3D Quantitative Structure-activity Relationship Research Articles

Design of Novel Acat Inhibitors as Potent Anti-Hyperlipidemic Agents Using Chemometric Approaches

Aim: A 2D QSAR study of acyl-coenzyme A (CoA): cholesterol acyltransferase (ACAT) inhibitors revealed that electronic, topological, and steric properties are important structural features required for activity against ACAT. Background: In order to interpret the evidence encrypted by the molecular structure of the compounds, a standard physicochemical descriptors-centered, and Quantitative Structure-Activity Relationship (QSAR) approach was implemented on a data set of Indoline derivatives were reported to be acyl-coenzyme A (CoA): cholesterol acyltransferase ACAT inhibitors. Objective: The ACAT enzyme plays an important role in the absorption of dietary cholesterol. Therefore, the inhibition of ACAT is a key strategy or primary objective for the treatment of hypercholesterolemia and atherosclerosis. Method: Chemo metric models were designed by inserting a battery of statistical techniques in the current study that demonstrate the linear approaches of analysis, including multiple linear regression (MLR), partial least square PLS, and non-linear methods such as artificial neural networks (ANN). Result: The activity contributions of these molecules were analyzed through regression equation, and the best QSAR model was created with an excellent correlative and predictive ability. Significant statistical values S = 0.35, F = 60.30, r = 0.92, r² = 0.85, r² (CV) = 0.82 of the designed models were obtained using stepwise MLR and a comparable PLS and FFNN model with r² (CV) = 0.82, 0.88 and 0.86 respectively and the relevant descriptors like inertia moment 1 size, Kier Chiv4 (cluster) index, Kier Chiv6(ring) index offered important information regarding this model. Conclusion: The model reveals that inertia moment 1 size, Kier Chiv4 (cluster) index, and Kier Chiv6 (ring) index are prerequisite descriptors to determine other promising ACAT antagonists with high and liable potency against the target. Therefore, these characteristics may be used efficiently for the design and evaluation of active compounds as new ACAT inhibitors thanks to their utilization.

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.

Theoretical investigation of selective inhibitory activity of chromone-based compounds against monoamine oxidase (MAO)-A and -B

Monoamine oxidase (MAO) is crucial for the breakdown of monoamine neurotransmitters, making it a promising target for treating neurodegenerative disorders, such as depression, Alzheimer’s disease, and Parkinson’s disease. In this study, we investigated the selective inhibitory activity of chromone-based compounds against MAO-A and MAO-B for neurodegenerative disease treatment. In literary sources, thirty chromone derivatives have been identified as potential ligands for MAO-A and MAO-B inhibitors. We utilized molecular docking to evaluate how the most active compound interacted with the targeted MAO-A and MAO-B. Compound 2 g, the most active for MAO-A, demonstrated a lower CDOCKER energy compared to the co-crystallized ligand. Meanwhile, compound 2f, the most active for MAO-B, showed a CDOCKER energy similar to the co-crystallized ligand and exhibited similar binding patterns. Furthermore, we constructed a quantitative structure-activity relationship (QSAR) model to predict the properties and estimate IC50 values for 30 chromone derivatives functioning as MAO-A and MAO-B inhibitors. The model predictions were validated against experimental measurements. Our 2D QSAR model demonstrated robustness, with a statistically significant non-cross-validated coefficient (r2 < 0.9), cross-validated correlation coefficient (q2 < 0.6), and predictive squared correlation coefficient (r2 pred < 0.8). Additionally, MD simulations confirmed the stable binding of compounds 2 g and 2f with MAO-A and MAO-B, respectively, displaying substantial binding energy. The most effective pharmacophore model identified key features, such as hydrogen bond acceptors and hydrophobic interactions, that contribute significantly to inhibitory potency. This study offers valuable insight into the selection of compounds with improved selectivity for MAO inhibition.

