3D-QSAR Analysis Research Articles

Dithiocarbamate fungicides suppress aromatase activity in human and rat aromatase activity depending on structures: 3D-QSAR analysis and molecular simulation

ABSTRACT Dithiocarbamate fungicides have been widely used in agricultural practices due to their effective control of fungal diseases, thereby contributing to global food security and agricultural productivity. In this study, the inhibitory potency of eight compounds on human and rat aromatase (CYP19A1) activity was evaluated. The results revealed that zineb exhibited the highest inhibitory potency on human CYP19A1 (IC50, 2.79 μM). Maneb (IC50, 3.09 μM), thiram (IC50, 4.76 μM), and ferbam (IC50, 6.04 μM) also demonstrated potent inhibition on human CYP19A1. For the rat CYP19A1, disulfiram (IC50, 1.90 μM) displayed the strongest inhibition followed by maneb (2.16 μM), zineb (2.54 μM), and thiram (6.99 μM). These dithiocarbamates acted as mixed/non-competitive inhibitors of human and rat CYP19A1. Dithiothreitol (DTT), a reducing agent, partially rescued thiram-mediated inhibition when incubated at the same. Moreover, positive correlations were observed between log P, topological polar surface area, molecular weight, and heavy atoms and IC50 values. 3D-QSAR analysis revealed the hydrogen bond acceptor and donor play critical roles in the binding of dithiocarbamates to human CYP19A1. In silico analysis showed that dithiocarbamates bind to the haem binding site, containing Cys437 residues. In conclusion, some dithiocarbamates potently inhibit human and rat CYP19A1 via interacting with haem-binding Cys437 residues.

In silico identification and virtual screening to discover potent therapeutic phytochemicals against CMT2A

Charcot-Marie-Tooth (CMT2A) neuropathies are a set of monogenic diseases that affect the peripheral nervous system. The pathogenesis of CMT2A, a disease caused by genetic mutations, is linked to impaired mitochondrial dynamics and axonal biology. Therapeutic options are still limited, with only a few drugs and other authorized or underdeveloped approaches. A ligand-based virtual screening methodology was used to identify the potential MFN promoters. The natural compound subset of the ZINC database (n = 559600) was obtained and filtered using a ligand-based virtual screening technique. The top 200 compounds were identified to have more than four features that matched the target compound. Pyrx software was used to analyze the molecular docking. Based on the number and type of important binding interactions and docking results, we selected top-20 compounds with the best binding affinities for the targeted protein. 3D-QSAR analyses were performed on potential ligands identified through molecular docking analyses to predict biological activity. The pEC50 and docking scores were used to identify the potential drugs. The ADMET analysis was used to assess the kinetic characteristics of the top two drugs. According to molecular dynamics simulation, the top compound ZINC000005313168 showed high conformational stability. Based on docking results and MD findings, the best in silico hits among the compounds investigated was ZINC000005313168. These findings suggest that the compound ZINC000005313168 could be used to treat CMT2A disease. Further in vivo and in vitro studies are required to consider the present analyses.

Paraben preservatives exhibit inhibition on human and rat steroid 5α-reductase 1: A comprehensive 3D-QSAR and computational analysis

Parabens are preservatives used in personal care products, cosmetics, and pharmaceuticals. Steroid 5α-reductase 1 (SRD5A1) catalyzes the conversion of testosterone to dihydrotestosterone and is present in the brain, contributing to neurosteroid production. This study aimed to assess the effects of nine paraben preservatives on SRD5A1 in human SF126 glioblastoma cell and rat brain microsomes, particularly focusing on dihydrotestosterone production in SF126 cells. The results showed that methyl, ethyl, propyl, butyl, hexyl, heptyl, nonyl, phenyl, and benzyl paraben inhibited human SRD5A1, with nonylparaben having the strongest effect (7.59 μM). Additionally, kinetic analysis indicated that parabens acted as mixed/noncompetitive inhibitors, leading to a significant decrease in dihydrotestosterone production in SF126 cells. While rat SRD5A1 exhibited lower sensitivity to parabens, docking analysis revealed that parabens bind to the NADPH-binding site of both human and rat SRD5A1. In conclusion, these results highlight the inhibitory effects of paraben preservatives on SRD5A1 and elucidate their binding mechanisms, underscoring their role in hormone production.

