using the message-passing neural network (MPNN) model demonstrated the potential of selected hits as an oral medication based on lipophilic profile Log P (0.19-2.69) and bioavailability (76.30% to 78.46%). The clinical toxicity score was 31.24% to 35.30%, with the least toxicity score (8.30%) observed with compound 180. The DFT calculations were carried out to determine the stability, physicochemical parameters and chemical reactivity of selected compounds. The docking results were further validated by 100ns molecular dynamic simulation analysis. Conclusion: The promising lead compounds found endorsed compared to standard reference drugs MTX and PTX that are best for anticancer activity and can lead to novel therapies after experimental validations. Furthermore, it is suggested to unveil the inhibitory potential of identified hits via in-vitro and in-vivo approaches.

2D-QSAR Modeling, Docking, Synthesis and In-Vitro Evaluation of Novel Flavone Derivatives as Anticancer Agents

Background: Cancer is the second leading cause of death globally and is responsible for 10 million deaths in 2020 (2.26 million breast cancer deaths). Due to the problems like drug resistance, toxicities and economic burden, there is a need for the development of novel anticancer agents. Objectives: To design novel flavone derivatives by 2D QSAR studies and docking studies and to evaluate the compounds as potential anticancer agents against MCF7 cell line by MTT assay. Methods: We designed a series of novel flavone derivatives by 2D QSAR modelling using the software QSARINS.The molecular docking studies were carried out to study the molecular interaction and binding affinitiesof the designed compounds against tyrosine protein kinase (PDB ID: 2SRC) by Auto DockVina software. ADMET profiles were calculated for all the designed compounds and five compounds were chosen for synthesis.The synthesized compounds were characterized and evaluated in vitro for anticancer activity against MCF7 cell line by MTT assay. Based on 2D QSAR and molecular docking studies, compounds 3c, 3f, 3i and 3m were synthesized and evaluated for anticancer activity against MCF-7 cell lines. Results: Molecular docking studies of the compounds showed good binding affinity against tyrosine- protein kinase (2SRC). The synthesized flavone derivatives were evaluated for anti-cancer activity against human breast cancer cell line MCF-7 by MTT assay using cisplatin as a positive control. The novel flavone derivative (3c) exhibits more cytotoxicity effect, and the IC50 value of the compound was found to be 52.03 μg/ml. Optimization of these novel scaffolds requires extensive studies on more derivatives. Conclusion: The novel flavone derivatives will be good lead compounds targeting breast cancer.

Conducting 2D and 3D QSAR Analyses and Molecular Docking Studies of Analogues of 2-(1-(1,3,4-thiadiazol-2-yl)piperidin-4-yl)ethan-1-ol with the Aim of Identifying Promising Drug Candidates for Targeting Glioblastoma

Background: 2-(1-(1,3,4-thiadiazol-2-yl)piperidin-4-yl) ethan-1-ol analogues represent novel glutaminase 1 inhibitors. Their exemplary antineoplastic efficacy underscores their prospective utility in glioblastoma chemotherapy. Objectives: This study aimed to elucidate 2D and 3D-QSAR models that authenticate the antineoplastic efficacy of ethan-1-ol analogues and delineate optimal structural configurations conducive to new pharmaceutical design. Methods: The Heuristic Method (HM) was employed for the development of a 2D-linear QSAR paradigm, whilst the Gene Expression Programming (GEP) algorithm was employed for a 2D-nonlinear QSAR paradigm. Concurrently, the CoMSIA methodology was deployed to scrutinize the nexus between pharmaceutical structure and potency. An ensemble of 200 nascent anti-glioma ethan-1-ol compounds was conceptualized, and their potency levels were prognosticated via chemical descriptors and molecular field delineations. Pharmaceuticals epitomizing peak potency were earmarked for molecular docking validation. Results: The empirical modeling exhibited pronounced superiority with the 3D paradigm, succeeded by the GEP nonlinear paradigm and culminated with the HM linear model. The 3D paradigm was characterized by a robust Q2 (0.533), R2 (0.921), and F-values (132.338) complemented by a minimal SEE (0.110). The molecular descriptor MNO coupled with the hydrogen bond donor field facilitated novel pharmaceutical conceptualizations, leading to the identification of the quintessential active molecule, 24J.138, lauded for its superlative antineoplastic attributes and docking proficiency. Conclusion: The orchestration of bidimensional and tridimensional paradigms, synergized by innovative amalgamation of contour maps and molecular descriptors, provides novel insights and methodologies for the synthesis of glioblastoma chemotherapeutic agents.

