Rational Design of New Small Derivatives of 2,2'-Bithiophene as Hole Transport Material for Perovskite Solar Cells.

Using Density Functional Theory (DFT) and Time Dependent DFT (TD-DFT) methods, this inquiry theoretically examines seven novel hole-transport materials (HTMs) namely DFBT1, DFBT2, DFBT3, DFBT4, DFBT5, DFBT6, and DFBT7 based on the 2,2'bithiophene core for future use as HTMs for perovskite solar cells (PSCs). The model molecule has been modified through substituting the end groups situated on the diphenylamine moieties with a tow acceptor bridged by thiophene, this modification was performed to test the impact of the π-bridge and acceptor on the electronic, photophysical, and photovoltaic properties of the newly created molecules. DFBT1 - DFBT7 displayed a lower band gap (1.49 eV to 2.69 eV) than the model molecule (3.63 eV). Additionally, the newly engineered molecules presented a greater λmax ranging from 393.07 nm to 541.02 nm in dimethylformamide solvent, as compared to the model molecule (380.61 nm). The PCEs of all newly designed molecules (22.42% to 29.21%) were high compared with the reference molecule (19.62%). Thus, this study showed that all seven newly small molecules were excellent candidates for a novel PSC.

Synthesis, Antimicrobial, Molecular Docking Against Bacterial and Fungal Proteins and In Silico Studies of Glucopyranoside Derivatives as Potent Antimicrobial Agents.

Carbohydrate derivatives play a crucial roles in biochemical and medicinal research, especially in the fields of chemistry and biochemistry. From this perspective, the present study was designed to explore the synthesis of methyl α-D-glucopyranoside derivatives (1-8), focusing on their efficacy against bacterial and fungal inhibition. The structure of the synthesized compounds was ascertained using FTIR, 1H-NMR, 13C-NMR, mass and elemental analyses. Antimicrobial screening revealed strong antifungal properties, with compound 7 exhibiting minimum inhibitory concentrations (MICs) ranging from 16-32 µg/L and minimum bactericidal concentrations (MBCs) ranging from 64-128 µg/L. Incorporating decanoyl acyl groups at C-2 and C-3 of (7) significantly improved the efficacy against bacteria and fungi. Structure-activity relationship (SAR) analysis indicated that adding nonanoyl and decanoyl groups to the ribose moiety enhanced potency against both bacterial and fungal strains. Computational methods, including molecular docking, density functional theory (DFT), Petra, Osiris, Molinspiration (POM) evaluation, and molecular dynamics (MD) simulations, were used to assess the efficacy of these derivatives. Compounds 6 and 7, which presented nonanoyl and decanoyl substituents, demonstrated greater efficacy. In addition, DFT studies identified compound 8 as possessing ideal electronic properties. Molecular docking revealed that compound 8 exhibits exceptional binding affinities to bacterial proteins, conferring potent antibacterial and antifungal activities. In addition, pharmacokinetic optimization via POM analysis highlighted compounds 1 and 2 as promising bioavailable drugs with minimal toxicity. Molecular dynamics simulations confirmed the stability of the 2-S. aureus complex, revealing the therapeutic potential of compounds 2 and 8. Future experiments are required to validate their efficacy for pharmaceutical development. The integration of in vitro and in silico methods, including DFT anchoring dynamics and molecular dynamics simulations, provides a solid framework for the advancement of effective anti-infective drugs.

Identification of Potent Acetylcholinesterase Inhibitors as New Candidates for Alzheimer Disease via Virtual Screening, Molecular Docking, Dynamic Simulation, and Molecular Mechanics-Poisson-Boltzmann Surface Area Calculations.

