Synthesis, molecular modelling, and biological evaluation of novel quinoxaline derivatives for treating type II diabetes.

Quinoxalines are benzopyrazine derivatives with significant therapeutic impact in the pharmaceutical industry. They proved to be useful against inflammation, bacterial, fungal, viral infection, diabetes and other applications. Very recently, in January 2024, the FDA approved new quinoxaline containing drug, erdafitinib for treatment of certain carcinomas. Despite the diverse biological activities exhibited by quinoxaline derivatives and the role of secretory phospholipase A2 (sPLA2) in diabetes-related complications, the potential of sPLA2-targeting quinoxaline-based inhibitors to effectively address these complications remains unexplored. Therefore, we designed novel sPLA2- and α-glucosidase-targeting quinoxaline-based heterocyclic inhibitors to regulate elevated post-prandial blood glucose linked to patients with diabetes-related cardiovascular complications. Compounds 5a-d and 6a-d were synthesised by condensing quinoxaline hydrazides with various aryl sulphonyl chlorides. Biological screening revealed compound 6a as a potent sPLA2 inhibitor (IC50 = 0.0475 µM), whereas compound 6c most effectively inhibited α-glucosidase (IC50 = 0.0953 µM), outperforming the positive control acarbose. Moreover, compound 6a was the best inhibitor for both enzymes. Molecular docking revealed pharmacophoric features, highlighting the importance of a sulfonohydrazide moiety in the structural design of these compounds, leading to the development of potent sPLA2 and α-glucosidase inhibitors. Collectively, our findings helped identify promising candidates for developing novel therapeutic agents for treating diabetes mellitus.

A small, focused library comprising 8 novel compounds was synthesised using a series of substituted quinoxaline sulfonohydrazide derivatives.All synthesised compounds were tested against phospholipase A2 (sPLA2) and α-glucosidase enzymes.The compounds exhibited activities against α-glucosidase and were potent at nanomolar concentrations against sPLA2 isozymes.Structure-based molecular modelling was employed to rationalise the SAR of the compounds.

Pharmacophore Synergism in Diverse Scaffold Clinches in Aurora Kinase B.

Aurora kinase B (AKB) is a crucial signaling kinase with an important role in cell division. Therefore, inhibition of AKB is an attractive approach to the treatment of cancer. In the present work, extensive quantitative structure-activity relationships (QSAR) analysis has been performed using a set of 561 structurally diverse aurora kinase B inhibitors. The Organization for Economic Cooperation and Development (OECD) guidelines were used to develop a QSAR model that has high statistical performance (R2tr = 0.815, Q2LMO = 0.808, R2ex = 0.814, CCCex = 0.899). The seven-variable-based newly developed QSAR model has an excellent balance of external predictive ability (Predictive QSAR) and mechanistic interpretation (Mechanistic QSAR). The QSAR analysis successfully identifies not only the visible pharmacophoric features but also the hidden features. The analysis indicates that the lipophilic and polar groups-especially the H-bond capable groups-must be present at a specific distance from each other. Moreover, the ring nitrogen and ring carbon atoms play important roles in determining the inhibitory activity for AKB. The analysis effectively captures reported as well as unreported pharmacophoric features. The results of the present analysis are also supported by the reported crystal structures of inhibitors bound to AKB.

Mesoporous Silica Modified with 2-Phenylimidazo[1,2-a] pyridine-3-carbaldehyde as an Effective Adsorbent for Cu(II) from Aqueous Solutions: A Combined Experimental and Theoretical Study.

In this study, we will present an efficient and selective adsorbent for the removal of Cu(II) ions from aqueous solutions. The silica-based adsorbent is functionalized by 2-phenylimidazo[1,2-a] pyridine-3-carbaldehyde (SiN-imd-py) and the characterization was carried out by applying various techniques including FT-IR, SEM, TGA and elemental analysis. The SiN-imd-py adsorbent shows a good selectivity and high adsorption capacity towards Cu(II) and reached 100 mg/g at pH = 6 and T = 25 °C. This adsorption capacity is important compared to other similar adsorbents which are currently published. The adsorption mechanism, thermodynamics, reusability and the effect of different experimental conditions, such as contact time, pH and temperature, on the adsorption process, were also investigated. In addition, a theoretical study was carried out to understand the adsorption mechanism and the active sites of the adsorbent, as well as the stability of the complex formed and the nature of the bonds.