QSAR Studies and Scaffold Optimization of Predicted Novel ACC 2 Inhibitors to Treat Metabolic Syndrome.

Metabolic syndrome is one of the major non-communicable global health hazards of the modern world owing to its amplifying prevalence. Acetyl coenzyme-A carboxylase 2 (ACC 2) is one of the most crucial enzymes involved in the manifestation of this disease because of its regulatory role in fatty acid metabolism. To find novel potent ACC 2 inhibitors as therapeutic potential leads for combating metabolic syndrome. In the present study, a two-dimensional quantitative structure-activity relationship (2D QSAR) approach was executed on biologically relevant thiazolyl phenyl ether derivatives as ACC 2 inhibitors for structural optimization. The physiochemical descriptors were calculated and thus a correlation was derived between the observed and predicted activity by the regression equation. The significant descriptors i.e. log P (Whole Molecule) and Number of H-bond Donors (Substituent 1) obtained under study were considered for the design of new compounds and their predicted biological activity was calculated from the regression equation of the developed model. The compounds were further validated by docking studies with the prepared ACC 2 receptor. The most promising predicted leads with the absence of an H-bond donor group at the substituted phenyl ether moiety yet increased overall lipophilicity exhibited excellent amino acid binding affinity with the receptor and showed predicted inhibitory activity of 0.0025 μM and 0.0027 μM. The newly designed compounds were checked for their novelty. Lipinski's rule of five was applied to check their druggability and no violation of this rule was observed. The compounds designed in the present study have tremendous potential to yield orally active ACC 2 inhibitors to treat metabolic syndrome.

Identification of PLK1-PBD Inhibitors from the Library of Marine Natural Products: 3D QSAR Pharmacophore, ADMET, Scaffold Hopping, Molecular Docking, and Molecular Dynamics Study.

PLK1 is found to be highly expressed in various types of cancers, but the development of inhibitors for it has been slow. Most inhibitors are still in clinical stages, and many lack the necessary selectivity and anti-tumor effects. This study aimed to create new inhibitors for the PLK1-PBD by focusing on the PBD binding domain, which has the potential for greater selectivity. A 3D QSAR model was developed using a dataset of 112 compounds to evaluate 500 molecules. ADMET prediction was then used to select three molecules with strong drug-like characteristics. Scaffold hopping was employed to reconstruct 98 new compounds with improved drug-like properties and increased activity. Molecular docking was used to compare the efficient compound abbapolin, confirming the high-activity status of [(14S)-14-hydroxy-14-(pyridin-2-yl)tetradecyl]ammonium,[(14S)-15-(2-furyl)-14-hydroxypentadecyl]ammonium and [(14S)-14-hydroxy-14-phenyltetradecyl]ammonium. Molecular dynamics simulations and MMPBSA were conducted to evaluate the stability of the compounds in the presence of proteins. An in-depth analysis of [(14S)-15-(2-furyl)-14-hydroxypentadecyl]ammonium and [(14S)-14-hydroxy-14-phenyltetradecyl]ammonium identified them as potential candidates for PLK1 inhibitors.