Huperzine A (HUP) plays a crucial role in Alzheimer's therapy by enhancing cognitive function through increased cholinergic activity as a reversible acetylcholinesterase (AChE) inhibitor. Despite some limitations being seen in AChE inhibitors, ongoing research remains dedicated to finding innovative and more effective treatments for Alzheimer's disease. To achieve the goal of the discovery of potential HUP analogues with improved physicochemical properties, less toxic properties, and high biological activity, many in silico methods were applied. Based on the acetylcholinesterase-ligand complex, an e-pharmacophore model was developed. Subsequently, a virtual screening involving a collection of 1762 natural compounds, sourced from the PubChem database, was performed. This screening yielded 131 compounds that exhibited compatibility with the established pharmacophoric hypothesis. These selected ligands were then subjected to molecular docking within the active site of the 4EY5 receptor. As a result, we identified four compounds that displayed remarkable docking scores and exhibited low free binding energy to the target. These top four compounds, CID_162895946, CID_44461278, CID_44285285, and CID_81108419, were submitted to ADMET prediction and molecular dynamic simulations, yielding encouraging findings in terms of their pharmacokinetic characteristics and stability. Finally, the molecular dynamic simulation, cross-dynamic correlation matrix, free energy landscape, and MM-PBSA calculations demonstrated that two ligands from the selected ligands formed very resilient complexes with the enzyme acetylcholinesterase, with significant binding affinity. Therefore, these two compounds are recommended for further experimental research as possible (AChE) inhibitors.

Ligand-Based Design of Novel Quinoline Derivatives as Potential Anticancer Agents: An In-Silico Virtual Screening Approach.

In this study, using the Comparative Molecular Field Analysis (CoMFA) approach, the structure-activity relationship of 33 small quinoline-based compounds with biological anti-gastric cancer activity in vitro was analyzed in 3D space. Once the 3D geometric and energy structure of the target chemical library has been optimized and their steric and electrostatic molecular field descriptions computed, the ideal 3D-QSAR model is generated and matched using the Partial Least Squares regression (PLS) algorithm. The accuracy, statistical precision, and predictive power of the developed 3D-QSAR model were confirmed by a range of internal and external validations, which were interpreted by robust correlation coefficients (RTrain2=0.931; Qcv2=0.625; RTest2=0.875). After carefully analyzing the contour maps produced by the trained 3D-QSAR model, it was discovered that certain structural characteristics are beneficial for enhancing the anti-gastric cancer properties of Quinoline derivatives. Based on this information, a total of five new quinoline compounds were developed, with their biological activity improved and their drug-like bioavailability measured using POM calculations. To further explore the potential of these compounds, molecular docking and molecular dynamics simulations were performed in an aqueous environment for 100 nanoseconds, specifically targeting serine/threonine protein kinase. Overall, the new findings of this study can serve as a starting point for further experiments with a view to the identification and design of a potential next-generation drug for target therapy against cancer.

Computer-Aided Strategy on 5-(Substituted benzylidene) Thiazolidine-2,4-Diones to Develop New and Potent PTP1B Inhibitors: QSAR Modeling, Molecular Docking, Molecular Dynamics, PASS Predictions, and DFT Investigations.

A set of 5-(substituted benzylidene) thiazolidine-2,4-dione derivatives was explored to study the main structural requirement for the design of protein tyrosine phosphatase 1B (PTP1B) inhibitors. Utilizing multiple linear regression (MLR) analysis, we constructed a robust quantitative structure-activity relationship (QSAR) model to predict inhibitory activity, resulting in a noteworthy correlation coefficient (R2) of 0.942. Rigorous cross-validation using the leave-one-out (LOO) technique and statistical parameter calculations affirmed the model's reliability, with the QSAR analysis revealing 10 distinct structural patterns influencing PTP1B inhibitory activity. Compound 7e(ref) emerged as the optimal scaffold for drug design. Seven new PTP1B inhibitors were designed based on the QSAR model, followed by molecular docking studies to predict interactions and identify structural features. Pharmacokinetics properties were assessed through drug-likeness and ADMET studies. After that density functional theory (DFT) was conducted to assess the stability and reactivity of potential diabetes mellitus drug candidates. The subsequent dynamic simulation phase provided additional insights into stability and interactions dynamics of the top-ranked compound 11c. This comprehensive approach enhances our understanding of potential drug candidates for treating diabetes mellitus.

Molecular docking, drug-likeness and DFT study of some modified tetrahydrocurcumins as potential anticancer agents.