Two and Three-Dimensional Quantitative Structure-Activity Relationship Analysis on A Series of Anthelmintics

A 2D and 3D quantitative structure activity relationship (QSAR) analysis has been performed on a data set of 29 bezothiazole derivatives as Anthelmintics. Several types of descriptors including topological, spatial, thermodynamic, information content and E-state indices have been used to derive a quantitative relationship between the Anthelmintics activity and structural properties. Statistically significant models was obtained with the descriptors The model was also tested successfully for external validation criteria. The model is not only able to predict the activity of new compounds but also explained the important regions in the molecules in quantitative manner

Aromatase as a novel target of parabens in human and rat placentas: 3D-quantitative structure-activity relationship and docking analysis

Aromatase (CYP19A1), a pivotal enzyme in the biosynthesis of estradiol from testosterone, is predominantly expressed in reproductive tissues including placentas. This study investigated the effects of paraben acid and nine parabens on the activity of human and rat CYP19A1 using microsomes derived from human and rat placentas and on estradiol secretion in human choriocarcinoma BeWo cells. The results showed that propyl, butyl, hexyl, heptyl, and nonyl parabens significantly inhibited human CYP19A1 activity, with IC50 values of 66.37, 61.08, 55.65, 48.26, and 27.24 μM, respectively. In BeWo cells, these parabens notably diminished estradiol secretion at concentrations of 100 μM. Similarly, rat CYP19A1 was inhibited by these parabens, with IC50 values of 98.07, 70.10, 41.30, 27.93, and 6.33 μM for propyl, butyl, hexyl, heptyl, and nonyl parabens, respectively. Kinetic analysis identified these compounds as mixed inhibitors. Bivariate correlation analysis revealed a negative correlation between the partition coefficient value, molecular weight, the number of carbon atoms in the alcohol moiety, as well as heavy atom number and IC50 values. Three-dimensional quantitative structure-activity relationship analysis highlighted the critical role of hydrophobic regions in determining inhibitory potency. Docking studies suggested that parabens interact with the heme-iron binding site of both human and rat CYP19A1. This study elucidates the inhibitory effects of various parabens on CYP19A1 and their binding mechanisms, thereby providing a deeper understanding of their potential impact on estrogen biosynthesis.

Integration of 3D-QSAR, molecular docking, and machine learning techniques for rational design of nicotinamide-based SIRT2 inhibitors

Selective inhibitors of sirtuin-2 (SIRT2) are increasingly recognized as potential therapeutics for cancer and neurodegenerative diseases. Derivatives of 5-((3-amidobenzyl)oxy)nicotinamides have been identified as some of the most potent and selective SIRT2 inhibitors reported to date (​Ai et al., 2016​; ​Ai et al., 2023​, ​Baroni et al., 2007​). In this study, a 3D-QSAR (3D-Quantitative Structure-Activity Relationship) model was developed using a dataset of 86 nicotinamide-based SIRT2 inhibitors from the literature, along with GRIND-derived pharmacophore models for selected inhibitors. External validation parameters emphasized the reliability of the 3D-QSAR model in predicting SIRT2 inhibition within the defined applicability domain. The interpretation of the 3D-QSAR model facilitated the generation of GRIND-derived pharmacophore models, which in turn enabled the design of novel SIRT2 inhibitors. Furthermore, based on molecular docking results for the SIRT1–3 isoforms, two classification models were developed: a SIRT1/2 model using the Naive Bayes algorithm and a SIRT2/3 model using the k-nearest neighbors algorithm, to predict the selectivity of inhibitors for SIRT1/2 and SIRT2/3. External validation parameters of the selectivity models confirmed their predictive power. Ultimately, the integration of 3D-QSAR, selectivity models and prediction of ADMET properties facilitated the identification of the most promising selective SIRT2 inhibitors for further development.

