3D-QSAR Models Research Articles

Molecular Docking Study and 3D-QSAR Model for Trans-Stilbene Derivatives as Ligands of CYP1B1

Scientific research on stilbenes is conducted for their chemopreventive and therapeutic properties. In experimental studies, natural and synthetic trans-stilbenes exhibit antioxidant, anti-inflammatory, cardioprotective, and anticancer effects. The antitumor activity of some natural and synthetic stilbenes is associated with their interaction with cytochrome P450 family 1, which leads to the inhibition of procarcinogen activation. In the present study, three-dimensional quantitative structure–activity relationship analysis (3D-QSAR) was performed on a series of forty-one trans-stilbene derivatives to identify the most significant features of the molecules responsible for their CYP1B1 inhibitory activity. The developed 3D-QSAR model presented a cross-validated correlation coefficient Q2 of 0.554. The model’s predictive ability was confirmed by external validation (r2 = 0.808). The information provided by 3D-QSAR analysis is expected to be valuable for the rational design of novel CYP1B1 inhibitors.

Natural Compounds from Alhagi maurorum as Potential HCC and HepG2 Inhibitors: An Integrated Study using Pharmacophore Development, Molecular Docking, MD Simulation, and DFT Approaches.

The rise in the frequency of liver cancer all over the world makes it a prominent area of research in the discovery of new drugs or repurposing of existing drugs. This article describes the pharmacophore-based structure-activity relationship (3DQSAR) on the secondary metabolites of Alhagi maurorum to inhibit human liver cancer cell lines Hepatocellular carcinoma (HCC) and hepatoma G2 (HepG2) which represents the molecular level understanding for isolated phytochemicals of Alhagi maurorum. The definite features, such as hydrophobic regions, average shape, and active compounds' electrostatic patterns, were mapped to screen phytochemicals. The 3D-QSAR model generates pharmacophore-based descriptors and alignment of active compounds. Further, docking studies were performed on the active compounds to check out their binding affinity with the active site of the target proteins. It was further validated by applying molecular simulations, and the results were found to be accurate. The geometrical optimization and energy gap of the hit compound were calculated by the density functional theory (DFT). Then, ADMET was performed on this hit compound for drug-like features and toxicity. Out of 59 compounds, eight ligands were found active after the 3D-QSAR study. After that, molecular docking was performed on the active compounds F72, F52, F54, F29, F37, F38, F25, and F29, which were recognized as potential targets, and the docking results showed that compound F52 (also an FDA-approved drug) was the best hit. F52 was found to be the best hit against liver cancer cell lines HCC and HepG2. This study would be helpful for early drug discovery optimization and lead identification.

An integrated theoretical study on natural alkaloids as SARS-CoV-2 main protease inhibitors: a step toward discovery of potential drug candidates with anti-COVID-19 activity.

Background: in the twenty-first century, the emergence of COVID-19 as a highly transmissible pandemic disease caused by SARS-CoV-2 posed a significant threat to humanity. Aims & Objectives: the disease spreads through small respiratory droplets, necessitating the use of various compounds for treatment, with alkaloids being recognized as particularly crucial owing to their diverse pharmaceutical properties. Methodology: in this study, a dataset comprising 100 natural alkaloids obtained from the literature was transformed into 2D chemical structures using Chem Draw 19.1. Subsequently, 3DQSAR studies were conducted on the dataset, resulting in the automatic screening of 50 compounds from the initial pool of 100 compounds. The values of q 2 and r 2 of the validated field-based 3DQSAR model were 0.7186 and 0.971, respectively. The validated atom-based 3DQSAR model has q 2 and r 2 scores of 0.6025 and 0.9845, respectively. Based on the obtained results, 10 compounds with exceptionally active predictive IC50 values were selected for further analysis. Docking experiments were then performed on the selected compounds, and the top three compounds with the highest docking scores were identified as diazepinomicin, (+)-N-methylisococlaurine, and hymenocardine-H. After docking, MM-GBSA was performed on the complexes of diazepinomicin, (+)-N-methylisococlaurine and hymenocardine-H with their corresponding proteins, which resulted in the authentication of the molecular docking scores. MD simulations were also performed to check the flexibility, stability and compactness of these complexes for revalidation of docking scores. Results: finally, ADMET experiments revealed that (+)-N-methylisococlaurine exhibited the most favourable properties among these three compounds.