The present study utilized molecular docking and density functional theory (DFT) approaches, and ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties to investigate the binding interactions, reactivity, stability, and drug-likeness of curcumin (1), tetrahydrocurcumin (2), and tetrahydrocurcumin derivatives (3-6) as potential anti-cancer agents. MGL (Molecular Graphic Laboratory) and Discovery Studio Visualizer (DSV) software employed for docking studies. Pharmacokinetic and pharmacodynamic (ADME-Tox) analyses were conducted using SwissADME and pKCSM web servers. Total Electron Density (TED) measurements identified molecular adsorption sites, considering various factors, including quantum chemical characteristics, to assess compound effectiveness using DFT method implanted in the Gaussian software. The binding energy (Eb) from docking simulations was used to evaluate inhibitory potential. ADMET analysis suggested favorable oral bioavailability and pharmacokinetics for all studied substances, excluding compound 4. DFT and docking investigations highlighted compounds 1, 2, and 6 as optimal scaffolds for drug design based on in silico screening tests.

Synthesis, biological evaluation and molecular modelling of 3-Formyl-6-isopropylchromone derived thiosemicarbazones as α-glucosidase inhibitors.

Type-2 Diabetes Mellitus (T2DM) is one of the most common metabolic disorders in the world and over the past three decades its incidence has increased drastically. α-Glucosidase inhibitors are used to control the hyperglycemic affect of T2DM. Herein, we report the synthesis, α-glucosidase inhibition, structure activity relationship, pharmacokinetics and docking analysis of various novel chromone based thiosemicarbazones 3(a-r). The derivatives displayed potent activity against α-glucosidase with IC50 in range of 0.11 ± 0.01-79.37 ± 0.71 µM. Among all the synthesized compounds, 3a (IC50 = 0.17 ± 0.026 µM), 3 g (IC50 = 0.11 ± 0.01 µM), 3n (IC50 = 0.55 ± 0.02 µM), and 3p (IC50 = 0.43 ± 0.025 µM) displayed higher inhibitory activity as compared to the standard, acarbose. Moreover, we have developed a statistically significant 2D-QSAR model (R2tr:0.9693; F: 50.4647 and Q2LOO:0.9190), which can be used in future to further design potent thiosemicarbazones as inhibitors of α-glucosidase.

Design of novel quinoline derivatives as antibreast cancer using 3D-QSAR, molecular docking and pharmacokinetic investigation.

Breast cancer has been one of the most challenging women's cancers and leading cause of mortality for decades. There are several studies being conducted all the time to find a cure for breast cancer. Quinoline derivatives have shown their potential as antitumor agents in breast cancer therapy. In this work, three-dimensional quantitative structure-activity relationships (3D-QSAR) and molecular docking with aromatase enzyme (Protein Data Bank: 3S7S) studies were performed to suggest the current scenario of quinoline derivatives as antitumor agents and to refine the path of these derivatives to discover and develop new drugs against breast cancer. For developing the 3D-QSAR model, comparative molecular similarity indices analysis (CoMSIA) and comparative molecular field analysis (CoMFA) were included. To attain the high level of predictability, the best CoMSIA model was applied. External validation utilizing a test set has been used in order to validate the predictive capabilities of the built model. According to the findings, electrostatic, hydrophobic and hydrogen bond donor, and acceptor fields had a significant impact on antibreast cancer activity. Thus, we generated a variety of novel effective aromatase inhibitors based on prior findings and we predicted their inhibitory activity using the built model. In addition, absorption, distribution, metabolism, elimination and toxicity properties were employed to explore the effectiveness of new drug candidates.

In silico detection of potential inhibitors from vitamins and their derivatives compounds against SARS-CoV-2 main protease by using molecular docking, molecular dynamic simulation and ADMET profiling.

COVID-19 is a new infectious disease caused by SARS-COV-2 virus of the coronavirus Family. The identification of drugs against this serious infection is a significant requirement due to the rapid rise in the positive cases and deaths around the world. With this concept, a molecular docking analysis for vitamins and their derivatives (28 molecules) with the active site of SARS-CoV-2 main protease was carried out. The results of molecular docking indicate that the structures with best binding energy in the binding site of the studied enzyme (lowest energy level) are observed for the compounds; Folacin, Riboflavin, and Phylloquinone oxide (Vitamin K1 oxide). A Molecular Dynamic simulation was carried out to study the binding stability for the selected vitamins with the active site of SARS-CoV-2 main protease enzyme. Molecular Dynamic shows that Phylloquinone oxide and Folacin are quite unstable in binding to SARS-CoV-2 main protease, while the Riboflavin is comparatively rigid. The higher fluctuations in Phylloquinone oxide and Folacin indicate that they may not fit very well into the binding site. As expected, the Phylloquinone oxide exhibits small number of H-bonds with protein and Folacin does not form a good interaction with protein. Riboflavin exhibits the highest number of Hydrogen bonds and forms consistent interactions with protein. Additionally, this molecule respect the conditions mentioned in Lipinski's rule and have acceptable ADMET proprieties which indicates that Riboflavin (Vitamin B2) could be interesting for the antiviral treatment of COVID-19.