QSAR, Molecular Docking, MD Simulation and MMGBSA Calculations Approaches to Recognize Concealed Pharmacophoric Features Requisite for the Optimization of ALK Tyrosine Kinase Inhibitors as Anticancer Leads.

ALK tyrosine kinase ALK TK is an important target in the development of anticancer drugs. In the present work, we have performed a QSAR analysis on a dataset of 224 molecules in order to quickly predict anticancer activity on query compounds. Double cross validation assigns an upward plunge to the genetic algorithm−multi linear regression (GA-MLR) based on robust univariate and multivariate QSAR models with high statistical performance reflected in various parameters like, fitting parameters; R2 = 0.69−0.87, F = 403.46−292.11, etc., internal validation parameters; Q2LOO = 0.69−0.86, Q2LMO = 0.69−0.86, CCCcv = 0.82−0.93, etc., or external validation parameters Q2F1 = 0.64−0.82, Q2F2 = 0.63−0.82, Q2F3 = 0.65−0.81, R2ext = 0.65−0.83 including RMSEtr < RMSEcv. The present QSAR evaluation successfully identified certain distinct structural features responsible for ALK TK inhibitory potency, such as planar Nitrogen within four bonds from the Nitrogen atom, Fluorine atom within five bonds beside the non-ring Oxygen atom, lipophilic atoms within two bonds from the ring Carbon atoms. Molecular docking, MD simulation, and MMGBSA computation results are in consensus with and complementary to the QSAR evaluations. As a result, the current study assists medicinal chemists in prioritizing compounds for experimental detection of anticancer activity, as well as their optimization towards more potent ALK tyrosine kinase inhibitor.

QSAR Evaluations to Unravel the Structural Features in Lysine-Specific Histone Demethylase 1A Inhibitors for Novel Anticancer Lead Development Supported by Molecular Docking, MD Simulation and MMGBSA.

Using 84 structurally diverse and experimentally validated LSD1/KDM1A inhibitors, quantitative structure-activity relationship (QSAR) models were built by OECD requirements. In the QSAR analysis, certainly significant and understated pharmacophoric features were identified as critical for LSD1 inhibition, such as a ring Carbon atom with exactly six bonds from a Nitrogen atom, partial charges of lipophilic atoms within eight bonds from a ring Sulphur atom, a non-ring Oxygen atom exactly nine bonds from the amide Nitrogen, etc. The genetic algorithm-multi-linear regression (GA-MLR) and double cross-validation criteria were used to create robust QSAR models with high predictability. In this study, two QSAR models were developed, with fitting parameters like R2 = 0.83-0.81, F = 61.22-67.96, internal validation parameters such as Q2LOO = 0.79-0.77, Q2LMO = 0.78-0.76, CCCcv = 0.89-0.88, and external validation parameters such as, R2ext = 0.82 and CCCex = 0.90. In terms of mechanistic interpretation and statistical analysis, both QSAR models are well-balanced. Furthermore, utilizing the pharmacophoric features revealed by QSAR modelling, molecular docking experiments corroborated with the most active compound's binding to the LSD1 receptor. The docking results are then refined using Molecular dynamic simulation and MMGBSA analysis. As a consequence, the findings of the study can be used to produce LSD1/KDM1A inhibitors as anticancer leads.

Identification of potent aldose reductase inhibitors as antidiabetic (Anti-hyperglycemic) agents using QSAR based virtual Screening, molecular Docking, MD simulation and MMGBSA approaches.