Machine learning approaches in designing anti-HIV nitroimidazoles: 2D/3D QSAR, kNN-MFA, docking, dynamics, PCA analysis and MMGBSA studies

In this study, newly synthesized 20 nitroimidazole derivatives were subjected to 2D and 3D Quantitative Structure-Activity Relationship (QSAR) study to investigate their anti-HIV activity against both ROD and IIIB strains. Later, proposed hypothesis was virtually proved by further in-silico studies. In statistically significant 2D-QSAR models r2 values for IIIB strains 0.9241 and for ROD strains 0.9412 with corresponding q2 values of 0.7706 and 0.8299, were obtained, respectively. Different models were constructed using three different kNN-MFA 3D QSAR approaches such as SW-FB, SA score, and GA. By using the generated hypothesis, newer analogues of nitroimidazole derivatives was designed and molecular modelling studies were conducted to prove the hypothesis. The three molecules were displayed the good docking scores compared to the reference molecule. The stabilities of docked complexes were analyzed by MD simulations and MMGB/SA calculations. These results offer insightful design guidance for novel anti-HIV compounds synthesis and suggest interesting directions for future pharmaceutical research.

Discovery of Small Molecule Inhibitors Targeting CTNNB1 (β-catenin) for Endometrial cancer: Employing 3D QSAR, Drug-Likeness Assessment, ADMET Predictions, Molecular Docking and Simulation.

Endometrial carcinoma (EC) is a type of cancer that originates in the lining of the uterus, known as the endometrium. It is associated with various treatment options such as surgery, radiation therapy, chemotherapy, and hormone therapy, each presenting unique challenges and limitations. Beta-catenin, a protein involved in the development and progression of several cancers, including EC, plays a crucial role. Abnormal beta-catenin signaling is often linked to the emergence of specific EC subtypes, affecting tumor growth and invasion. The study's objective is to identify compounds targeting the beta-catenin protein for treating endometrial cancer (EC) using in silico drug design. Our approach includes molecular docking to evaluate binding affinities, ADME profiling for pharmacokinetic properties, toxicity assessments, and molecular dynamics simulations to assess compound stability and interactions. Approximately one thousand anti-cancer phytochemicals were sourced from PubChem and subjected to molecular docking simulations against the beta-catenin protein. The compounds were evaluated based on their binding affinities, with the top five selected for further analysis. These five molecules underwent toxicity and ADME profiling. The Prediction of Activity Spectra for Substances (PASS) tool was used to identify compounds targeting CTNNB1. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were employed to establish quantitative structure-activity relationship (QSAR) models for the five CTNNB1 antagonist molecules. The selected five compounds, namely Pazopanib, Binimetinib, Telatinib, 4-(2,3-Dihydrobenzo[ b][1,4]dioxin-6-yl)-3-((5-nitrothiazol-2-yl)thio)-1H-1,2,4-triazol-5(4H)-one, and Ribavirin, demonstrated efficacy against CTNN1. MD simulations of the docked complexes confirmed the stability of these drugs in binding to the target protein. All five molecules showed promising safety and effectiveness profiles according to their ADME and toxicity evaluations. Through a comprehensive screening process employing in silico drug design methods, this study successfully identified five potential human anticancer drug candidates targeting the beta-catenin protein. These findings offer a foundation for further experimental validation and development towards the treatment of EC.