Discovery of Phenylcarbamoylazinane-1,2,4-Triazole Amides Derivatives as the Potential Inhibitors of Aldo-Keto Reductases (AKR1B1 & AKRB10): Potential Lead Molecules for Treatment of Colon Cancer.

Both members of the aldo-keto reductases (AKRs) family, AKR1B1 and AKR1B10, are over-expressed in various type of cancer, making them potential targets for inflammation-mediated cancers such as colon, lung, breast, and prostate cancers. This is the first comprehensive study which focused on the identification of phenylcarbamoylazinane-1, 2,4-triazole amides (7a−o) as the inhibitors of aldo-keto reductases (AKR1B1, AKR1B10) via detailed computational analysis. Firstly, the stability and reactivity of compounds were determined by using the Guassian09 programme in which the density functional theory (DFT) calculations were performed by using the B3LYP/SVP level. Among all the derivatives, the 7d, 7e, 7f, 7h, 7j, 7k, and 7m were found chemically reactive. Then the binding interactions of the optimized compounds within the active pocket of the selected targets were carried out by using molecular docking software: AutoDock tools and Molecular operation environment (MOE) software, and during analysis, the Autodock (academic software) results were found to be reproducible, suggesting this software is best over the MOE (commercial software). The results were found in correlation with the DFT results, suggesting 7d as the best inhibitor of AKR1B1 with the energy value of −49.40 kJ/mol and 7f as the best inhibitor of AKR1B10 with the energy value of −52.84 kJ/mol. The other potent compounds also showed comparable binding energies. The best inhibitors of both targets were validated by the molecular dynamics simulation studies where the root mean square value of <2 along with the other physicochemical properties, hydrogen bond interactions, and binding energies were observed. Furthermore, the anticancer potential of the potent compounds was confirmed by cell viability (MTT) assay. The studied compounds fall into the category of drug-like properties and also supported by physicochemical and pharmacological ADMET properties. It can be suggested that the further synthesis of derivatives of 7d and 7f may lead to the potential drug-like molecules for the treatment of colon cancer associated with the aberrant expression of either AKR1B1 or AKR1B10 and other associated malignancies.

QSAR study of unsymmetrical aromatic disulfides as potent avian SARS-CoV main protease inhibitors using quantum chemical descriptors and statistical methods.

In silico research was executed on forty unsymmetrical aromatic disulfide derivatives as inhibitors of the SARS Coronavirus (SARS-CoV-1). Density functional theory (DFT) calculation with B3LYP functional employing 6-311 â€‹+ â€‹G(d,p) basis set was used to calculate quantum chemical descriptors. Topological, physicochemical and thermodynamic parameters were calculated using ChemOffice software. The dataset was divided randomly into training and test sets consisting of 32 and 8 compounds, respectively. In attempt to explore the structural requirements for bioactives molecules with significant anti-SARS-CoV activity, we have built valid and robust statistics models using QSAR approach. Hundred linear pentavariate and quadrivariate models were established by changing training set compounds and further applied in test set to calculate predicted IC50 values of compounds. Both built models were individually validated internally as well as externally along with Y-Randomization according to the OECD principles for the validation of QSAR model and the model acceptance criteria of Golbraikh and Tropsha's. Model 34 is chosen with higher values of R2, R2 test and Q2cv (R2 â€‹= â€‹0.838, R2 test â€‹= â€‹0.735, Q2 cv â€‹= â€‹0.757). It is very important to notice that anti-SARS-CoV main protease of these compounds appear to be mainly governed by five descriptors, i.e. highest occupied molecular orbital energy (EHOMO), energy of molecular orbital below HOMO energy (EHOMO-1), Balaban index (BI), bond length between the two sulfur atoms (S1S2) and bond length between sulfur atom and benzene ring (S2Bnz). Here the possible action mechanism of these compounds was analyzed and discussed, in particular, important structural requirements for great SARS-CoV main protease inhibitor will be by substituting disulfides with smaller size electron withdrawing groups. Based on the best proposed QSAR model, some new compounds with higher SARS-CoV inhibitors activities have been designed. Further, in silico prediction studies on ADMET pharmacokinetics properties were conducted.