3D QASR, Antimicrobial Activity and Molecular Docking Studies of C-14 Chiral Matrine Derivatives as Potential Antibiotics.

Antibiotic resistance is recognized as one of the top ten global public health threats, posing a significant challenge to human health. The stereochemistry of chiral molecules, alongside their specific interactions with biological targets, provides essential insights for the development of novel antibacterial agents, This study investigated the antibacterial activity of 32 previously synthesized 14-position chiral matrine derivatives. Among these derivatives, compound Q4 exhibited the strongest activity against Propionibacterium acnes, with a minimum inhibitory concentration (MIC) of 0.007 mg/mL, surpassing that of the positive control (MIC = 0.016 mg/mL). Additionally, compounds Q20 and Q2 demonstrated antibacterial activities comparable to the positive controls. The results indicated that R-configured compounds exhibited significantly greater antibacterial potency than their S-configured counterparts. A systematic analysis using 3D-QSAR modeling elucidated the relationship between molecular structure and antibacterial activity, emphasizing the predominance of steric fields over electrostatic fields, in alignment with experimental observations. Furthermore, molecular docking confirmed the binding interactions between compound Q4 and the target protein. In conclusion, C-14 chiral matrine derivatives exhibit considerable potential as effective antibacterial agents, meriting further exploration in the search for new treatments to combat antibiotic-resistant infections.

Conformation Study and Design of Novel 6-Hydroxybenzothiazole-2-Carboxamides as Potentially Potent and Selective Monoamine Oxidase B Inhibitors for Neuroprotection.

6-hydroxybenzothiazole-2-carboxamide is a novel, potent, and specific monoamine oxidase B inhibitor that can be used to study the structure of molecules and come up with new ways to protect neurons. The objective of this work was to create an effective model using derivatives of 6- hydroxybenzothiazole-2-carboxamide and establish a dependable predictive foundation for the development of neuroprotective monoamine oxidase B inhibitors for the treatment of neurodegenerative diseases. The construction and optimization of all compounds were carried out sequentially using ChemDraw software and Sybyl-X software. The optimized compounds were further analyzed using the COMSIA approach and the Sybyl-X software tool for QSAR modeling. A set of novel compounds of 6-hydroxybenzothiazole-2-carboxamide were created and their IC50 values were forecasted using QSAR modeling. Ultimately, the recently developed compounds underwent a screening process using their IC50 values, and molecular docking tests were conducted on the ten most promising compounds with the highest IC50 values. The 3D-QSAR model exhibited favorable outcomes. The value of q2 in the COMSIA model was 0.569. The model demonstrated a superior r2 value of 0.915, a lower SEE of 0.109, and a higher F-value of 52.714. The statistical findings and validation of the model were deemed adequate. Furthermore, analyzing the contour plots might assist in identifying the necessary structural specifications. This work has the potential to provide an insight into the development of active medicines that protect the nervous system against neurodegenerative disorders.

Design of bisamide inhibitors of the TASK-1 potassium channel in silico.

TWIK-related acid-sensitive potassium channel 1 (TASK-1) is expressed ubiquitously across various tissues and plays a significant role in neural activity and anesthetic modulation, making it a crucial target for pharmaceutical research. The high conservation of binding site residues within the TASK family, particularly between TASK-1 and TASK-3, necessitates the development of selective inhibitors for TASK-1. In this study, we utilized a combination of structure-based drug design (SBDD) and ligand-based drug design (LBDD) approaches. Initially, several bisamide-centered molecules were designed using the program MolAICal, which is recognized for its ability to generate selective inhibitors containing bisamide segments, and conducted preliminary screening via molecular docking. Subsequently, 3D-QSAR models were developed for 56 bisamide derivatives targeting TASK-1 and TASK-3, with the models exhibiting robust predictive capabilities (TASK-1: Q2 = 0.61, R2pred = 0.84; TASK-3: Q2 = 0.60, R2pred = 0.71). Using these models, the candidate molecules were subjected to activity prediction and subsequent filtering. Ultimately, molecular dynamics simulations, coupled with free energy calculations, pinpointed two bisamide-core molecules with favorable ADMET properties as potential selective inhibitors for TASK-1. Furthermore, molecular dynamics simulations revealed the critical role of the key residue Leu122 in conferring selectivity to bisamide compounds for TASK-1 channel proteins.