Multistage in silico approach to identify novel quinoline derivatives as potential c-kit kinase inhibitors

The type II-C-KIT signaling network has been extensively studied for its potential as a target for cancer treatment, leading to the investigation of quinoline derivatives as compounds with inhibitory effects on c-Kit kinase. In this study, a multistage approach was employed, including the creation of pharmacophore models, 3D QSAR analysis, virtual screening, docking investigations, and molecular dynamics stimulation. The pharmacophore evaluation included a data set of 29 ligands, which resulted in the generation of the ADDHR_1pharmacophore model as the most promising, with a survival score of 5.6812. The main objective was to utilize the atom-based 3D-QSAR approach for generating robust 3D-QSAR models aimed at identifying new TypeII-C-kit kinase inhibitors. The evaluations of these models have convincingly demonstrated their high predictive power (Q2 = 0.6547, R2 = 0.9947). Using atom-based 3D-QSAR data, a total of 7564 novel compounds were generated from R-group enumeration. Molecular docking and MM-GBSA study revealed that compound A1 exhibited the highest binding score of −9.30 kcal/mol and a Δ GBind value of −90.56 kcal/mol. The ZINC compounds were then screened using the pharmacophore model, followed by virtual screening, which identified ZINC65798256, ZINC09317958, ZINC73187176, and ZINC76176670 as potential candidates with promising docking scores. Among these, ZINC65798256 demonstrated the best binding interactions with amino acid residues, ASP810, LYS623, CYS673, and THR670 (PDB ID: 1T46). To further analyze the structural features and molecular interactions, molecular dynamics simulation was conducted for a time scale of 100 ns. Communicated by Ramaswamy H. Sarma

Lead generation of UPPS inhibitors targeting MRSA: Using 3D-QSAR pharmacophore modeling, virtual screening, molecular docking, and molecular dynamic simulations

Undecaprenyl Pyrophosphate Synthase (UPPS) is a vital target enzyme in the early stages of bacterial cell wall biosynthesis. UPPS inhibitors have antibacterial activity against resistant strains such as MRSA and VRE. In this study, we used several consecutive computer-based protocols to identify novel UPPS inhibitors. The 3D QSAR pharmacophore model generation (HypoGen algorithm) protocol was used to generate a valid predictive pharmacophore model using a set of UPPS inhibitors with known reported activity. The developed model consists of four pharmacophoric features: one hydrogen bond acceptor, two hydrophobic, and one aromatic ring. It had a correlation coefficient of 0.86 and a null cost difference of 191.39, reflecting its high predictive power. Hypo1 was proven to be statistically significant using Fischer’s randomization at a 95% confidence level. The validated pharmacophore model was used for the virtual screening of several databases. The resulting hits were filtered using SMART and Lipinski filters. The hits were docked into the binding site of the UPPS protein, affording 70 hits with higher docking affinities than the reference compound (6TC, − 21.17 kcal/mol). The top five hits were selected through extensive docking analysis and visual inspection based on docking affinities, fit values, and key residue interactions with the UPPS receptor. Moreover, molecular dynamic simulations of the top hits were performed to confirm the stability of the protein–ligand complexes, yielding five promising novel UPPS inhibitors.Graphical

Molecular phylogeny, Sequence-based drug design, Docking built virtual screening, dynamics simulations, and ADMET properties of thiazolino 2-pyridone amide derivatives as an inhibitor of Chlamydia trachomatis and SARS-CoV-2 protein