CADD Methods for Developing Novel Compounds Synthesized to Inhibit Tyrosine Kinase Receptors.

Growth factors and their receptor tyrosine kinases play a central role in regulating vital cellular processes such as proliferation, differentiation, division, and cell survival, and they are closely associated with the development of various types of cancer, particularly in the context of angiogenesis. Although several small chemical compounds targeting tyrosine kinase receptors have been approved by the FDA for cancer treatment by inhibiting angiogenesis, there is still a need for more effective medications. in silico studies are now crucial tools for the design of new drugs, offering considerable advantages such as cost and time reduction. In this review, we examined recent in silico research carried out between 2022 and 2024, focusing on new drug candidates synthesized to fight cancer, in particular by targeting tyrosine kinase receptors involved in the process of angiogenesis.

Advancements in Computational Approaches for Antidiabetic Drug Discovery: A Review.

Diabetes mellitus (DM) manifests as a complex and chronic metabolic disorder, posing a significant threat to global public health and contributing substantially to mortality rates. It is characterized by elevated blood glucose levels or hyperglycemia and requires effective preventive and therapeutic strategies. One promising approach involves targeting the inhibition of α- glucosidase and α-amylase, key enzymes responsible for carbohydrate hydrolysis. Inhibiting these enzymes proves beneficial in reducing postprandial glucose levels and mitigating postprandial hyperglycemia. However, existing antidiabetic medications are associated with undesirable side effects, highlighting the need to develop new molecules with increased efficacy and reduced side effects. Traditional methods for designing such molecules are often lengthy and costly. To address this, computer-based molecular modeling tools offer a promising approach to evaluate the antidiabetic activities of chemical compounds. This review aims to compile information on chemical compounds assessed for their anti-diabetic activities through molecular modeling, with a particular focus on the period from 2020 to 2023.

Design of New Aromatic Tertiary Amine-based as Butyrylcholinesterase Inhibitors Relying on Molecular Docking, ADME-Tox and Molecular Dynamics.

INTRODUCTION: Butyrylcholinesterase (BChE) plays a pivotal role in the progression of Alzheimer's disease. Empirical research demonstrated a fundamental alteration in the role of BChE concerning the reduction of cholinergic neurotransmission within the brains of individuals at advanced stages of Alzheimer's. METHOD: This study focuses on developing potent inhibitors for Butyrylcholinesterase (BChE) in the context of Alzheimer's disease (AD) treatment. Building upon previous research, a series of 44 aromatic tertiary amine-based compounds was investigated. Starting with ADME-Tox studies, the pharmacokinetic and pharmacodynamic properties of the compounds were analyzed to select promising candidates for BChE inhibition, which is a crucial factor in AD pathology. RESULTS: Molecular docking analyses identified compound M18 as the most promising candidate, and further compounds (X9 and X10) were proposed based on M18's chemical structure. These compounds displayed superior properties in terms of binding energies and hydrogen bonds in comparison to M18. CONCLUSION: The Molecular Dynamics (MD) simulations, which are over a 500 ns timeframe, confirmed the conformational stability of compounds X9 and X10, compared to M18. Overall, the stated results suggest that the proposed compounds, including X9 and X10 specifically, have a significant potential as candidates for BChE inhibition. This presents a promising avenue for therapeutic intervention in Alzheimer's disease.

An In Silico Study Based on QSAR and Molecular Docking and Molecular Dynamics Simulation for the Discovery of Novel Potent Inhibitor against AChE.