Study on the anti-HBV activity of matrine alkaloids from Oxytropis ochrocephala by MTT, 3d-QSAR, molecular docking and molecular dynamics simulation

To elucidate the structure-activity relationship of 17 matrine alkaloids from Oxytropis ochrocephala Bunge, their effect on hepatitis B surface antigen (HBsAg) secretion was studied using the MTT assay. A 3D-QSAR analysis showed a strong correlation between chemical structures and biological activities (q2 = 0.625, r2 = 0.859). Molecular docking and molecular dynamics simulations revealed that hydrogen bonding and hydrophobic interactions with hepatitis B core protein (PDB:5T2P) are key to inhibiting HBsAg secretion, suggesting potential for developing natural anti-hepatitis B drugs.

Quantitative structure-activity relationship and ADME prediction studies on series of spirooxindoles derivatives for anti-cancer activity against colon cancer cell line HCT-116

Forty-one derivatives of spirooxindoles, active against HCT-116 colon cancer cells, underwent pharmacophore-based 3D-QSAR analysis to understand their correlation with anti-cancer activity. The study identified a seven-point pharmacophore model (ADHHRRR1) and QSAR models, offering insights for lead optimization and novel analogue design, thus advancing anti-cancer drug discovery. This research underscores the value of molecular modeling in elucidating structure-activity relationships and enhancing drug development efforts.

Novel mandelic acid derivatives containing piperazinyls as potential candidate fungicides against Monilinia fructicola: Design, synthesis and mechanism study

Brown rot of stone fruit, a disease caused by the ascomycete fungus Monilinia fructicola, has caused significant losses to the agricultural industry. In order to explore and discover potential fungicides against M. fructicola, thirty-one novel mandelic acid derivatives containing piperazine moieties were designed and synthesized based on the amide skeleton. Among them, target compound Z31 exhibited obvious in vitro antifungal activity with the EC50 value of 11.8 mg/L, and significant effects for the postharvest pears (79.4 % protective activity and 70.5 % curative activity) at a concentration of 200 mg/L. Antifungal activity for the target compounds was found to be significantly improved by the large steric hindrance of the R1 groups and the electronegative of the piperazines in the molecular structure, according to a three-dimensional quantitative structure–activity relationship (3D-QSAR) analysis. Further mechanism studies have demonstrated that the compound Z31 can disrupt cell membrane integrity, resulting in increased membrane permeability, release of intracellular electrolytes, and affect the normal growth of hyphae. Additional, morphological study also indicated that Z31 may disrupt the integrity of the membrane by inducing generate excess endogenous reactive oxygen species (ROS) and resulting in the peroxidation of cellular lipids, which was further verified by the detection of malondialdehyde (MDA) content. These studies have provided the basis for the creation of novel fungicides to prevent brown rot in stone fruits.

Evaluation of antibacterial, cytotoxicity, and apoptosis activity of novel chromene-sulfonamide hybrids synthesized under solvent-free conditions and 3D-QSAR modeling studies