Ligand-based cheminformatics and free energy-inspired molecular simulations for prioritizing and optimizing G-protein coupled receptor kinase-6 (GRK6) inhibitors in multiple myeloma treatment.

Multiple myeloma (MM) is the second most frequently diagnosed hematological malignancy, presenting limited treatment options with no curative potential and significant drug resistance. Recent studies involving genetic knockdown established the crucial role of GRK6 in upholding the viability of MM cells, emphasizing the need to identify potential inhibitors. Computational exploration of GRK6 inhibitors has not been attempted previously. Herein, the present study reports a multilayered lead prioritization and optimization framework using chemometrics and molecular simulations. 2D QSAR studies revealed that hydrogen bonding and polar interactions enhanced GRK6 inhibitory activity, while increased electron accessibility posed a risk of off-target effects. The pharmacophore hypothesis (DDHRRR_1) featured two hydrogen bond donors, one hydrophobic region, and three aromatic rings, laying the foundation for the 3D QSAR models. Hydrophobic groups, such as pyridine and pyrazole, were shown to enhance inhibition, while smaller groups, like ethyl and hydroxyl, reduced activity. 12,557 DrugBank compounds were screened using the developed chemometric models and molecular docking in tandem, which led to the identification of 7 potential parent leads for subsequent QSAR-guided structural optimizations. 350 lead analogs were generated and the top 4 were further analyzed using molecular docking, ADMET, molecular dynamics, and metadynamics analysis based on Principal Component Analysis (PCA), Probability Density Function (PDF), and Free Energy Landscapes (FEL). Upon cumulative retrospection, we propose a novel analog of DB07168 (DB07168-A13) (docking score: -11.2 kcal/mol, MM-GBSA binding energy: -55.2 kcal/mol) as the most promising GRK6 inhibitor, warranting further in vitro validation, for addressing prospective therapeutic intervention in MM.

3D-QSAR studies on Pyrido[2,3-d]pyrimidine Derivatives as Fibroblast Growth Factor Receptor 1 Inhibitors: Application of Molecular Field Analysis (MFA)

Three-dimensional quantitative structure–activity relationship (3D-QSAR) models were developed for 77 pyrido[2,3-d]pyrimidines derivatives, inhibiting fibroblast growth factor receptor 1 (FGFR1). The QSAR model was developed using 56 compounds and its predictive ability was assessed using a test set of 21 compounds. The predictive 3D-QSAR models have conventional r2 values of 0.920 for MFA and the cross-validated coefficient r2cv values of 0.884 for MFA. The results of 3D-QSAR methodologies provide a powerful tool directed to the design of potent and selective pyrido[2,3-d]pyrimidines inhibitors.

Molecular Modeling Studies of β-aminoacyl containing Homopiperazine derivatives as DPP4 Inhibitors

Dipeptidyl peptidase IV (DPP4) is a promising target for developing novel anti-diabetic and anti-obesity drugs. Pharmacophore based three dimensional quantitative structure activity relationship studies (3D-QSAR) and molecular docking were performed on a series of 56 β-aminoacyl-containing homopiperazine derivatives of DPP4 inhibitors to find out the structural relationship with the activity. The best predictive 3D-QSAR model with pharmacophore based alignment resulted in R2 (training set) value of 0.9407, Q2 (internal test set) value of 0.9053, Pearson-R value of 0.9517 and root mean square error (RMSE) 0.2838. The information rendered by pharmacophore based 3D-QSAR models and docking studies may afford valuable clues to optimize the lead and design of potent, selective inhibitors.

Discovery of Novel Inhibitors Against Resistant Streptococcus pneumoniae MurF Enzyme using Phamracophore Modeling and QSAR Analysis

Main aim of the current study was to discover novel inhibitors against Streptococcus pneumoniae MurF enzyme using analogue based drug design. Pharmacophore mapping and 3D-QSAR analysis was performed on some previously synthesized and evaluated sulfonamide derivatives which are known to be potent inhibitors of penicillin resistant Streptococcus pneumoniae MurF receptor. 3D-QSAR study based on the principle of alignment of pharmacophoric features was performed on the same set of inhibitors using the PHASE module of Schrodinger suite. A five point pharmacophore, ADHRR, with one hydrogen bond acceptor, one hydrogen bond donor, one hydrophobic group and two aromatic rings yielded a statistically significant 3D-QSAR model with 0.96 as R2 value for three PLS components and good predictive correlation coefficients of Q2 = 0.8084, F = 140.6 and Pearson-R = 0.9157 for the test set with nine compounds. Thus, indicating good predictive power of the pharmacophore based 3D-QSAR model.