Acetylcholinesterase (AChE) is one of the main drug targets for treating Alzheimer's disease. This current study relies on multiple molecular modeling approaches to develop new potent inhibitors of AChE. We explored a 2D QSAR study using the statistical method of multiple linear regression based on a set of substituted 5-phenyl-1,3,4-oxadiazole and N-benzylpiperidine analogs, which were recently synthesized and proved their inhibitory activities against acetylcholinesterase (AChE). The molecular descriptors, polar surface area, dipole moment, and molecular weight are the key structural properties governing AChE inhibition activity. The MLR model was selected based on its statistical parameters: R2 = 0.701, R2test = 0.76, Q2CV = 0.638, and RMSE = 0.336, demonstrating its predictive reliability. Randomization tests, VIF tests, and applicability domain tests were adopted to verify the model's robustness. As a result, 11 new molecules were designed with higher anti-Alzheimer's activities than the model molecule. We demonstrated their improved pharmacokinetic properties through an in silico ADMET study. A molecular docking study was conducted to explore their AChE inhibition mechanisms and binding affinities in the active site. The binding scores of compounds M1, M2, and M6 were (-12.6 kcal/mol), (-13 kcal/mol), and (-12.4 kcal/mol), respectively, which are higher than the standard inhibitor Donepezil with a binding score of (-10.8 kcal/mol). Molecular dynamics simulations over 100 ns were used to validate the molecular docking results, indicating that compounds M1 and M2 remain stable in the active site, confirming their potential as promising anti-AChE inhibitors.

Targeting Fructosamine Oxidase (Amadoriase II) in Aspergillus fumigatus: Comprehensive Virtual Screening, ADMET Analysis, and Molecular Dynamics Simulation of Triazole Derivatives.

INTRODUCTION: Aspergillus fumigatus, a significant fungal pathogen, poses a threat to human health, especially in immunocompromised individuals. Addressing the need for novel antifungal strategies, this study employs virtual screening to identify potential inhibitors of Fructosamine oxidase, also known as Amadoriase II, a crucial enzyme in A. fumigatus (PDB ID: 3DJE). METHOD: Virtual screening of 81,197 triazole derivatives was subjected to computational analysis, aiming to pinpoint molecules with high binding affinity to the active site of Fructosamine oxidase. Subsequently, an in-depth ADMET analysis assessed the pharmacokinetic properties of lead compounds, ensuring their viability for further development. Molecular dynamics simulations were performed to evaluate the stability of top-ranked compounds over time. RESULTS: The results unveil a subset of triazole derivatives displaying promising interactions, suggesting their potential as inhibitors for further investigation. CONCLUSION: This approach contributes to the development of targeted antifungal agents, offering a rational starting point for experimental validation and drug development against Aspergillus fumigatus infections.

Integrated Exploration of Pyranocoumarin Derivatives as Synergistic Inhibitors of Dual-target for Mpro and PLpro Proteins of SARS-CoV-2 through Molecular Docking, ADMET Analysis, and Molecular Dynamics Simulation.

AIMS: This study aimed to explore the potential of natural anticoagulant compounds as synergistic inhibitors of the main protease (Mpro) and papain-like protease (PLpro) of SARS-CoV-2 and find effective therapies against SARS-CoV-2 by investigating the inhibitory effects of natural anticoagulant compounds on key viral proteases. OBJECTIVE: The objectives of this study were to conduct rigorous virtual screening and molecular docking analyses to evaluate the binding affinities and interactions of selected anticoagulant compounds with Mpro and PLpro, to assess the pharmacokinetic and pharmacodynamic profiles of the compounds to determine their viability for therapeutic use, and to employ molecular dynamics simulations to understand the stability of the identified compounds over time. METHODS: In this study, a curated collection of natural anticoagulant compounds was conducted. Virtual screening and molecular docking analyses were performed to assess binding affinities and interactions with Mpro and PLpro. Furthermore, pharmacokinetic and pharmacodynamic analyses were carried out to evaluate absorption, distribution, metabolism, and excretion profiles. Molecular dynamics simulations were performed to elucidate compound stability. RESULTS: Natural compounds exhibiting significant inhibitory activity against Mpro and PLpro were identified. A dual-target approach was established as a promising strategy for attenuating viral replication and addressing coagulopathic complications associated with SARS-CoV-2 infection. CONCLUSION: The study lays a solid foundation for experimental validation and optimization of identified compounds, potentially leading to the development of precise treatments for SARS-CoV-2.

