In vitro antimicrobial, anticancer evaluation, and in silico studies of mannopyranoside analogs against bacterial and fungal proteins: Acylation leads to improved antimicrobial activity.

Carbohydrate analogs are an important, well-established class of clinically useful medicinal agents that exhibit potent antimicrobial activity. Thus, we explored the various therapeutic potential of methyl α-D-mannopyranoside (MαDM) analogs, including their ability to synthesize and assess their antibacterial, antifungal, and anticancer properties; additionally, molecular docking, molecular dynamics simulation, and ADMET analysis were performed. The structure of the synthesized MαDM analogs was ascertained by spectroscopic techniques and physicochemical and elemental analysis. In vitro antimicrobial activity was assessed and revealed significant inhibitory effects, particularly against gram-negative bacteria along with the prediction of activity spectra for substances (PASS). Concurrently, MαDM analogs showed good results against antifungal pathogens and exhibited promising anticancer effects in vitro, demonstrating dose-dependent cytotoxicity against Ehrlich ascites carcinoma (EAC) cancer cells while sparing normal cells from compound 5, with an IC50 of 4511.65 µg/mL according to the MTT colorimetric assay. A structure-activity relationship (SAR) study revealed that hexose combined with the acyl chains of decanoyl (C-10) and benzenesulfonyl (C6H5SO2-) had synergistic effects on the bacteria and fungi that were examined. Molecular docking was performed against the Escherichia coli (6KZV) and Candida albicans (1EAG) proteins to acquire insights into the molecular interactions underlying the observed biological activities. The docking results were further supported by 100 ns molecular dynamics simulations, which provided a dynamic view of the stability and flexibility of complexes involving MαDM and its targets. In addition, ADMET analysis was used to evaluate the toxicological and pharmacokinetic profiles. Owing to their promising drug-like properties, these MαDM analogs exhibit potential as prospective therapeutic candidates for future development.

QSAR-driven screening uncovers and designs novel pyrimidine-4,6-diamine derivatives as potent JAK3 inhibitors.

This study presents a robust and integrated methodology that harnesses a range of computational techniques to facilitate the design and prediction of new inhibitors targeting the JAK3/STAT pathway. This methodology encompasses several strategies, including QSAR analysis, pharmacophore modeling, ADMET prediction, covalent docking, molecular dynamics (MD) simulations, and the calculation of binding free energies (MM/GBSA). An efficacious QSAR model was meticulously crafted through the employment of multiple linear regression (MLR). The initial MLR model underwent further refinement employing an artificial neural network (ANN) methodology aimed at minimizing predictive errors. Notably, both MLR and ANN exhibited commendable performance, showcasing R2 values of 0.89 and 0.95, respectively. The model's precision was assessed via leave-one-out cross-validation (CV) yielding a Q2 value of 0.65, supplemented by rigorous Y-randomization. , The pharmacophore model effectively differentiated between active and inactive drugs, identifying potential JAK3 inhibitors, and demonstrated validity with an ROC value of 0.86. The newly discovered and designed inhibitors exhibited high inhibitory potency, ranging from 6 to 8, as accurately predicted by the QSAR models. Comparative analysis with FDA-approved Tofacitinib revealed that the new compounds exhibited promising ADMET properties and strong covalent docking (CovDock) interactions. The stability of the new discovered and designed inhibitors within the JAK3 binding site was confirmed through 500 ns MD simulations, while MM/GBSA calculations supported their binding affinity. Additionally, a retrosynthetic study was conducted to facilitate the synthesis of these potential JAK3/STAT inhibitors. The overall integrated approach demonstrates the feasibility of designing novel JAK3/STAT inhibitors with robust efficacy and excellent ADMET characteristics that surpass Tofacitinib by a significant margin.Communicated by Ramaswamy H. Sarma.

In silico computational drug discovery: a Monte Carlo approach for developing a novel JAK3 inhibitors.

Inhibition of Janus kinase 3 (JAK3), a member of the JAK family of tyrosine kinases, remains an essential area of research for developing treatments for autoimmune diseases, particularly cancer and rheumatoid arthritis. The recent discovery of a new JAK3 protein, PDB ID: 4Z16, offers exciting possibilities for developing inhibitors capable of forming a covalent bond with the Cys909 residue, thereby contributing to JAK3 inhibition. A powerful prediction model was constructed and validated using Monte Carlo methods, employing various internal and external techniques. This approach resulted in the prediction of eleven new molecules, which were subsequently filtered to identify six compounds exhibiting potent pIC50 values. These candidates were then subjected to ADMET analysis, molecular docking (including reversible-reversible docking with tofacitinib, an FDA-approved drug, and reversible-irreversible docking for the newly designed compounds), molecular dynamics (MD) analysis for 300 ns, and calculation of free binding energy. The results suggested that these compounds hold promise as JAK3 inhibitors. In summary, the new compounds have exhibited favorable outcomes compared to other compounds across various modeling approaches. The collective findings from these investigations provide valuable insights into the potential therapeutic applications of covalent JAK3 inhibitors, offering a promising direction for the development of novel treatments for autoimmune disorders.Communicated by Ramaswamy H. Sarma.