The aldose reductase (AR) enzyme is an important target enzyme in the development of therapeutics against hyperglycaemia induced health complications such as retinopathy, etc. In the present study, a quantitative structure activity relationship (QSAR) evaluation of a dataset of 226 reported AR inhibitor (ARi) molecules is performed using a genetic algorithm - multi linear regression (GA-MLR) technique. Multi-criteria decision making (MCDM) analysis furnished two five variables based QSAR models with acceptably high performance reflected in various statistical parameters such as, R2 = 0.79-0.80, Q2 LOO = 0.78-0.79, Q2 LMO = 0.78-0.79. The QSAR model analysis revealed some of the molecular features that play crucial role in deciding inhibitory potency of the molecule against AR such as; hydrophobic Nitrogen within 2 Å of the center of mass of the molecule, non-ring Carbon separated by three and four bonds from hydrogen bond donor atoms, number of sp2 hybridized Oxygen separated by four bonds from sp2 hybridized Carbon atoms, etc. 14 in silico generated hits, using a compound 18 (a most potent ARi from present dataset with pIC50 = 8.04 M) as a template, on QSAR based virtual screening (QSAR-VS) furnished a scaffold 5 with better ARi activity (pIC50 = 8.05 M) than template compound 18. Furthermore, molecular docking of compound 18 (Docking Score = -7.91 kcal/mol) and scaffold 5 (Docking Score = -8.08 kcal/mol) against AR, divulged that they both occupy the specific pocket(s) in AR receptor binding sites through hydrogen bonding and hydrophobic interactions. Molecular dynamic simulation (MDS) and MMGBSA studies right back the docking results by revealing the fact that binding site residues interact with scaffold 5 and compound 18 to produce a stable complex similar to co-crystallized ligand's conformation. The QSAR analysis, molecular docking, and MDS results are all in agreement and complementary. QSAR-VS successfully identified a more potent novel ARi and can be used in the development of therapeutic agents to treat diabetes.

Identification of Food Compounds as inhibitors of SARS-CoV-2 main protease using molecular docking and molecular dynamics simulations.

SARS-CoV-2 has rapidly emerged as a global pandemic with high infection rate. At present, there is no drug available for this deadly disease. Recently, Mpro (Main Protease) enzyme has been identified as essential proteins for the survival of this virus. In the present work, Lipinski's rules and molecular docking have been performed to identify plausible inhibitors of Mpro using food compounds. For virtual screening, a database of food compounds was downloaded and then filtered using Lipinski's rule of five. Then, molecular docking was accomplished to identify hits using Mpro protein as the target enzyme. This led to identification of a Spermidine derivative as a hit. In the next step, Spermidine derivatives were collected from PubMed and screened for their binding with Mpro protein. In addition, molecular dynamic simulations (200 ns) were executed to get additional information. Some of the compounds are found to have strong affinity for Mpro, therefore these hits could be used to develop a therapeutic agent for SARS-CoV-2.

Mechanistic and Predictive QSAR Analysis of Diverse Molecules to Capture Salient and Hidden Pharmacophores for Anti-Thrombotic Activity.

Thrombosis is a life-threatening disease with a high mortality rate in many countries. Even though anti-thrombotic drugs are available, their serious side effects compel the search for safer drugs. In search of a safer anti-thrombotic drug, Quantitative Structure-Activity Relationship (QSAR) could be useful to identify crucial pharmacophoric features. The present work is based on a larger data set comprising 1121 diverse compounds to develop a QSAR model having a balance of acceptable predictive ability (Predictive QSAR) and mechanistic interpretation (Mechanistic QSAR). The developed six parametric model fulfils the recommended values for internal and external validation along with Y-randomization parameters such as R2tr = 0.831, Q2LMO = 0.828, R2ex = 0.783. The present analysis reveals that anti-thrombotic activity is found to be correlated with concealed structural traits such as positively charged ring carbon atoms, specific combination of aromatic Nitrogen and sp2-hybridized carbon atoms, etc. Thus, the model captured reported as well as novel pharmacophoric features. The results of QSAR analysis are further vindicated by reported crystal structures of compounds with factor Xa. The analysis led to the identification of useful novel pharmacophoric features, which could be used for future optimization of lead compounds.