In this study, eleven novel chromene sulfonamide hybrids were synthesized by a convenient method in accordance with green chemistry. At first, chromene derivatives (1–9a) were prepared through the multi-component reaction between aryl aldehydes, malononitrile, and 3-aminophenol. Then, synthesized chromenes were reacted with appropriate sulfonyl chlorides by grinding method to give the corresponding chromene sulfonamide hybrids (1–11b). Synthesized hybrids were obtained in good to high yield and characterized by IR, 1HNMR, 13CNMR, CHN and melting point techniques. In addition, the broth microdilution assay was used to determine the minimal inhibitory concentration of newly synthesized chromene-sulfonamide hybrids. The MTT test was used to determine the cytotoxicity and apoptotic activity of the newly synthesized compounds against fibroblast L929 cells. The 3D‑QSAR analysis confirmed the experimental assays, demonstrating that our predictive model is useful for developing new antibacterial inhibitors. Consequently, molecular docking studies were performed to validate the findings of the 3D-QSAR analysis, confirming the potential binding interactions of the synthesized chromene-sulfonamide hybrids with the target enzymes. Molecular docking studies were employed to support the 3D-QSAR predictions, providing insights into the binding interactions between the newly synthesized chromene-sulfonamide hybrids and their target bacterial enzymes, thereby reinforcing the potential efficacy of these compounds as antibacterial agents. Also, some of the experimental outcomes supported or conflicted with the pharmacokinetic prediction (especially about compound carcinogenicity). The performance of ADMET predictor results was assessed. The work presented here proposes a computationally driven strategy for designing and discovering a new sulfonamide scaffold for bacterial inhibition.

Development and experimental validation of 3D QSAR models for the screening of thyroid peroxidase inhibitors using integrated methods of computational chemistry

The intricate network of glands and organs that makes up the endocrine system. Hormones are used to regulate and synchronize the nervous and physiological systems. The agents which perturbate an endocrine system are called endocrine disruptors and they can eventually affect cellular proliferation and differentiation in target tissues. A subclass of endocrine disruptors known as thyroid disruptors (TDs) or thyroid disrupting chemicals (TDCs) influence the hypothalamo-pituitary-thyroid axis or directly interfere with thyroid function by binding to thyroid hormone receptors. Thyroid hormone levels in circulation are now included in more test guidelines (OECD TG 441, 407, 408, 414, 421/422, 443/416). Although these might be adequate to recognize thyroid adversity, they are unable to explain the underlying mechanism of action. Thyroid peroxidase (TPO) and sodium iodide symporter (NIS), two proteins essential in the biosynthesis of thyroid hormones, are well-accepted molecular targets for inhibition. The screening of a large number of molecules using high throughput screening (HTS) requires a minimum quantity of sample, cost, and time consuming. Whereas 3-dimensional quantitative structure-activity relationship (3D-QSAR) analysis can screen the TDCs before synthesizing a compound. In the present study, the human TPO (hTPO) and NIS (hNIS) structures were modelled using homology modeling and the quality of the structures was validated satisfactorily using MD simulation for 100ns. Further, 190 human TPO inhibitors with IC50 were curated from Comptox and docked with the modelled structure of TPO using D238, H239 and D240 centric grid. The binding conformation of a molecule with low binding energy was used as a reference and the rest other molecules were aligned after generating the possible conformers. The activity-stratified partition was performed for aligned molecules and training set (139), test set (51) were defined. The machine learning models such as k Nearest Neighbor (kNN) and Random Forest (RF) models were built and validated using external experimental dataset containing 10 molecules. Among the 10 molecules, all 10 molecules were identified as TPO inhibitors and demonstrated 100 % accuracy qualitatively. To confirm the selective TPO inhibition all 10 molecules were docked with the modelled structure of hNIS and the results have demonstrated the selective TPO inhibition.

Conducting 2D and 3D QSAR Analyses and Molecular Docking Studies of Analogues of 2-(1-(1,3,4-thiadiazol-2-yl)piperidin-4-yl)ethan-1-ol with the Aim of Identifying Promising Drug Candidates for Targeting Glioblastoma