Identification of terpenoids as potential inhibitors of SARS-CoV-2 (main protease) and spike (RBD) via computer-aided drug design.

The scientific community has been faced with a major challenge in the fight against the SARS-CoV-2 virus responsible for the COVID-19 pandemic, due to the lack of targeted antiviral drugs. To address this issue, we used an in silico approach to screen 23 natural compounds from the terpenoid class for their ability to target key SARS-CoV-2 therapeutic proteins. The results revealed that several compounds showed promising interactions with SARS-CoV-2 proteins, specifically the main protease and the spike receptor binding domain. The molecular docking analysis revealed the importance of certain residues, such as GLY143, SER144, CYS145 and GLU166, in the main protease of the SARS-CoV-2 protein, which play a crucial role in interactions with the ligand. In addition, our study highlighted the importance of interactions with residues GLY496, ARG403, SER494 and ARG393 of the spike receptor-binding domain within the SARS-CoV-2 protein. ADMET and drug similarity analyses were also performed, followed by molecular dynamics and MM-GBSA calculations, to identify potential drugs could be repurposed to combat COVID-19. Indeed, the results suggest that certain terpenoid compounds of plant origin have promising potential as therapeutic targets for SARS-CoV-2. However, additional experimental studies are required to confirm their efficacy as drugs against COVID-19.Communicated by Ramaswamy H. Sarma.

Antioxidant, Volatile Compounds; Antimicrobial, Anti-Inflammatory, and Dermatoprotective Properties of Cedrus atlantica (Endl.) Manetti Ex Carriere Essential Oil: In Vitro and In Silico Investigations.

Cedrus atlantica (Endl.) Manetti ex Carriere is an endemic tree possessing valuable health benefits which has been widely used since time immemorial in international traditional pharmacopoeia. The aim of this exploratory investigation is to determine the volatile compounds of C. atlantica essential oils (CAEOs) and to examine their in vitro antimicrobial, antioxidant, anti-inflammatory, and dermatoprotective properties. In silico simulations, including molecular docking and pharmacokinetics absorption, distribution, metabolism, excretion, and toxicity (ADMET), and drug-likeness prediction were used to reveal the processes underlying in vitro biological properties. Gas chromatography-mass spectrophotometry (GC-MS) was used for the chemical screening of CAEO. The antioxidant activity of CAEO was investigated using four in vitro complementary techniques, including ABTS and DPPH radicals scavenging activity, ferric reductive power, and inhibition of lipid peroxidation (ß-carotene test). Lipoxygenase (5-LOX) inhibition and tyrosinase inhibitory assays were used for testing the anti-inflammatory and dermatoprotective properties. GC-MS analysis indicated that the main components of CAEO are ß-himachalene (28.99%), α-himachalene (14.43%), and longifolene (12.2%). An in vitro antimicrobial activity of CAEO was examined against eleven strains of Gram-positive bacteria (three strains), Gram-negative bacteria (four strains), and fungi (four strains). The results demonstrated high antibacterial and antifungal activity against ten of them (>15 mm zone of inhibition) using the disc-diffusion assay. The microdilution test showed that the lowest values of MIC and MBC were recorded with the Gram-positive bacteria in particular, which ranged from 0.0625 to 0.25 % v/v for MIC and from 0.5 to 0.125 % v/v for MBC. The MIC and MFC of the fungal strains ranged from 0.5 to 4.0% (MIC) and 0.5 to 8.0% v/v (MFC). According to the MBC/MIC and MFC/MIC ratios, CAEO has bactericidal and fungicidal activity. The results of the in vitro antioxidant assays revealed that CAEO possesses remarkable antioxidant activity. The inhibitory effects on 5-LOX and tyrosinase enzymes was also significant (p < 0.05). ADMET investigation suggests that the main compounds of CAEO possess favorable pharmacokinetic properties. These findings provide scientific validation of the traditional uses of this plant and suggest its potential application as natural drugs.