QSAR and Pharmacophore Modeling of Nitrogen Heterocycles as Potent Human N-Myristoyltransferase (Hs-NMT) Inhibitors.

N-myristoyltransferase (NMT) is an important eukaryotic monomeric enzyme which has emerged as an attractive target for developing a drug for cancer, leishmaniasis, ischemia-reperfusion injury, malaria, inflammation, etc. In the present work, statistically robust machine leaning models (QSAR (Quantitative Structure-Activity Relationship) approach) for Human NMT (Hs-NMT) inhibitory has been performed for a dataset of 309 Nitrogen heterocycles screened for NMT inhibitory activity. Hundreds of QSAR models were derived. Of these, the model 1 and 2 were chosen as they not only fulfil the recommended values for a good number of validation parameters (e.g., R2 = 0.77-0.79, Q2LMO = 0.75-0.76, CCCex = 0.86-0.87, Q2-F3 = 0.74-0.76, etc.) but also provide useful insights into the structural features that sway the Hs-NMT inhibitory activity of Nitrogen heterocycles. That is, they have an acceptable equipoise of descriptive and predictive qualities as per Organisation for Economic Co-operation and Development (OECD) guidelines. The developed QSAR models identified a good number of molecular descriptors like solvent accessible surface area of all atoms having specific partial charge, absolute surface area of Carbon atoms, etc. as important features to be considered in future optimizations. In addition, pharmacophore modeling has been performed to get additional insight into the pharmacophoric features, which provided additional results.

Synthesis and Characterization of Some New Quinoxalin-2(1H)one and 2-Methyl-3H-quinazolin-4-one Derivatives Targeting the Onset and Progression of CRC with SAR, Molecular Docking, and ADMET Analyses.

The pathogenesis of colorectal cancer is a multifactorial process. Dysbiosis and the overexpression of COX-2 and LDHA are important effectors in the initiation and development of the disease through chromosomal instability, PGE2 biosynthesis, and induction of the Warburg effect, respectively. Herein, we report the in vitro testing of some new quinoxalinone and quinazolinone Schiff's bases as: antibacterial, COX-2 and LDHA inhibitors, and anticolorectal agents on HCT-116 and LoVo cells. Moreover, molecular docking and SAR analyses were performed to identify the structural features contributing to the biological activities. Among the synthesized molecules, the most active cytotoxic agent, (6d) was also a COX-2 inhibitor. In silico ADMET studies predicted that (6d) would have high Caco-2 permeability, and %HIA (99.58%), with low BBB permeability, zero hepatotoxicity, and zero risk of sudden cardiac arrest, or mutagenicity. Further, (6d) is not a potential P-gp substrate, instead, it is a possible P-gpI and II inhibitor, therefore, it can prevent or reverse the multidrug resistance of the anticancer drugs. Collectively, (6d) can be considered as a promising lead suitable for further optimization to develop anti-CRC agents or glycoproteins inhibitors.

Structure features of peptide-type SARS-CoV main protease inhibitors: Quantitative structure activity relationship study.

In the present work, an extensive QSAR (Quantitative Structure Activity Relationships) analysis of a series of peptide-type SARS-CoV main protease (MPro) inhibitors following the OECD guidelines has been accomplished. The analysis was aimed to identify salient and concealed structural features that govern the MPro inhibitory activity of peptide-type compounds. The QSAR analysis is based on a dataset of sixty-two peptide-type compounds which resulted in the generation of statistically robust and highly predictive multiple models. All the developed models were validated extensively and satisfy the threshold values for many statistical parameters (for e.g. R2 â€‹= â€‹0.80-0.82, Q2 loo â€‹= â€‹0.74-0.77, Q 2 LMO  â€‹= â€‹0.66-0.67). The developed QSAR models identified number of sp2 hybridized Oxygen atoms within seven bonds from aromatic Carbon atoms, the presence of Carbon and Nitrogen atoms at a topological distance of 3 and other interrelations of atom pairs as important pharmacophoric features. Hence, the present QSAR models have a good balance of Qualitative (Descriptive QSARs) and Quantitative (Predictive QSARs) approaches, therefore useful for future modifications of peptide-type compounds for anti- SARS-CoV activity.