Identification of novel RANKL inhibitors through in silico analysis

Receptor activator of nuclear factor-κB ligand (RANKL) is considered the principal regulator of osteoclast differentiation. Therefore, strategies interfering with the RANKL-RANK signaling pathway may effectively inhibit osteoclast differentiation and mitigate bone resorption. Consequently, RANKL has become a promising target for new drug design strategies. Despite extensive research on specific drugs and antibodies, only a few have shown efficacy in treating osteoporosis. To address this challenge, we aimed to explore new approaches for designing drugs for osteoporosis. In this study, a 3D quantitative structure–activity relationship (QSAR) pharmacophore model was built for RANKL with reference to known inhibitor IC50 values. The optimal pharmacophore model was then employed as a 3D query to screen databases for novel lead compounds. The obtained compounds were subjected to ADMET and TOPKAT analyses to predict drug pharmacokinetics and toxicity. Molecular docking and de novo evolution approaches were applied to verify the docking binding affinities of the compounds. Five candidate compounds were subjected to further in vitro analyses to assess their anti-osteoporotic effects, among which compound 4 demonstrated significant inhibitory activity, achieving an inhibitory rate of 92.6 % on osteoclastogenesis at a concentration of 10 μM. Subsequent molecular dynamics (MD) simulations to assess the stability and behavior of compound 4 and its evolved variant, ZINC00059014397_Evo, within the RANKL binding site revealed that the variant is a potential therapeutic agent for targeting osteoclasts. This study offers valuable insights for developing next generation RANKL inhibitors for osteoporosis treatments.

Identification of tuberculosis inhibitors through QSAR-based virtual screening and molecular dynamics simulation of novel pyrimidine derivatives

Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), is the leading cause of death due to antimicrobial resistance, highlighting the urgency for innovative solutions and required the development of new drugs for TB treatment. In this study, we have conducted virtual screening and 2D quantitative structure activity relationship (2D-QSAR) models to analyze a set of fifty pyrimidine derivatives, aiming to uncover potential inhibitors for TB. The dataset is divided into a training set of thirty-eight molecules and a test set, using multiple linear regression (MLR). The key metrics such as R2 = 0.82, R2adj = 0.78, Ntest = 12, and R2test = 0.70, demonstrate the robustness of the built 2D-QSAR model. Leveraging the applicability domain of the model, using the Williams plot, databases of newer pyrimidine derivatives were created for drug-like property screening and activity prediction (pIC50) in TB treatment. Subsequently, molecular docking high-throughput virtual screening (HTVS), and dynamics simulations were employed to predict docking poses within Mtb kinases A and B (PDB: 6B2P). Detailed analysis revealed effective interactions between active amino acid sites in the Mtb pocket and the novel design drug molecules, sustaining the stability of their complexes.

Discovery of Novel Acethydrazide-Containing Flavonol Derivatives as Potential Antifungal Agents.

In this study, a series of novel hydrazide-containing flavonol derivatives was designed, synthesized, and evaluated for antifungal activity. In the in vitro antifungal assay, most of the target compounds exhibited potent antifungal activity against seven tested phytopathogenic fungi. In particular, compound C32 showed the best antifungal activity against Rhizoctonia solani (EC50 = 0.170 μg/mL), outperforming carbendazim (EC50 = 0.360 μg/mL) and boscalid (EC50 = 1.36 μg/mL). Compound C24 exhibited excellent antifungal activity against Valsa mali, Botrytis cinerea, and Alternaria alternata with EC50 values of 0.590, 0.870, and 1.71 μg/mL, respectively. The in vivo experiments revealed that compounds C32 and C24 were potential novel agricultural antifungals. 3D quantitative structure-activity relationship (3D-QSAR) models were used to analyze the structure-activity relationships of these compounds. The analysis results indicated that introducing appropriate electronegative groups at position 4 of a benzene ring could effectively improve the anti-R. solani activity. In the antifungal mechanism study, scanning electron microscopy and transmission electron microscopy analyses revealed that C32 disrupted the normal growth of hyphae by affecting the structural integrity of the cell membrane and cellular respiration. Furthermore, compound C32 exhibited potent succinate dehydrogenase (SDH) inhibitory activity (IC50 = 8.42 μM), surpassing that of the SDH fungicide boscalid (IC50 = 15.6 μM). The molecular dynamics simulations and docking experiments suggested that compound C32 can occupy the active site and form strong interactions with the key residues of SDH. Our findings have great potential for aiding future research on plant disease control in agriculture.