Background: 2-(1-(1,3,4-thiadiazol-2-yl)piperidin-4-yl) ethan-1-ol analogues represent novel glutaminase 1 inhibitors. Their exemplary antineoplastic efficacy underscores their prospective utility in glioblastoma chemotherapy. Objectives: This study aimed to elucidate 2D and 3D-QSAR models that authenticate the antineoplastic efficacy of ethan-1-ol analogues and delineate optimal structural configurations conducive to new pharmaceutical design. Methods: The Heuristic Method (HM) was employed for the development of a 2D-linear QSAR paradigm, whilst the Gene Expression Programming (GEP) algorithm was employed for a 2D-nonlinear QSAR paradigm. Concurrently, the CoMSIA methodology was deployed to scrutinize the nexus between pharmaceutical structure and potency. An ensemble of 200 nascent anti-glioma ethan-1-ol compounds was conceptualized, and their potency levels were prognosticated via chemical descriptors and molecular field delineations. Pharmaceuticals epitomizing peak potency were earmarked for molecular docking validation. Results: The empirical modeling exhibited pronounced superiority with the 3D paradigm, succeeded by the GEP nonlinear paradigm and culminated with the HM linear model. The 3D paradigm was characterized by a robust Q2 (0.533), R2 (0.921), and F-values (132.338) complemented by a minimal SEE (0.110). The molecular descriptor MNO coupled with the hydrogen bond donor field facilitated novel pharmaceutical conceptualizations, leading to the identification of the quintessential active molecule, 24J.138, lauded for its superlative antineoplastic attributes and docking proficiency. Conclusion: The orchestration of bidimensional and tridimensional paradigms, synergized by innovative amalgamation of contour maps and molecular descriptors, provides novel insights and methodologies for the synthesis of glioblastoma chemotherapeutic agents.

Demethoxylation of curcumin enhances its inhibition on human and rat 17β-hydroxysteroid dehydrogenase 3: QSAR structure-activity relationship and in silico docking analysis

Curcuminoids have many pharmacological effects. They or their metabolites may have side effects by suppressing 17β-hydroxysteroid dehydrogenase 3 (17β-HSD3). Herein, we investigated the inhibition of curcuminoids and their metabolites on human and rat 17β-HSD3 and analyzed their structure-activity relationship (SAR) and performed in silico docking. Curcuminoids and their metabolites ranked in terms of IC50 values against human 17β-HSD3 were bisdemethoxycurcumin (0.61 μM) > curcumin (8.63 μM) > demethoxycurcumin (9.59 μM) > tetrahydrocurcumin (22.04 μM) > cyclocurcumin (29.14 μM), and those against rat 17β-HSD3 were bisdemethoxycurcumin (3.94 μM) > demethoxycurcumin (4.98 μM) > curcumin (9.62 μM) > tetrahydrocurcumin (45.82 μM) > cyclocurcumin (143.5 μM). The aforementioned chemicals were mixed inhibitors for both enzymes. Molecular docking analysis revealed that they bind to the domain between the androstenedione and NADPH active sites of 17β-HSD3. Bivariate correlation analysis showed a positive correlation between LogP and pKa of curcumin derivatives with their IC50 values. Additionally, a 3D-QSAR analysis revealed that a pharmacophore model consisting of three hydrogen bond acceptor regions and one hydrogen bond donor region provided a better fit for bisdemethoxycurcumin compared to curcumin. In conclusion, curcuminoids and their metabolites possess the ability to inhibit androgen biosynthesis by directly targeting human and rat 17β-HSD3. The inhibitory strength of these compounds is influenced by their lipophilicity and ionization characteristics.

Clustering of atoms relative to vector space in the Z-matrix coordinate system and 'graphical fingerprint' analysis of 3D pharmacophore structure.

The behavior of a molecule within its environment is governed by chemical fields present in 3D space. However, beyond local descriptors in 3D, the conformations a molecule assumes, and the resulting clusters also play a role in influencing structure-activity models. This study focuses on the clustering of atoms according to the vector space of four atoms aligned in the Z-Matrix Reference system for molecular similarity. Using 3D-QSAR analysis, it was aimed to determine the pharmacophore groups as interaction points in the binding region of the β2-adrenoceptor target of fenoterol stereoisomers. Different types of local reactive descriptors of ligands have been used to elucidate points of interaction with the target. Activity values for ligand-receptor interaction energy were determined using the Levenberg-Marquardt algorithm. Using the Molecular Comparative Electron Topology method, the 3D pharmacophore model (3D-PhaM) was obtained after aligning and superimposing the molecules and was further validated by the molecular docking method. Best guesses were calculated with a non-output validation (LOO-CV) method. Finally, the data were calculated using the 'graphic fingerprint' technique. Based on the eLKlopman (Electrostatic LUMO Klopman) descriptor, the Q2 value of this derivative set was calculated as 0.981 and the R2ext value is calculated as 0.998.