Structure-Based QSAR Modeling of RET Kinase Inhibitors from 49 Different 5,6-Fused Bicyclic Heteroaromatic Cores to Patent-Driven Validation.

RET receptor tyrosine kinase is crucial for nerve and tissue development but can be an important oncogenic driver. This study focuses on exploring the design principles of potent RET inhibitors through molecular docking and 3D-QSAR modeling of 5,6-fused bicyclic heteroaromatic derivatives. First of all, RET inhibitors of 49 different bicyclic substructures were collected from five different data sources and selected through molecular docking simulations. QSAR models were built from the 3399 conformers of 952 RET inhibitors using the partial least-squares method and statistically evaluated. The optimal QSAR model exhibited high predictive performance, with R 2 (of training data) and Q 2 (of test data) values of 0.801 and 0.794, respectively, effectively predicting known inhibitors. The optimal model was doubly verified by patent-filed RET inhibitors as the out-of-set data to demonstrate acceptable residual analysis results. Moreover, feature importance analysis of the QSAR model outlined the impact of substituent characteristics on the inhibitory activity within the 5,6-fused bicyclic heteroaromatic core structures. Furthermore, the relationship between structure and inhibitory activity was successfully applied to the RET screening of known clinical and nonclinical kinase inhibitors to afford accurate off-target prediction.

Synthesis and Fungicidal Activities of Coumarin Derivatives as Succinate Dehydrogenase Inhibitors.

Succinate dehydrogenase inhibitors (SDHIs) have been developed to the fastest growing family of fungicides. To develop novel succinate dehydrogenase (SDH) inhibitors, 27 novel non-amides coumarin derivatives were designed, synthesized, and characterized. The bioassay revealed that most of the target compounds exhibited significant antifungal activity against Botrytis cinerea in vitro. Notably, compounds 1a and 2c with EC50 values of 0.92 and 0.52µg/mL, respectively, which were better than that of positive control chlorothalonil (EC50=3.14µg/mL). Moreover, in vivo antifungal assay results showed that compound 2c could observably inhibit the growth of B. cinerea on Kuerla pears with remarkable protective at a dosage of 200µg/mL. The scanning electron microscopy (SEM) and transmission electron microscopy (TEM) investigation indicated that compound 2c significantly damaged the cell structures of B. cinerea mycelium. Three-dimensional quantitative structure-activity relationship (3D-QSAR) models were analyzed for structure-activity relationships of all target compounds. Furthermore, molecular docking revealed that compound 2c was able to bind closely to the receptor protein of SDH. Enzyme activity analysis also further verified its inhibitory effect. These results demonstrated that compound 2c may be potential candidate for novel SDH inhibitors, and these results afforded further valuable reference for SDHIs discovery.

3D and 2D-QSAR Studies on Natural Flavonoids for Nitric Oxide Production Inhibitory Activity

Background: Nitric oxide (NO), an important second messenger molecule, regulates numerous physiological responses, while excessive NO generates negative effects on the circulatory, nervous and immune systems. Recently, some natural flavonoids were reported to possess the capability of inhibiting LPS-induced NO production. To fully understand the nature of their own NO inhibitory activity, it is necessary to address the structural requirements of flavonoids as NO inhibitors. Objective: The objective of this work was to develop efficient QSAR models for predicting the NOinhibitory activity of new flavonoids and improving insights into the critical properties of the chemical structures that were required for the ideal NO production inhibitory activities. Methods: To provide insights into the structural basis of flavonoids as NO inhibitors, 3D quantitative structure-activity relationship (3D-QSAR) and 2D-QSAR models were developed on a dataset of 55 flavonoids using comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA) and hologram quantitative structure-activity relationship (HQSAR) approaches. Results: The statistically significant models for CoMFA, CoMSIA and HQSAR resulted in crossvalidated coefficient (q2) values of 0.523, 0.572 and 0.639, non-cross-validated coefficient (r2) values of 0.793, 0.828 and 0.852, respectively. The robustness of these models was further affirmed using a test set of 18 compounds, which resulted in predictive correlation coefficients (r2 pred) of 0.968, 0.954 and 0.906. Furthermore, the models-derived contour maps were appraised for activity trends for the molecules analyzed. Conclusion: The 3D and 2D-QSAR models constructed in this paper were efficient in estimating the NO inhibitory activities of flavonoids and facilitating the design of flavonoid-derived NO production inhibitors.