Integrated pharmacophore‐based screening and docking strategy for identifying type II inhibitors of VEGFR‐2 kinase

Pharmacophore modeling and quantitative structure-activity relationship (QSAR) analysis represent indispensable methodologies in modern drug discovery, offering insights into ligand-receptor interactions and aiding in the rational design of potent therapeutic agents. This study presents a thorough investigation into the identification of novel inhibitors targeting VEGFR-2 kinase, a key regulator in angiogenesis and a promising target in oncology.Leveraging a dataset comprising 72 type-II inhibitors, a series of pharmacophore hypotheses were developed using a set of six active ligands. Subsequent evaluation led to the selection of top-performing hypotheses, ADDHRR_6 and ADDHRR_10, which demonstrated good predictive capabilities in atom-based 3D QSAR modeling.Virtual screening of the Maybridge database using these pharmacophore hypotheses resulted in the identification of potential hits, which were evaluated through molecular docking analyses. Ten compounds exhibited favorable docking scores and important interactions with key residues, such as Glu885, Glu917, Cys919, and Asp1046.Additionally, comprehensive absorption, distribution, metabolism, and excretion (ADME) analysis was performed and indicated that these compound had acceptable physicochemical properties. These findings highlight the utility of integrated pharmacophore-based screening and molecular docking approaches in the identification of novel type II VEGFR-2 kinase inhibitors.

An in silico study on human carcinogenicity mechanism of polybrominated biphenyls exposure

Polybrominated biphenyls (PBBs) are associated with an increased risk of thyroid cancer; however, relevant mechanistic studies are lacking. In this study, we investigated the mechanisms underlying PBB-induced human thyroid cancer. Molecular docking and molecular dynamics methods were employed to investigate the metabolism of PBBs by the cytochrome P450 enzyme under aryl hydrocarbon receptor mediation into mono- and di-hydroxylated metabolites. This was taken as the molecular initiation event. Subsequently, considering the interactions of PBBs and their metabolites with the thyroxine-binding globulin protein as key events, an adverse outcome pathway for thyroid cancer caused by PBBs exposure was constructed. Based on 2D quantitative structure activity relationship (2D-QSAR) models, the contribution of amino acid residues and binding energy were analyzed to understand the mechanism underlying human carcinogenicity (adverse effect) of PBBs. Hydrogen bond and van der Waals interactions were identified as key factors influencing the carcinogenic adverse outcome pathway of PBBs. Analysis of non-bonding forces revealed that PBBs and their hydroxylation products were predominantly bound to the thyroxine-binding globulin protein through hydrophobic and hydrogen bond interactions. The key amino acids involved in hydrophobic interactions were alanine 330, arginine 381 and lysine 270, and the key amino acids involved in hydrogen bond interactions were arginine 381 and lysine 270. This study provides valuable insights into the mechanisms underlying human health risk associated with PBBs exposure.

Potent inhibition of human and rat 17β-hydroxysteroid dehydrogenase 1 by curcuminoids and the metabolites: 3D QSAR and in silico docking analysis

ABSTRACT Curcumin, an extensively utilized natural pigment in the food industry, has attracted considerable attention due to its potential therapeutic effects, such as anti-tumorigenic and anti-inflammatory activities. The enzyme 17β-Hydroxysteroid dehydrogenase 1 (17β-HSD1) holds a crucial position in oestradiol production and exhibits significant involvement in oestrogen-responsive breast cancers and endometriosis. This study investigated the inhibitory effects of curcuminoids, metabolites, and analogues on 17β-HSD1, a key enzyme in oestradiol synthesis. Screening 10 compounds, including demethoxycurcumin (IC50, 3.97 μM) and dihydrocurcumin (IC50, 5.84 μM), against human and rat 17β-HSD1 revealed varying inhibitory potencies. These compounds suppressed oestradiol secretion in human BeWo cells at ≥ 5–10 μM. 3D-Quantitative structure-activity relationship (3D-QSAR) and molecular docking analyses elucidated the interaction mechanisms. Docking studies and Gromacs simulations suggested competitive or mixed binding to the steroid or NADPH/steroid binding sites of 17β-HSD1. Predictive 3D-QSAR models highlighted the importance of hydrophobic regions and hydrogen bonding in inhibiting 17β-HSD1 activity. In conclusion, this study provides valuable insights into the inhibitory effects and mode of action of curcuminoids, metabolites, and analogues on 17β-HSD1, which may have implications in the field of hormone-related disorders.