The propagation of emerging diseases and the expensive cost and time lost by using the classic methods, especially in the current scenario with the world being plagued by SARS-CoV-2 and Chlamydia trachomatis diseases, make finding another way to invent new medication very important. That's why we used computational approaches to predict protein-ligand interactions of thiazolino 2-pyridone amide derivatives. The high-throughput virtual screening requires extensive combing through existing datasets in the hope of finding possible matches to screen for new molecules able to inhibit SARS-CoV-2 and Chlamydia trachomatis diseases. In this study, 46 thiazolino-2-pyridone amide derivatives were chosen for planning the powerful inhibitors by utilizing various strategies: QSAR analysis, phylogenetic analysis, homology modeling, docking simulation, molecular dynamics (MD) simulation, as well as ADMET Screening. The 2D QSAR investigation uncovers that these compounds show a satisfactory connection with bioactivity. From that point onward, phylogenetic analysis and homology modeling were used to model the selected receptors, which were then evaluated using both the SAVES and PROSA servers, indicating the best correctness of the modeled protein with the experimental results. Additionally, a docking simulation investigation was carried out to comprehend the 46 thiazolino-2-pyridone amide derivatives' interactions with homologous proteins. Additionally, MD simulations coupled with MM/GBSA verified the chosen complex systems' stability over 1000 ps. Two compounds were chosen as possible inhibitors based on these findings. The expected thiazolino-2-pyridone amide's oral bioavailability and toxicity have been discovered under the ADMET. Thus, these discoveries can be leveraged to develop novel molecules with the necessary action.

Development of 2D and 3D QSAR models of pyrazole derivatives as acetylcholine esterase inhibitors

The drugs that are the most useful in all stages of Alzheimer?s disease (AD) are acetylcholinesterase (AChE) inhibitors. The objectives of this work are to generate various QSAR models for such drugs and to select a robust predictive models from the corresponding models. Studies were then focused on finding a range of pyrazole-like AChE inhibitors by 2D and 3D QSAR analysis. The genetic algorithm-based multiple linear regression (GA-MLR) provided the statistically robust 2D QSAR model that depicted the significance of the molecular volume and the number of multiple bonds along with the presence/ absence of specific atom-centred fragments and topological distance between 2D pharmacophoric features. Furthermore, these results were correlated well with the electrostatic and steric contour maps retrieved from the 3D QSAR (i.e., alignment-dependent molecular field analysis). The 2D QSAR analysis developed a highly statistical and reliable model, which was compared with the mechanistic interpretation of 3D structures and their electrostatic and steric field contributions leading to a predictive 3D QSAR model. The molecule- protein interactions, elicited by molecular docking, corroborated with the field interactions, as revealed by 2D QSAR. Thus, the developed computational models and simulation analyses in the current work provide valuable information for the future design of pyrazole and spiropyrazoline analogs, as potent AChE inhibitors.

Development of 2-dimensional and 3-dimensional QSAR models of Indazole derivatives as TTK inhibitors having Anticancer potential

The study aimed to explore the anticancer efficacy of indazole pharmacophore by analyzing a series of 109 derivatives of indazole as Tyrosine Threonine Kinase (TTK) inhibitors through quantitative activity relationship analysis using 2D and 3D QSAR techniques. The best 2D-QSAR model was generated by the MLR method, showing a high correlation coefficient (r2) of 0.9512, and good internal (q2), and external (pred_r2) cross-validation regression coefficients of 0.8998, and 0.8661, respectively. The residual values were modest, indicating good agreement between the observed and predicted pIC50 values, which suggested that the chosen model was predictably accurate. The 3D QSAR model, built using the SWF kNN approach, displayed a high internal cross-validation regression coefficient (q2) of 0.9132. Essential structural features/considerations in developing indazole as prospective anticancer medicines have been suggested. The study provides a reliable and predictive model for the prediction of anticancer activity of indazole derivatives. The identified essential structural features/considerations may be useful for the development of prospective anticancer medicines.

Pharmacophore mapping, 3D QSAR, molecular docking, and ADME prediction studies of novel Benzothiazinone derivatives.