In Silico Molecular Dynamics and 3D‐QSAR Study on Thiazolidinedione Derivatives as Dual Inhibitors of DPP IV and PTP1B (DDPI's) for the Management of T2DM

AbstractSimultaneous inhibition of dipeptidyl peptidase IV and protein tyrosine phosphatase 1B as DDPI's emerged as a therapeutic intervention for the management of T2DM. In the present study, we have employed molecular dynamics simulation in conjugation with the field‐based 3D QSAR studies on a dataset having DDPI activities to identify the spatial fingerprints of target‐specific thiazolidinedione analogs. Separate contours were generated for both DPP IV and PTP1B showing respective pharmacophoric structural requirements for optimal inhibitory activity. These developed 3D QSAR models also showed good statistical measures (DPP IV: r2 = 0.9468; q2 = 0.7173, and PTP1B: r2 = 0.9718; q2 = 0.819) with the excellent predictive ability with PLS‐generated validation constraints. Comparative steric and electrostatic features were elucidated using respective contour maps for selective target‐specific favorable activity. Furthermore, molecular docking was used for elucidating the mode of binding as DDPI's to DPP IV and PTP1B, along with MD simulation (200 ns) analysis such as RMSD, RMSF, Rg, SASA, PCA, and FEL. These studies revealed that all the protein‐ligand docked complexes elicited an overall better stability as compared to reference ligand (vildagliptin or ertiprotafib) complexes. Molecular docking studies revealed that compound 24 (DPP IV: −150.667 kcal/mol; PTP1B: −142.792 kcal/mol) showed maximum affinity toward both the proteins DPP IV and PTP1B, as compared to their respective standard inhibitor, i.e., vildagliptin −99.9843 kcal/mol and ertiprotafib −125.399 kcal/mol, respectively. A comparative study of these developed multitargeted QSAR models along with molecular docking and dynamics study were employed for the optimization of drug candidates as DDPI's. Compound 24 showed most stable behavior in the binding pockets of both the enzymes, as compared to their respective standard inhibitors. This study will be helpful in designing novel DDPIs with appropriately substitute thiazolidinedione for the management of T2DM. The designed compound YSR‐14 exhibited the predicted IC50 values of 0.143 µM in DPP IV and 0.158 µM in PTP1B along with their excellent binding affinity against both targets.

Identification of inhibitors targeting the FLT3-ITD mutation through 4D-QSAR, in vitro, and in silico

The FMS-like tyrosine kinase 3-internal tandem duplication (FLT3-ITD) mutation is a key target for acute myeloid leukemia (AML) treatment. The second-generation inhibitors such as Gilteritinib still present off-target effects and associated side effects. Therefore, identifying novel FLT3-ITD inhibitors has become a promising strategy for AML treatment. In this study, a 4D-QSAR model was developed based on Gilteritinib and its analogues, and it was found that introducing hydrophobic bulky groups at the piperazine or piperidine of Gilteritinib would enhance the binding affinity to FLT3-ITD. So, three series of targeted compounds (A1-A5, B1–B5 and C1–C5) were designed and synthesized. The antiproliferative activity against MOLM-13 cells was evaluated in vitro. Compound A1 (IC50 = 25.65 nM), with a cubane group at the piperazine position; Compounds B2 (IC50 = 63.38 nM) and C2 (IC50 = 54.96 nM), with a norbornene group at the piperidine position, showed the strongest inhibition in their series. Their IC50 values were comparable to that of the positive control Gilteritinib (IC50 = 22.37 nM). FLT3-ITD was confirmed as the degradation target through a kinase inhibition assay, where the IC50 values were 2.12 nM (Compound A1), 1.29 nM (Compound B2), and 3.06 nM (Compound C2), which were comparable to that of Gilteritinib (IC50 = 0.43 nM). Additionally, molecular docking and molecular dynamics (MD) simulations showed that Compounds A1, B2, and C2 had similar binding modes to that of Gilteritinib with more stable affinities. Overall, these results demonstrated that Compounds A1, B2, and C2 were promising inhibitors for targeting AML with FLT3-ITD mutation.