3D-QSAR-Directed Synthesis of Halogenated Coumarin-3-Hydrazide Derivatives: Unveiling Their Potential as SDHI Antifungal Agents.

The pursuit of new succinate dehydrogenase (SDH) inhibitors is a leading edge in fungicide research and development. The use of 3D quantitative structure-activity relationship (3D-QSAR) models significantly enhances the development of compounds with potent antifungal properties. In this study, we leveraged the natural product coumarin as a molecular scaffold to synthesize 74 novel 3-coumarin hydrazide derivatives. Notably, compounds 4ap (0.28 μg/mL), 6ae (0.32 μg/mL), and 6ah (0.48 μg/mL) exhibited exceptional in vitro effectiveness against Rhizoctonia solani, outperforming the commonly used fungicide boscalid (0.52 μg/mL). Furthermore, compounds 4ak (0.88 μg/mL), 6ae (0.61 μg/mL), 6ah (0.65 μg/mL), and 6ak (1.11 μg/mL) showed significant activity against Colletotrichum orbiculare, surpassing both the SDHI fungicide boscalid (43.45 μg/mL) and the broad-spectrum fungicide carbendazim (2.15 μg/mL). Molecular docking studies and SDH enzyme assays indicate that compound 4ah may serve as a promising SDHI fungicide. Our ongoing research aims to refine this 3D-QSAR model further, enhance molecular design, and conduct additional bioactivity assays.

Per- and polyfluoroalkyl substances inhibit human and rat 17β-hydroxysteroid dehydrogenase 1: Quantitative structure-activity relationship and molecular docking analysis

Per- and polyfluoroalkyl (PFAS) substances are enduring industrial materials. 17β-Hydroxysteroid dehydrogenase isoform 1 (17β-HSD1) is an estrogen metabolizing enzyme, which transforms estrone into estradiol in human placenta and rat ovary. Whether PFAS inhibit 17β-HSD1 and what the structure-activity relationship (SAR) remains unexplored. We screened 18 PFAS for inhibiting human and rat 17β-HSD1 in microsomes and studied their SAR and mode of action(MOA). Of the 11 perfluorocarboxylic acids (PFCAs), C8-C14 PFCAs at a concentration of 100 μM substantially inhibited human 17β-HSD1, with order of C11 (half-maximal inhibition concentration, IC50, 8.94 μM) > C10 (10.52 μM) > C12 (14.90 μM) > C13 (30.97 μM) > C9 (43.20 μM) > C14 (44.83 μM) > C8 (73.38 μM) > others. Of the 7 per- and poly-fluorosulfonic acids (PFSAs), the potency was C8S (IC50, 14.93 μM) > C7S (80.70 μM) > C6S (177.80 μM) > others. Of the PFCAs, C8-C14 PFCAs at 100 μM markedly reduced rat 17β-HSD1 activity, with order of C11 (IC50, 9.11 μM) > C12 (14.30 μM) > C10 (18.24 μM) > C13 (25.61 μM) > C9 (67.96 μM) > C8 (204.39 μM) > others. Of the PFSAs, the potency was C8S (IC50, 37.19 μM) > C7S (49.38 μM) > others. In contrast to PFOS (C6S), the partially fluorinated compound 6:2 FTS with an equivalent number of carbon atoms demonstrated no inhibition of human and rat 17β-HSD1 activity at a concentration of 100 μM. The inhibition of human and rat enzymes by PFAS followed a V-shaped trend from C4 to C14, with a nadir at C11. Moreover, human 17β-HSD1 was more sensitive than rat enzyme. PFAS inhibited human and rat 17β-HSD1 in a mixed mode. Docking analysis revealed that they bind to the NADPH and steroid binding site of both 17β-HSD1 enzymes. The 3D quantitative SAR (3D-QSAR) showed that hydrophobic region, hydrogen bond acceptor and donor are key factors in binding to 17β-HSD1 active sites. In conclusion, PFAS exhibit inhibitory effects on human and rat 17β-HSD1 depending on factors such as carbon chain length, degree of fluorination, and the presence of carboxylic acid or sulfonic acid groups, with a notable V-shaped shift observed at C11.