High-throughput virtual screening of phenylpyrimidine derivatives as selective JAK3 antagonists using computational methods.

In this study, we used phenylpyrimidine derivatives with known biological activity against JAK3, a critical tyrosine kinase enzyme involved in signaling pathways, to find similar compounds as potential treatments for rheumatoid arthritis. These inhibitors inhibited JAK3 activity by forming a covalent bond with the Cys909 residue, which resulted in a strong inhibitory effect. Phenylpyrimidine is considered a promising therapeutic target. For pharmacophore modeling, 39 phenylpyrimidine derivatives with high pIC50 (Exp) values were chosen. The best pharmacophore model produced 28 molecules, and the five-point common pharmacophore hypothesis from P HASE (DHRRR_1) revealed the requirement for a hydrogen bond donor feature, a hydrophobic group feature, and three aromatic ring features for further design. The validation of the pharmacophore model phase was performed through 3D-QSAR using partial least squares (P LS). The 3D-QSAR study produced two successful models, an atom-based model (R2 = 0.95; Q2 = 0.67) and a field-based model (R2 = 0.93; Q2 = 0.76), which were used to predict the biological activity of new compounds. The pharmacophore model successfully distinguished between active and inactive medications, discovered potential JAK3 inhibitors, and demonstrated validity with a ROC of 0. 77. ADME-Tox was used to eliminate compounds that might have adverse effects. The best pharmacokinetics and affinity derivatives were selected for covalent docking. A molecular dynamics simulation of the selected molecules and the protein complex was performed to confirm the stability of the interaction with JAK3, whereas MM/GBSA simulations further confirmed their binding affinity. By using the principle of retrosynthesis, we were able to map out a pathway for synthesizing these potential drug candidates. This study has the potential to offer valuable and practical insights for optimizing novel derivatives of phenylpyrimidine.Communicated by Ramaswamy H. Sarma.

In silico discovery of potent and selective Janus kinase 3 (JAK3) inhibitors through 3D-QSAR, covalent docking, ADMET analysis, molecular dynamics simulations, and binding free energy of pyrazolopyrimidine derivatives.

Rheumatoid arthritis is a prevalent and debilitating chronic disease worldwide. Targeting Janus kinase 3 (JAK3) has emerged as a crucial molecular strategy to treat this condition. In this study, we employed a comprehensive theoretical approach that included 3D-QSAR, covalent docking, ADMET, and molecular dynamics to propose and optimize new anti-JAK3 compounds. We investigated a series of 28 1H-pyrazolo[3.4-d]pyrimidin-4-amino inhibitors and developed a highly accurate 3D-QSAR model using comparative molecular similarity index analysis (COMSIA). The model predicted with Q2 = 0.59, R2 = 0.96, and R2(Pred) = 0.89, was validated using Y-randomization and external validation methods. Our covalent docking studies identified T3 and T5 as highly potent inhibitors of JAK3 compared to the reference ligand 17. Additionally, we evaluated the ADMET properties and drug similarity of our newly developed compounds and reference ligand, providing critical insights for further optimization of anti-JAK3 medications. Furthermore, MM-GBSA analysis showed promising results for the designed compounds. Finally, we validated our docking results using molecular dynamics simulations, which confirmed the stability of hydrogen bonding contacts with key residues required to block JAK3 activity. Our findings offer new chemical scaffolds and insights that could lead to the development of novel and effective JAK3 therapeutic targets for treating rheumatoid arthritis.Communicated by Ramaswamy H. Sarma.

In silico screening of a series of 1,6-disubstituted 1H-pyrazolo[3,4-d]pyrimidines as potential selective inhibitors of the Janus kinase 3.

Rheumatoid arthritis is a common chronic disabling inflammatory disease that is characterized by inflammation of the synovial membrane and leads to discomfort. In the current study, twenty-seven 1,6-disubstituted 1H-pyrazolo[3,4-d]pyrimidines were tested as potential selective inhibitors of the tyrosine-protein kinase JAK3 using a number of molecular modeling methods. The activity of the screened derivatives was statistically quantified using multiple linear regression and artificial neural networks. To assess the quality, robustness, and predictability of the generated models, the leave-one-out cross-validation method was applied with favorable results (Q2 = 0.75) and Y-randomization. In addition, the evaluation of the predictive ability of the established model was confirmed by means of an external validation using a composite test set and an applicability domain approach. The covalent docking indicated that the tested 1H-pyrazolo[3,4-d]pyrimidines containing the acrylic aldehyde moiety had irreversible interaction with the residue Cys909 in the active sites of the tyrosine-protein kinase JAK3 by Michael addition. The molecular dynamics for three selected derivatives (compounds 9, 12, and 18) were used to verify the covalent docking by determining the stability of hydrogen bonding interactions with active sites, which are needed to stop tyrosine-protein kinase JAK3. The results obtained showed that the tested compounds containing acrylic aldehyde moiety had favorable binding free energies, indicating a strong affinity for the JAK3 enzyme. Overall, this current study suggests that the tested compounds containing the acrylic aldehyde moiety have the potential to act as anti-JAK3 inhibitors. They could be explored further to be used as treatment options for rheumatoid arthritis.Communicated by Ramaswamy H. Sarma.