Synthesis, Antiphospholipase A2, Antiprotease, Antibacterial Evaluation and Molecular Docking Analysis of Certain Novel Hydrazones.

Some novel hydrazone derivatives 6a-o were synthesized from the key intermediate 4-Chloro-N-(2-hydrazinocarbonyl-phenyl)-benzamide 5 and characterized using IR, ¹H-NMR, 13C-NMR, mass spectroscopy and elemental analysis. The inhibitory potential against two secretory phospholipase A2 (sPLA2), three protease enzymes and eleven bacterial strains were evaluated. The results revealed that all compounds showed preferential inhibition towards hGIIA isoform of sPLA2 rather than DrG-IB with compounds 6l and 6e being the most active. The tested compounds exhibited excellent antiprotease activity against proteinase K and protease from Bacillus sp. with compound 6l being the most active against both enzymes. Furthermore, the maximum zones of inhibition against bacterial growth were exhibited by compounds; 6a, 6m, and 6o against P. aeruginosa; 6a, 6b, 6d, 6f, 6l, 6m, 6n, and 6o against Serratia; 6k against S. mutans; and compounds 6a, 6d, 6e, 6m, and 6n against E. feacalis. The docking simulations of hydrazones 6a-o with GIIA sPLA2, proteinase K and hydrazones 6a-e with glutamine-fructose-6-phosphate transaminase were performed to obtain information regarding the mechanism of action.

Synthesis, X-ray diffraction, thermogravimetric and DFT analyses of pyrimidine derivatives.

An eco-benign synthesis of pyrimidine derivatives 2a,b containing different functional groups with different electronic character starting from nitroalkenes 1a and 2b has been described. The structures for 1a and 2a,b have been characterized by single crystal X-ray diffraction analysis. The thermal data of the molecules pointed towards important structural aspects of their stability. The mechanism of their thermal decomposition is discussed. The thermodynamic parameters of the dissociation steps were evaluated and discussed. DFT calculations reveal that the compound 1a possesses a high calculated dipole moment value (8.28 D) which indicates its high reactivity towards its surrounding molecules.

Leveraging nitrogen occurrence in approved drugs to identify structural patterns.

BACKGROUND: The process of drug development and discovery is costly and slow. Although an understanding of molecular design principles and biochemical processes has progressed, it is essential to minimize synthesis-testing cycles. An effective approach is to analyze key heteroatoms, including oxygen and nitrogen. Herein, we present an analysis focusing on the utilization of nitrogen atoms in approved drugs. RESEARCH DESIGN AND METHODS: The present work examines the frequency, distribution, prevalence, and diversity of nitrogen atoms in a dataset comprising 2,049 small molecules approved by different regulatory agencies (FDA and others). Various types of nitrogen atoms, such as sp3-, sp2-, sp-hybridized, planar, ring, and non-ring are included in this investigation. RESULTS: The results unveil both previously reported and newly discovered patterns of nitrogen atom distribution around the center of mass in the majority of drug molecules. CONCLUSIONS: This study has highlighted intriguing trends in the role of nitrogen atoms in drug design and development. The majority of drugs contain 1-3 nitrogen atoms within 5Å from the center of mass (COM) of a molecule, with a higher preference for the ring and planar nitrogen atoms. The results offer invaluable guidance for the multiparameter optimization process, thus significantly contributing toward the conversion of lead compounds into potential drug candidates.

In silico study to recognize novel angiotensin-converting-enzyme-I inhibitors by 2D-QSAR and constraint-based molecular simulations.