Evaluation of Fifteen 5,6-Dihydrotetrazolo[1,5-c]quinazolines Against Nakaseomyces glabrata: Integrating In Vitro Studies, Molecular Docking, QSAR, and In Silico Toxicity Assessments.

Nakaseomyces glabrata (Candida glabrata), the second most prevalent Candida pathogen globally, has emerged as a major clinical threat due to its ability to develop high-level azole resistance. In this study, two new 5,6-dihydrotetrazolo[1,5-c]quinazoline derivatives (c11 and c12) were synthesized and characterized using IR, LC-MS, 1H, and 13C NMR spectra. Along with 13 previously reported analogues, these compounds underwent in vitro antifungal testing against clinical N. glabrata isolates using a serial dilution method (0.125-64 mg/L). Remarkably, compounds c5 and c1 exhibited potent antifungal activity, with minimum inhibitory concentrations of 0.37 μM and 0.47 μM, respectively-about a 20-fold improvement in μM concentration over standard drugs like amphotericin B, caspofungin, and micafungin. A detailed structure-activity relationship analysis revealed crucial molecular features enhancing antifungal potency. Extensive molecular docking studies across 18 protein targets explored potential binding pockets and affinities of the lead compounds. A robust 3D-QSAR model, incorporating molecular descriptors Mor26m and Mor29e, displayed good predictive ability for antifungal activity. In silico predictions indicated an absence of herbicidal effect, negligible environmental toxicity (to honeybees, avian species, and aquatic organisms), and mild human toxicity concerns for these compounds. This comprehensive approach aims to develop novel and effective antifungal compounds against the clinically relevant pathogen N. glabrata.

Predicting antibacterial activity of some Curcumin compounds: Fingerprint-based 2D-QSAR models

The strong spreads of bacterial infections, caused inter alia by the strong bacterial resistance to drugs, have impelled scientists to look for new antimicrobial agents. A series of Curcumin derivatives with heterocyclic moiety have been evaluated, in terms of MIC (Minimum Inhibitory Concentration), because of their antibacterial activity against Gram-Positive Cocci (Streptococcus aureus) as well as Gram-Negative Bacilli (Escherichia coli) bacterial strains.To investigate the relationship between activities and structures, a 2D-QSAR study is applied to a set of fifty compounds that are split into two sub-groups; the first group contains 29 molecules that exhibited antibacterial activity towards S. aureus bacterium, while the second one includes 27 molecules that displayed the same activity against E. coli.This study was conducted using the kernel-based Partial Least-Square method (KPLS) combined with four non classical descriptors 2D binary fingerprints (linear, dendritic, radial, and MACCS). All forms of the previous fingerprints displayed good statistical indicators: regression correlation coefficient R2> 0.9 and validation coefficient Q2> 0.8.In terms of correlation and validity, the model built from the dendritic fingerprint was chosen as the best KPLS model for both types of bacteria.Relating to the model visualization, Curcumin's ß-diketone moiety and 1,6-heptadiene consistently improve their efficacy against different types of bacteria.The feruloyl moieties substituents in Curcumin Skeleton showed a positive contribution for the hydroxyl radicals, while the methoxy group was unfavorable.Regardless of the type of bacteria, the hydrophobic group produces a favorable interaction for all structures.

Synthesis and Antifungal Activity Evaluation of Novel L-Carvone-Based 1,3,4-Thiadiazole-amide Derivatives as a Potential Succinate Dehydrogenase Inhibitor.