In the quest to combat tuberculosis, DprE1, a challenging target for novel anti-tubercular agents due to its small size and membrane location, has been a focus of research. DprE1 catalyzes the transformation of DPR into Ketoribose DPX, with Benzothiazinone emerging as a potent pharmacophore for inhibiting DprE1. Clinical trial drugs such as BTZ043, BTZ038, PBTZ169, and TMC-207 have shown promising results as DprE1 inhibitors. This study employed pharmacophore mapping of Pyrazolopyridine, Dinitrobenzamide, and Benzothiazinone derivatives to identify crucial features for eliciting a biological response. Benzothiazinone (Ligand code: 73) emerged as a reference ligand with a fitness score of 3.000. ROC analysis validated the pharmacophore with an excellent score of 0.71. To build a 3D QSAR model, a series of Benzothiazinone congeneric derivatives were explored. The model exhibited strong performance, with a standard deviation of 0.1531, a correlation coefficient for the training set (R2) value of 0.9754, and a correlation coefficient for test set Q2 value of 0.7632, indicating robust predictive capabilities. Contour maps guided the design of novel benzothiazinone derivatives, emphasizing steric, electrostatic, hydrophobic, H-bond acceptor, and H-bond donor groups for structure-activity relationships. Docking studies against PDB ID: 4NCR demonstrated favorable scores, with interactions aligning well with the in-built ligand 26J. Docking validation via RMSD values supported the reliability of the docking results. This comprehensive approach aids in the design of novel benzothiazinone derivatives with potential anti-tubercular properties, contributing to the development of novel anti-tubercular agents which can be pivotal in the eradication of tuberculosis.

Quinoline-based Schiff bases as possible antidiabetic agents: ligand-based pharmacophore modeling, 3D QSAR, docking, and molecular dynamics simulations study.

α-Glucosidase enzyme inhibition is a legitimate approach to combat type 2 diabetes mellitus as it manages to control postprandial hyperglycemia. In this pursuit, a literature search identified quinoline-based molecules as potential α-glucosidase inhibitors. Thus our intended approach is to identify pharmacophoric features responsible for the α-glucosidase inhibition. This was achieved by performing, ligand-based pharmacophore modeling, 3D QSAR model development, pharmacophore-based screening of a rationally designed quinoline-based benzohydrazide Schiff base library, identifying, synthesizing and characterizing molecules (6a-6j) by IR, (1H and 13C) NMR, and mass studies. Further, these molecules were evaluated for α-glucosidase and α-amylase inhibitory potential. Compound 6c was found to inhibit α-glucosidase enzyme with an IC50 value of 12.95 ± 2.35 μM. Similarly, compound 6b was found to have an IC50 value of 19.37 ± 0.96 μM as compared to acarbose (IC50: 32.63 ± 1.07 μM); the inhibitory kinetics of compounds 6b and 6c revealed a competitive type of inhibition; the inhibitory effect can be attributed to its mapped pharmacophoric feature and model validation with a survival score of 5.0697 and vector score of 0.9552. The QSAR model showed a strong correlation with an R 2 value of 0.96. All the compounds (6a-6j) showed no toxicity in L929 cell lines by the MTT assay method. Further, the binding orientation and stability of the molecules were assessed using molecular docking studies and MD trajectory analysis. The energy profile of the molecules with protein as a complex and molecules alone was evaluated using MM/GBSA and DFT calculations, respectively; finally, the pharmacokinetic profile was computed using ADMET analysis.

Design of some potent non-toxic Autoimmune disorder inhibitors based on 2D-QSAR, CoMFA, molecular docking, and molecular dynamics investigations