Cardiovascular diseases (CVD) such as heart failure, stroke, and hypertension affect 64.3 million people worldwide and are responsible for 30% of all deaths. Primary inhibition of the angiotensin-converting enzyme (ACE) is significant in the management of CVD. In the present study, the genetic algorithm-multiple linear regressions (GA-MLR) method is used to generate highly predictive and statistically significant (R2 = 0.70-0.75, Q2LOO=0.67-0.73, Q2LMO=0.66-0.72, CCCex=0.70-0.78) quantitative structure-activity relationships (QSAR) models conferring to OECD requirements using a dataset of 255 structurally diverse and experimentally validated ACE inhibitors. The models contain simply illustratable Padel, Estate, and PyDescriptors that correlate structural scaffold requisite for ACE inhibition. Also, constraint-based molecular docking reveals an interaction profile between ligands and enzymes which is then correlated with the essential structural features associated with the QSAR models. The QSAR-based virtual screening was utilized to find novel lead molecules from a designed database of 102 thiadiazole derivatives. The Applicability domain (AD), Molecular Docking, Molecular dynamics, and ADMET analysis suggest two compound D24 and D40 are inflexibly linked to the protein binding site and follows drug-likeness properties.Communicated by Ramaswamy H. Sarma.

Multi-Target In-Silico modeling strategies to discover novel angiotensin converting enzyme and neprilysin dual inhibitors.

Cardiovascular diseases, including heart failure, stroke, and hypertension, affect 608 million people worldwide and cause 32% of deaths. Combination therapy is required in 60% of patients, involving concurrent Renin-Angiotensin-Aldosterone-System (RAAS) and Neprilysin inhibition. This study introduces a novel multi-target in-silico modeling technique (mt-QSAR) to evaluate the inhibitory potential against Neprilysin and Angiotensin-converting enzymes. Using both linear (GA-LDA) and non-linear (RF) algorithms, mt-QSAR classification models were developed using 983 chemicals to predict inhibitory effects on Neprilysin and Angiotensin-converting enzymes. The Box-Jenkins method, feature selection method, and machine learning algorithms were employed to obtain the most predictive model with ~ 90% overall accuracy. Additionally, the study employed virtual screening of designed scaffolds (Chalcone and its analogues, 1,3-Thiazole, 1,3,4-Thiadiazole) applying developed mt-QSAR models and molecular docking. The identified virtual hits underwent successive filtration steps, incorporating assessments of drug-likeness, ADMET profiles, and synthetic accessibility tools. Finally, Molecular dynamic simulations were then used to identify and rank the most favourable compounds. The data acquired from this study may provide crucial direction for the identification of new multi-targeted cardiovascular inhibitors.

Estrogen Receptor Alpha Binders for Hormone-Dependent Forms of Breast Cancer: e-QSAR and Molecular Docking Supported by X-ray Resolved Structures.

Cancer, a life-disturbing and lethal disease with a high global impact, causes significant economic, social, and health challenges. Breast cancer refers to the abnormal growth of cells originating from breast tissues. Hormone-dependent forms of breast cancer, such as those influenced by estrogen, prompt the exploration of estrogen receptors as targets for potential therapeutic interventions. In this study, we conducted e-QSAR molecular docking and molecular dynamics analyses on a diverse set of inhibitors targeting estrogen receptor alpha (ER-α). The e-QSAR model is based on a genetic algorithm combined with multilinear regression analysis. The newly developed model possesses a balance between predictive accuracy and mechanistic insights adhering to the OECD guidelines. The e-QSAR model pointed out that sp2-hybridized carbon and nitrogen atoms are important atoms governing binding profiles. In addition, a specific combination of H-bond donors and acceptors with carbon, nitrogen, and ring sulfur atoms also plays a crucial role. The results are supported by molecular docking, MD simulations, and X-ray-resolved structures. The novel results could be useful for future drug development for ER-α.

Theranostics: a multifaceted approach utilizing nano-biomaterials.