Exploration of alpha-glucosidase inhibitors: A comprehensive in silico approach targeting a large set of triazole derivatives.

BACKGROUND: The increasing prevalence of diabetes and the side effects associated with current medications necessitate the development of novel candidate drugs targeting alpha-glucosidase as a potential treatment option. METHODS: This study employed computer-aided drug design techniques to identify potential alpha-glucosidase inhibitors from the PubChem database. Molecular docking was used to evaluate 81,197 compounds, narrowing the set for further analysis and providing insights into ligand-target interactions. An ADMET study assessed the pharmacokinetic properties of these compounds, including absorption, distribution, metabolism, excretion, and toxicity. Molecular dynamics simulations validated the docking results. RESULTS: 9 compounds were identified as potential candidate drugs based on their ability to form stable complexes with alpha-glucosidase and their favorable pharmacokinetic profiles, three of these compounds were subjected to the molecular dynamics, which showed stability throughout the entire 100 ns simulation. CONCLUSION: These findings suggest promising new alpha-glucosidase inhibitors for diabetes treatment. Further validation through in vitro and in vivo studies is recommended to confirm their efficacy and safety.

A comprehensive computational study to explore promising natural bioactive compounds targeting glycosyltransferase MurG in Escherichia coli for potential drug development.

Peptidoglycan is a carbohydrate with a cross-linked structure that protects the cytoplasmic membrane of bacterial cells from damage. The mechanism of peptidoglycan biosynthesis involves the main synthesizing enzyme glycosyltransferase MurG, which is known as a potential target for antibiotic therapy. Many MurG inhibitors have been recognized as MurG targets, but high toxicity and drug-resistant Escherichia coli strains remain the most important problems for further development. In addition, the discovery of selective MurG inhibitors has been limited to the synthesis of peptidoglycan-mimicking compounds. The present study employed drug discovery, such as virtual screening using molecular docking, drug likeness ADMET proprieties predictions, and molecular dynamics (MD) simulation, to identify potential natural products (NPs) for Escherichia coli. We conducted a screening of 30,926 NPs from the NPASS database. Subsequently, 20 of these compounds successfully passed the potency, pharmacokinetic, ADMET screening assays, and their validation was further confirmed through molecular docking. The best three hits and the standard were chosen for further MD simulations up to 400 ns and energy calculations to investigate the stability of the NPs-MurG complexes. The analyses of MD simulations and total binding energies suggested the higher stability of NPC272174. The potential compounds can be further explored in vivo and in vitro for promising novel antibacterial drug discovery.

Design of new small molecules derived from indolin-2-one as potent TRKs inhibitors using a computer-aided drug design approach.

Tropomyosin receptor kinase (TRKs) enzymes are responsible for cancers associated with the neurotrophic tyrosine kinase receptor gene fusion and are identified as effective targets for anticancer drug discovery. A series of small-molecule indolin-2-one derivatives showed remarkable biological activity against TRKs enzymatic activity. These small molecules could have an excellent profile for pharmaceutical application in the treatment of cancers caused by TRKs activity. The aim of this study is to modify the structure of these molecules to obtain new molecules with improved TRK inhibitory activity and pharmacokinetic properties favorable to the design of new drugs. Based on these series, we carried out a 3D-QSAR study. As a result, robust and reliable CoMFA and CoMSIA models are developed and applied to the design of 11 new molecules. These new molecules have a biological activity superior to the most active molecule in the starting series. The eleven designed molecules are screened using drug-likeness, ADMET proprieties, molecular docking, and MM-GBSA filters. The results of this screening identified the T1, T3, and T4 molecules as the best candidates for strong inhibition of TRKs enzymatic activity. In addition, molecular dynamics simulations are performed for TRK free and complexed with ligands T1, T3, and T4 to evaluate the stability of ligand-protein complexes over the simulation time. On the other hand, we proposed experimental synthesis routes for these newly designed molecules. Finally, the designed molecules T1, T2, and T3 have great potential to become reliable candidates for the conception of new drug inhibitors of TRKs.Communicated by Ramaswamy H. Sarma.