Current clinical research suggests that inhibitors of protein arginine deiminase 4 (PAD4), major histocompatibility complex (MHC) class II HLA-DQ-ALPHA chain, and thyrotropin receptor (or TSH receptor) which are of biological and therapeutic interest, may show potential in treating rheumatoid arthritis, type 1 diabetes, Graves' disease and other autoimmune disorder. In the present study, a comprehensive analysis was conducted on a collection of 32 compounds concerning their anti-rheumatoid arthritis activity as inhibitors of PAD4. This analysis represents the first instance in which these compounds were computationally examined, employing an in-silico approach that considered 2D-3D QSAR modeling, and molecular docking and was further validated through molecular dynamics and ADMET properties assessment. A credible 2D QSAR (Q_LOO^2 ​= ​0.6611 and R^2 ​= ​0.7535) model was constructed and verified using an external validation test set, Y-randomization, variance inflation factor (VIF), mean effect (MF), and William's plot applicability domain (AD). Ligand-based alignment was implemented in the 3D-QSAR examination. The outcomes demonstrated that CoMFA (uvepls) (Q2LOO ​= ​0.5877; R2 ​= ​0.9983) possess remarkable stability and foresight. The internal validation indicated that CoMFA (uvepls) MIFs display superior predictive capability compared to COMFA (ffdsel). Structural criteria determined by the contour maps of the model and molecular docking simulations were strategically employed to computationally develop 10 new, non-toxic autoimmune disease inhibitors with increased efficacy. Docking tests were done on the newly developed compounds to illustrate their binding mechanism and to identify critical interaction residues inside the active region of rheumatoid arthritis (PDB id: 3BLU). In addition, docking results of the selected designed compounds inside the active sites of type 1 diabetes receptor (6DFX), and Graves' disease receptor (4QT5) demonstrated their rheumatoid arthritis (PDB id: 3BLU) selectivity. A molecular dynamics simulation and binding free energy calculations using the MM/GBSA technique confirmed the stability of the proposed compound D4 inside the rheumatoid arthritis (3BLU) receptor active site. In summary, the results of our investigation might give considerable insight into the future design and development of new autoimmune disease inhibitors.

Evaluation of PPAR gamma agonists: A molecular docking and QSAR study of chalcone analog

In recent years, chalcones have attracted researchers with their unique structure and promising potential across a wide range of scientific disciplines. They are naturally occurring 1,3-diphenyl-2-propen-1-ones. As well as regulating cell proliferation, differentiation, and angiogenesis, PPARs are also involved in cell differentiation. The present study evaluates the molecular activity of 60 chalcone analogs and two marketed drugs as potential PPAR gamma agonists. Molecular docking studies, structural property calculations, and 2D QSAR studies were carried out to screen the best molecule in the library. The compound coded with 2i was found to have the highest binding affinity and selectivity for PPAR gamma. Firm interactions within 5.0 Å were considered for the docking analysis. Ramachandran plot interpretations also helped us to justify the firm binding at the catalytic site. The correlational studies using the QSAR model were carried out, and the coefficient of regression was found to be 0.9247, and the results were plotted. The 2i was found to be the most suitable compound from the library.

Machine learning-based q-RASAR approach for the in silico identification of novel multi-target inhibitors against Alzheimer's disease

In the present research, we present the application of a novel approach, termed the Machine Learning (ML)-Based q-RASAR (quantitative read-across structure-activity relationship) method, for the identification of potential multi-target inhibitors against Alzheimer's disease (AD). The q-RASAR effectively combines the principles of both read-across and 2D QSAR approaches. As a result, it is imperative to take into account similarity-related aspects in the process of developing q-RASAR models. In this investigation, we have implemented ML-based q-RASAR modeling against seven major targets (AChE, BuChE, BACE1, 5-HT6, CDK-5 enzymes, Amyloid precursor protein, and Tau aggregation) of AD using the initially selected features in 2D-QSAR models for the identifications of novel multitarget inhibitors. The models were individually used to check the applicability domain of a pool of 407270 natural products (NPs) obtained from the COCONUT database (https://coconut.naturalproducts.net/download) and provided prioritized compounds for experimental detection of their performance as anti-Alzheimer's drugs. Furthermore, we have also developed the q-RASAAR (quantitative read-across structure-activity-activity relationship) and selectivity-based q-RASAR models to explore the most important features contributing to the dual inhibition against the respective targets. Furthermore, we have applied seven distinct machine learning algorithms to check their influence on the predictive abilities of q-RASAR and q-RASAAR models. Moreover, we have also developed the univariate q-RASAR model, with the RA function as the primary independent variable. Additionally, molecular docking experiments have been conducted to gain insights into the atomic-level molecular interactions between ligands and enzymes. These observations are then juxtaposed with the structural characteristics obtained from models that elucidate the mechanistic aspects of binding events. These proposed models may serve as valuable tools for pinpointing crucial molecular attributes when designing potential drugs for Alzheimer's therapy through the rational design of multi-target inhibitors.