Biomaterials play a vital role in targeting therapeutics. Over the years, several biomaterials have gained wide attention in the treatment and diagnosis of diseases. Scientists are trying to make more personalized treatments for different diseases, as well as discovering novel single agents that can be used for prognosis, medication administration, and keeping track of how a treatment works. Theranostics based on nano-biomaterials have higher sensitivity and specificity for disease management than conventional techniques. This review provides a concise overview of various biomaterials, including carbon-based materials like fullerenes, graphene, carbon nanotubes (CNTs), and carbon nanofibers, and their involvement in theranostics of different diseases. In addition, the involvement of imaging techniques for theranostics applications was overviewed. Theranostics is an emerging strategy that has great potential for enhancing the accuracy and efficacy of medicinal interventions. Despite the presence of obstacles such as disease heterogeneity, toxicity, reproducibility, uniformity, upscaling production, and regulatory hurdles, the field of medical research and development has great promise due to its ability to provide patients with personalised care, facilitate early identification, and enable focused treatment.

QSAR modeling approaches to identify a novel ACE2 inhibitor that selectively bind with the C and N terminals of the ectodomain.

Due to the high rates of drug development failure and the massive expenses associated with drug discovery, repurposing existing drugs has become more popular. As a result, we have used QSAR modelling on a large and varied dataset of 657 compounds in an effort to discover both explicit and subtle structural features requisite for ACE2 inhibitory activity, with the goal of identifying novel hit molecules. The QSAR modelling yielded a statistically robust QSAR model with high predictivity (R2tr=0.84, R2ex=0.79), previously undisclosed features, and novel mechanistic interpretations. The developed QSAR model predicted the ACE2 inhibitory activity (PIC50) of 1615 ZINC FDA compounds. This led to the detection of a PIC50 of 8.604 M for the hit molecule (ZINC000027990463). The hit molecule's docking score is -9.67 kcal/mol (RMSD 1.4). The hit molecule revealed 25 interactions with the residue ASP40, which defines the N and C termini of the ectodomain of ACE2. The HIT molecule conducted more than thirty contacts with water molecules and exhibited polar interaction with the ARG522 residue coupled with the second chloride ion, which is 10.4 nm away from the zinc ion. Both molecular docking and QSAR produced comparable findings. Moreover, MD simulation and MMGBSA studies verified docking analysis. The MD simulation showed that the hit molecule-ACE2 receptor complex is stable for 400 ns, suggesting that repurposed hit molecule 3 is a viable ACE2 inhibitor.

Unveiling dynamics of nitrogen content and selected nitrogen heterocycles in thrombin inhibitors: a ceteris paribus approach.

BACKGROUND: Despite the progress in comprehending molecular design principles and biochemical processes associated with thrombin inhibition, there is a crucial need to optimize efforts and curtail the recurrence of synthesis-testing cycles. Nitrogen and N-heterocycles are key features of many anti-thrombin drugs. Hence, a pragmatic analysis of nitrogen and N-heterocycles in thrombin inhibitors is important throughout the drug discovery pipeline. In the present work, the authors present an analysis with a specific focus on understanding the occurrence and distribution of nitrogen and selected N-heterocycles in the realm of thrombin inhibitors. RESEARCH DESIGN AND METHODS: A dataset comprising 4359 thrombin inhibitors is used to scrutinize various categories of nitrogen atoms such as ring, non-ring, aromatic, and non-aromatic. In addition, selected aromatic and aliphatic N-heterocycles have been analyzed. RESULTS: The analysis indicates that ~62% of thrombin inhibitors possess five or fewer nitrogen atoms. Substituted N-heterocycles have a high occurrence, like pyrrolidine (23.24%), pyridine (20.56%), piperidine (16.10%), thiazole (9.61%), imidazole (7.36%), etc. in thrombin inhibitors. CONCLUSIONS: The majority of active thrombin inhibitors contain nitrogen atoms close to 5 and a combination of N-heterocycles like pyrrolidine, pyridine, piperidine, etc. This analysis provides crucial insights to optimize the transformation of lead compounds into potential anti-thrombin inhibitors.