In an effort to explore new-type high-efficiency antifungal agents, 25 novel L-carvone-based 1,3,4-thiadiazole-amide derivatives were designed, synthesized, and structurally characterized by IR, 1H NMR, 13C NMR, and high-resolution mass spectrometry (HRMS) analyses. The antifungal activity of the target compounds was preliminarily assayed at a concentration of 50 μg/mL, and boscalid, a commercialized fungicide identified as a succinate dehydrogenase inhibitor (SDHI), was employed as the positive control. It was found that all of the target compounds showed moderate to potent antifungal activity against the tested fungi compared to boscalid. Surprisingly, compound 4b exhibited broad-spectrum and significant inhibition activity against the growth of eight phytopathogenic strains with inhibitory rates of 67-89%. Further, the results of the EC50 value test suggested that the EC50 values of compound 4b against Physalospora piricola and Colletotrichum orbiculare were 16.33 and 18.06 μg/mL, respectively, and both of them were better than those of boscalid (16.64 and >50). Therefore, compound 4b deserves further study as a lead compound for novel fungicides. In addition, the inhibitory activity of compound 4b against succinate dehydrogenase (SDH) was evaluated as well to prove that compound 4b (IC50 = 3.38 μM) displayed higher SDH-inhibition activity than boscalid (IC50 = 7.02 μM). The binding mode of compound 4b and SDH was simulated by molecular docking and found to be similar to that of boscalid. The structure-activity relationships (SARs) of the target compounds were analyzed by establishing a 3D-QSAR model. Besides, a 4b-loaded complex 4b/CSTA on a reported L-carvone-based nanochitosan carrier CSTA containing the 1,3,4-thiadiazole-amide group was constructed, and its sustained release performance was investigated in the EtOH-H2O system (1:9, v/v). The complex 4b/CSTA exhibited preferred sustained release performance, indicating its potential for developing environmentally friendly nanofungicides.

Pharmacophore-based 3D-QSAR modeling, virtual screening, docking, molecular dynamics and biological evaluation studies for identification of potential inhibitors of alpha-glucosidase.

Alpha-glucosidase enzyme is considered an important therapeutic target for controlling hyperglycemia associated with type 2 diabetes. Novel scaffolds identified as potential alpha-glucosidase inhibitors from the Maybridge library utilizing pharmacophore modeling, molecular docking and biological evaluation are reported in this manuscript. A total of 51 xanthone series scaffolds previously reported as alpha-glucosidase inhibitors were collected and used as training and test sets. These sets were employed to develop and validate a pharmacophore-based 3D-QSAR model with statistically meaningful results using Schrodinger software. The model showed a high F value (F, 80.1) at five component partial least square factors, a high cross-validation coefficient (Q2, 0.66) and a good correlation coefficient (R2, 0.95). Pearson correlation coefficient (r) of 0.8400 indicated a greater degree of confidence in the model. Subsequently, virtual screening was performed with PHASE module of Schrodinger software using the above model to identify novel alpha-glucosidase inhibitors, and mapped compounds were evaluated for their interactions with the protein. The X-ray co-crystallised structure of the alpha-glucosidase protein in complex with acarbose (PDB Code: 5NN8) was used for molecular docking analysis using GLIDE module and a total of eight compounds were further selected for biological evaluation. Molecular dynamics analysis using GROMACS software was performed in the active site of alpha-glucosidase protein to gain insights into binding mechanism of the four active compounds which were finally found to exhibit inhibitory activity in the biological assay.

Design of some phthalazine molecules as novel VEGFR-2 target inhibitors through 3D-QSAR modeling, molecular docking and dynamic simulation and pharmacokinetics profiling

AbstractBreast cancer is one of the dominant cause of cancer-related mortality in females, with an incidence of approximately 1.3 million cases annually, necessitating the development of effective therapeutic strategies. In this study, 3D-QSAR models were reported based on Phthalazine derivatives as VEGFR-2 inhibitors. The activities of these derivatives were correlated with the steric (S), electrostatic (E), hydrogen bond acceptor (A), and donor (D), and hydrophobic (H) fields, which served as critical parameters in model development. Statistical studies of these models showed that the best models are; CoMFA_S (Q2 = 0.623, R2 = 0.941), and CoMSIA_E + D (Q2 = 0.615, R2 = 0.977). Based on the insights from the model fields and docking simulation of the template (compound 17), twelve molecules were designed. These novel molecules exhibited stronger potency compared to the template and the standard, Sorafenib. Compound 17A emerged as the most potent, with pIC50 = 5.98, for CoMFA_S and 5.85, for CoMSIA_E + D, and a strong docking affinity of − 97.271 kcal/mol, therefore subjected to a 100-ns MD simulation. Results indicate better interaction and stabilizing potential over Sorafenib, due to the lower RMSD, RMSF, Rg, values and favorable hydrogen bond analyses. These conclusions were validated by Gibbs free energy analysis and MM-GBSA calculations, revealing a more favorable interaction free energy of − 18.48 kcal/mol related to Sorafenib. Furthermore, these designed compounds demonstrated promising pharmacokinetic profiles.