Shaping the Future of Obesity Treatment: In Silico Multi-Modeling of IP6K1 Inhibitors for Obesity and Metabolic Dysfunction.

Recent research has uncovered a promising approach to addressing the growing global health concern of obesity and related disorders. The inhibition of inositol hexakisphosphate kinase 1 (IP6K1) has emerged as a potential therapeutic strategy. This study employs multiple ligand-based in silico modeling techniques to investigate the structural requirements for benzisoxazole derivatives as IP6K1 inhibitors. Firstly, we developed linear 2D Quantitative Structure-Activity Relationship (2D-QSAR) models to ensure both their mechanistic interpretability and predictive accuracy. Then, ligand-based pharmacophore modeling was performed to identify the essential features responsible for the compounds' high activity. To gain insights into the 3D requirements for enhanced potency against the IP6K1 enzyme, we employed multiple alignment techniques to set up 3D-QSAR models. Given the absence of an available X-ray crystal structure for IP6K1, a reliable homology model for the enzyme was developed and structurally validated in order to perform structure-based analyses on the selected dataset compounds. Finally, molecular dynamic simulations, using the docked poses of these compounds, provided further insights. Our findings consistently supported the mechanistic interpretations derived from both ligand-based and structure-based analyses. This study offers valuable guidance on the design of novel IP6K1 inhibitors. Importantly, our work exclusively relies on non-commercial software packages, ensuring accessibility for reproducing the reported models.

Investigation, scaffold hopping of novel donepezil-based compounds as anti-Alzhiemer's agents: synthesis, in-silico and pharmacological evaluations.

Alzheimer's disease (AD) is a multifaceted neurodegenerative condition. The pathogenesis of AD is highly intricate and the disease is apparent in the aged population ~ 50-70years old. Even after > 100years of research, the root origin of AD and its pathogenesis is unclear, complex and multifaceted. Herein, we have designed and synthesized 9 novel molecules with three different heterocyclic scaffolds namely pyrrolidone-2-one, quinoline & indoline-2-one to imitate and explore the novel chemical space around donepezil. The synthesized molecules were evaluated for their potential as anti-Alzheimer's agents through in-vitro and in-vivo studies in appropriate animal models. To further understand their interaction with acetylcholinesterase enzyme (AChE), extra-precision docking, and molecular dynamics simulation studies were carried out. As the number of compounds was limited to thoroughly explore the structure-activity relationship, atom-based 3D-quantitative structure-activity relationships (QSAR) studies were carried out to get more insights. All the designed compounds were found to inhibit AChE with IC50 in the micromolar range. From pyrrolidone-2-one series, 6-chloro-N-(1-(1-(3,4-dimethoxybenzyl)-2-oxopyrrolidin-3-yl)piperidin-4-yl)pyridine-3-sulfonamide (9), 2-(1-benzylpiperidin-4-yl)-6,7-dimethoxy-4-(4-methoxyphenyl)quinoline (18) from quinoline series and N-(1-benzylpiperidin-4-yl)-2-(2-oxoindolin-3-yl)acetamide (23) from indolin-2-one series inhibited AChE with an IC50 value of 0.01µM. Based on other biochemical studies like lipid peroxidation, reduced glutathione, superoxide dismutase, catalase, nitrite, and behavioural studies (Morris water maze), compound 9 was found to be a potent AChE inhibitor which can be further explored as a lead molecule to design more potent and effective anti-Alzheimer's agents.