3D-QSAR, molecular docking, DFT and ADMET studies on quinazoline derivatives to explore novel DHFR inhibitors.

Resistance to folate antagonists is caused by mutations in the dihydrofolate reductase (DHFR) genes. These mutations affect the amino acids at positions 51, 59, 108 and 164 of DHFR, which appear to play a major role in malaria treatment failure. Therefore, the design of new drugs able to overcome the problem of antifolate drug resistance should receive urgent attention. In this study, a three-dimensional quantitative structure-activity relationship (3 D-QSAR) and molecular docking studies have been performed on antimalarial quinazoline derivatives. The CoMFA (Q2 = 0.63, R2 = 0.83 and Rpred2 = 0.70) and the CoMSIA (Q2 = 0.584, R2 = 0.816, and Rpred2= 0.73) models show a good prediction of antimalarial activity. The reliability and robustness of the proposed models have been tested using several validation methods, which showed that the steric, electrostatic, hydrophobic and H-bond acceptor fields of the CoMSIA model play a key role in the prediction of antimalarial activity. Molecular docking studies reveal important interactions between two isomeric compounds (meta and para) and the DHFR receptor in its wild and mutant forms. The obtained outcomes of molecular docking studies have been validated using a new method based on visual inspection. The DFT study of the two isomeric compounds confirms clearly the trends of 3 D-QSAR and molecular docking for the design of new compounds. Moreover, the consistency between theoretical, 3 D-QSAR and molecular docking analysis provides guidance for the design of new drug candidates, which have been tested using ADMET properties and drug likeness analysis.Communicated by Ramaswamy H. Sarma.

Discovery of anti-colon cancer agents targeting wild-type and mutant p53 using computer-aided drug design.

Mutations in the p53 gene are common and occur in over 50% of all cancers, as it is involved in DNA damage repair, cell cycle regulation and apoptosis. Moreover, the p53 gene is mutated in 70% of colon cancers. Therefore, the development of drugs to combat this mutation requires urgent attention. With this in mind, in silico drug design approaches were applied on quinoline derivatives with anticancer activity. In 3D-QSAR study, steric, electrostatic, hydrophobic and H-bond acceptor fields (SEHA) play an important role in prediction and design of new colon cancer compounds. Indeed, the two best CoMSIA/SEHA models with (Q2 = 0.737, R2 = 0.914, Rpred2 = 0.720) and (Q2 = 0.738, R2 = 0.919, Rpred2= 0.739) show good prediction of human colon carcinoma HCT 116 (p53+/+) and (p53-/-) activities, respectively. Furthermore, the predictive ability and robustness of these models were tested by several validation methods. Molecular docking analyses reveal crucial interactions with the active sites of the p53 protein in both wild type and mutant. Based on these theoretical studies, we designed 10 new compounds with good anticancer activity potential, which were evaluated using ADMET properties. Molecular dynamics simulations were performed to confirm the detailed binding mode of the docking results. Finally, the MM-GBSA based on molecular dynamics simulation confirmed that the designed compounds were able to form stable hydrogen bonding interactions with the crucial residues, which are essential to overcome the p53 mutation in colon cancer.Communicated by Ramaswamy H. Sarma.

In silico design of EGFRL858R/T790M/C797S inhibitors via 3D-QSAR, molecular docking, ADMET properties and molecular dynamics simulations.