Comprehensive in silico discovery of c-Src tyrosine kinase inhibitors in cancer treatment: A unified approach combining pharmacophore modeling, 3D QSAR, DFT, and molecular dynamics simulation

ObjectiveTo investigate c-Src, a non-receptor tyrosine kinase dysregulated in various cancer types including colon, breast, and pancreatic cancers, as a potential drug target for cancer therapy. MethodsLigand-based pharmacophore modeling and 3D-QSAR analysis on a dataset of 34c-Src tyrosine kinase inhibitors were employed. The established pharmacophore model (DDRRR_1) features two hydrogen bond donor (D) and three aromatic ring (R) features, exhibiting favorable parameters (R2 = 0.926; Q2 = 0.895; F value = 47.9). Hypothesis validation, enrichment analysis, and contour plot analysis were conducted, followed by virtual screening of a PubChem database using the optimized pharmacophore model and filtering based on the Lipinski rule of five. ResultsThe most promising inhibitors underwent multistep molecular docking, density Functional Theory (DFT) analysis, ADMET assessments, molecular dynamics simulation, and PCA. CID_70144047 emerged as the most promising hit with all the above favorable properties. ConclusionThe study provides a comprehensive approach for identifying novel c-Src tyrosine kinase inhibitors, highlighting CID_70144047 as a promising leads with potential therapeutic applications in cancer treatment.

Research on the Anti-tumor Activity of Novel Histone Deacetylase Inhibitors based on 3D QSAR Model

Background: Histone deacetylases (HDACs) pertain to the category of Zn2 + or nicotinamide adenine dinucleotide (NAD+)- dependent proteolytic enzymes. While the antitumor effect of HDAC inhibitors alone has been demonstrated and the effect of HDAC inhibitors on solid tumors is not ideal, which considerably limits their clinical use. Therefore, the search for novel HDAC inhibitors equipped with specific inhibitors is extremely urgent and necessary. Methods: 3D-QSAR was employed to investigate insights into the crucial structural element that effect the activity of novel HDAC small molecule inhibitors. The best saliency CoMFA and CoMSIA models are obtained using 55 molecules in the training set and 16 molecules in the test set. Results: The statistical quality of the generated model is demonstrated by internal and external cross-validations. The CoMFA model obtained satisfactory values (q2 = 0.664, r2 = 0.917, SEE = 0.217) while optimized CoMSIA model exceed with (q2 = 0.672, r2 = 0.948, SEE = 0.175). Conclusion: The statistical parameters from 3D-QSAR models reveal that the results are reliable and significant with strong predictive ability. These theoretical results may contribute to the design of novel HDAC small molecule inhibitors with enhanced activity for the treatment of cancer.

“2D, 3D QSAR and Pharmacophore Identification of Thieno[3,2-d]pyrimidines as Cholesterol inhibitors.”

Introduction:: The present study reveals the 2D, 3D-QSAR analysis of Thieno[3,2-d]pyrimidine to express the biological activity against Cholesterol, structurally different ligands can fit the common receptor site, and Pharmacophore models well describe safety considerations of the said chemical entities Methods:: The organic exercises of the atoms were changed over into log IC50. The measurably huge of 2D-QSAR and 3D QSAR models are r2 = 0 .9762, q2 = 0.9379 and internal (q2 = 0.8837) and external (predictive r2 = 0.9162) respectively. 2D QSAR studies revealed that the Positive coefficient value of Quadrupole2 and Negative coefficient value of T_2_Cl_7 descriptors were major contributing descriptors. Results:: The 3D QSAR models indicate that steric and electrostatic effects primarily determine binding affinities. Conclusion:: From the best model obtained from the QSAR analyses, some newer compounds of the same series were developed, having better activity than the earlier compounds that have been reported.