In silico design of new ketorolac bioisosteres as anti-inflammatory agents

Nonsteroidal anti-inflammatory drugs (NSAIDs) are over-the-counter drugs and one of the most widely used pharmacological groups worldwide, their chronic use is limited due to their adverse effects, with gastrointestinal effects being the most relevant. Ketorolac is one of the most consumed NSAIDs worldwide, the use of which has been increasing in the treatment of acute and chronic pain; however, its use has been limited by its gastrointestinal adverse effects. Modifications in the structure of ketorolac may help the search for new chemical entity candidates for anti-inflammatory analgesic drugs with less gastric effects. Forty-two ketorolac-derived bioisosteres were designed, using in silico techniques, their physicochemical parameters (molecular weight, polar surface area, and lipophilicity) were determined with SwissADME, the Way2Drug server was used to predict the activity, a 2D quantitative structure-activity relationship (QSAR) analysis was performed to quantify the anti-inflammatory activity of the derivatives, using the ChemBL database and the R language; and the molecular modeling for the prediction of the positioning in the active site and the ligand efficiency (LE), for which the 5IKR file of the protein data bank database was used, and the molecular coupling was carried out under the parameters of AutoDock Vina. Of the 42 ketorolac derivatives, 12 molecules demonstrated good predictive oral absorption, of which only 8 molecules showed less gastro-injurious effects with Way2Drug and, under 2D QSAR analysis, 3 demonstrated excellent anti-inflammatory activity. Using the molecule modeling techniques, the LE was verified, and it was determined that molecules 32 and 34 adequately inhibited the cyclooxygenase-2 enzyme; however, the pKa of molecule 34 is not greater than that of ketorolac, molecule 32 being the one that meets with all the parameters proposed in the present study, so it would be a candidate to continue in preclinical phase studies to corroborate the findings made in this investigation.

3D-QSAR, ADMET, and molecular docking studies of aztreonam analogs as E. colis inhibitors.

The development of multidrug resistant strains of extended-spectrum β-lactamase-producing Escherichia coli has become a global problem; therefore, the discovery of new antibacterial agents is the only available solution. To improve and propose new compounds with antibacterial activity, the three-dimensional quantitative structure-activity relationship and molecular docking studies were carried out on Aztreonam analogs as E. coli inhibitors in DNA gyrase B. This study's 3D-Quantitative structure-activity relationship model was created using on the Comparative Molecular Field Analysis and the Comparative Molecular Similarity Indices Analysis. Using the Comparative Molecular Field Analysis (Q 2 = 0.73; R 2 = 0.82), excellent predictability was achieved, and the best Comparative Molecular Similarity Indices Analysis model (Q 2 = 0.88; R 2 = 0.9). The generated model's ability to predict outcomes was assessed 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 antibacterial activity. The results of the molecular docking revealed that the newly generated compound A6 has the highest binding affinity with DNA gyrase B. It forms 10 hydrogen bonds with amino acid residues of Asn104, Asn274, Asn132, Ser70, Ser237, Thr105, Glu273, and 2 salt bridges with amino acid residues of Ser70 and Glu273 and one pi-pi interacting with Gys271 amino acid residue in the binding site of 5G1, and this result was validated by a new assessment method. We created some novel, highly effective DNA gyrase B inhibitors based on the earlier findings, and the most accurate model predicted their inhibitory actions. The ADMET characteristics and pharmacological similarity of these novel inhibitors were also examined. These findings would be very beneficial in guiding the optimization process for the identification of novel drugs that can address the issue of multiple drug resistance.