The development of L858R/T790M/C797S mutations in EGFR is one of the main reasons for the emergence of resistance after third-generation treatment of non-small cell lung cancer (NSCLC). Therefore, the development of 4th generation drugs needs urgent attention. To overcome resistance, in silico drug discovery and Design approaches were employed on a library of 29 novel 9-heterocyclyl substituted 9H-purines derivatives with EGFRL858R/T790M/C797S inhibition for anticancer activity against NSCLC. The COMSIA/EHA model (Q2 = 0.584, R2 = 0.816, and R p r e d 2 = 0.73) showed a stable and reliable predictive ability of NSCLC activity, which was tested by several validation methods. Molecular docking studies reveal crucial interactions with EGFRL858R/T790M/C797S inhibition for NSCLC activity. Based on theoretical methods, we designed 10 new compounds with good activity potential, which were tested using ADMET properties. Next, the molecular docking results were examined by molecular dynamics simulations to verify the stability of hydrogen bonding interactions with important residues such as MET790, MET793 and SER797, which are essential for the design of 4th generation EGFR Inhibitors to combat drug-resistant NSCLC.

3D-QSAR, docking and ADMET properties of aurone analogues as antimalarial agents.

The development of multi-resistant strains of plasmodium parasite has become a global problem, therefore, the discovery of new antimalarial agents is the only available solution. In order to improve and propose new compounds with antimalarial activity, the three-dimensional quantitative structure-activity relationship (3D-QSAR) and molecular docking studies were carried on aurone analogues acting as Qo site inhibitors in cytochrome b. The 3D-QSAR model was established in this study based on the Comparative Molecular Field Analysis (CoMFA) and the Comparative Molecular Similarity Indices Analysis (CoMSIA). The good predictability was obtained using the CoMFA model (Q2 = 0.5; R2 = 0.97; R pred 2 = 0.72) and the best CoMSIA model (Q2 = 0.526; R2 = 0.915; R pred 2 =  â€‹ 0.765). The predictive capacity of the developed model was evaluated through external validation using a test set compound and an applicability domain technique. In this study, the Steric, electrostatic and hydrogen bond acceptor fields played a key role in antimalarial activity. The results of the molecular docking revealed theoretically the importance of the residues his183 and his82 in the active site of the heme bL, this result was validated by a new assessment method. Based on the previous results, we designed several new potent Cytochrome b inhibitors and their inhibitory activities were predicted by the best model. Furthermore, these new inhibitors were analyzed for their ADMET properties and drug likeness. These results would be of great help in leading optimization for new drug discovery that can solve the problem of multiple drug resistance.

Molecular docking and QSAR studies for modeling the antimalarial activity of hybrids 4-anilinoquinoline-triazines derivatives with the wild-type and mutant receptor pf-DHFR.

Plasmodium falciparum dihydrofolate reductase (pf-DHFR) is one of the several targets in the treatment of malaria. Double and quadruple mutations at residues 51, 59, 108, and 164 of pf-DHFR have been linked to antifolate resistance. Several efforts are underway to overcome this drug resistance and to produce potential inhibitors. In this regard, the quantitative structure-activity relationship (QSAR) and docking studies were performed for previously reported 4-anilinoquinoline and 1,3,5-triazines based molecular hybrids. The generated model showed good correlation coefficients (R2 = 0.70) and test set prediction coefficient (R2 = 0.74). These outcomes showed the good predictive competence of the established QSAR model. Based on these results we docked into active site of pf-DHFR protein with the most active (4) and the less active (5) compounds. The docking results revealed that these molecules interact specifically with SER108 and ILE164 in the pf-DHFR binding pocket as that of best active compound but also showed additional interactions with LEU40 and GLY44.

Molecular Modeling of Antimalarial Agents by 3D-QSAR Study and Molecular Docking of Two Hybrids 4-Aminoquinoline-1,3,5-triazine and 4-Aminoquinoline-oxalamide Derivatives with the Receptor Protein in Its Both Wild and Mutant Types.

Modeling studies using 3D-QSAR and molecular docking methods were performed on a set of 34 hybrids of 4-aminoquinoline derivatives previously studied as effective antimalarial agents of wild type and quadruple mutant Plasmodium falciparum dihydrofolate reductase (DHFR). So, the famous mathematical method multiple linear regression (MLR) was explored to build the QSAR model. The DFT-B3LYP method with the basis set 6-31G was used to calculate the quantum chemical descriptors, chosen to represent the electronic descriptors of molecular structures. On the contrary, the MM2 method was used to calculate lipophilic, geometrical, physicochemical, and steric descriptors. The QSAR model tested with artificial neural network (ANN) method shows high performance towards its predictability. The predicted model was confirmed by three validation methods: leave-one-out (LOO) cross validation, Y-randomization, and validation external. The molecular docking study of three compounds 9, 11, and 26 on both wild and quadruple mutant types of pf-DHFR-TS as the protein target helps to understand more and then predict the binding modes with the binding sites.