Multistage in silico approach to identify novel quinoline derivatives as potential c-kit kinase inhibitors

The type II-C-KIT signaling network has been extensively studied for its potential as a target for cancer treatment, leading to the investigation of quinoline derivatives as compounds with inhibitory effects on c-Kit kinase. In this study, a multistage approach was employed, including the creation of pharmacophore models, 3D QSAR analysis, virtual screening, docking investigations, and molecular dynamics stimulation. The pharmacophore evaluation included a data set of 29 ligands, which resulted in the generation of the ADDHR_1pharmacophore model as the most promising, with a survival score of 5.6812. The main objective was to utilize the atom-based 3D-QSAR approach for generating robust 3D-QSAR models aimed at identifying new TypeII-C-kit kinase inhibitors. The evaluations of these models have convincingly demonstrated their high predictive power (Q2 = 0.6547, R2 = 0.9947). Using atom-based 3D-QSAR data, a total of 7564 novel compounds were generated from R-group enumeration. Molecular docking and MM-GBSA study revealed that compound A1 exhibited the highest binding score of −9.30 kcal/mol and a Δ GBind value of −90.56 kcal/mol. The ZINC compounds were then screened using the pharmacophore model, followed by virtual screening, which identified ZINC65798256, ZINC09317958, ZINC73187176, and ZINC76176670 as potential candidates with promising docking scores. Among these, ZINC65798256 demonstrated the best binding interactions with amino acid residues, ASP810, LYS623, CYS673, and THR670 (PDB ID: 1T46). To further analyze the structural features and molecular interactions, molecular dynamics simulation was conducted for a time scale of 100 ns. Communicated by Ramaswamy H. Sarma

Computational studies of pyrimidine derivatives as inhibitors of human σ1 receptor using 3D-QSAR analysis, molecular docking, ADMET properties and DFT investigation

Neuropathic pain syndrome has a profoundly negative and agonizing impact on the lives of the individuals it afflicts. In order to find an effective treatment for this condition, extensive and thorough scientific studies have demonstrated that the σ1 receptor serves as an exceptional target for therapeutic compounds. The 3D-QSAR studies were constructed using the technique of comparative molecular similarity indice analysis (CoMSIA). The outcomes of these studies demonstrated the reliability of CoMSIA model (with R2 train value of 0.96 and Q2 value of 0.54) in accurately predicting the activity of various compounds. By assimilating the valuable insights gathered from the field contributors of the 3D-QSAR models and conducting molecular docking studies on the highly potent compound C48, a total of sixteen new compounds were successfully designed to exhibit enhanced efficacy against neuropathic pain. In addition to the comprehensive 3D-QSAR analysis, the newly synthesized compounds were subjected to an absorption, distribution, metabolism, excretion, and toxicity evaluation. This evaluation aimed to assess the pharmacokinetic and toxicological properties of the compounds, providing valuable insight for future in vitro investigations. Calculations DFT of the new compounds mol2, mol3, and mol4, including the analysis of their molecular properties, geometric optimization, frontier molecular orbital (FMOs), density states (DOS), and energy evaluation, In order to compare these compounds with the reference ligand 61 w, their respective properties were thoroughly investigated. The most stable orientations for the compounds Mol2, Mol4, and Mol3 were determined to be the ones that yielded the highest stability and efficiency. The significant advancements made in this study, should serve as a strong motivation for future in vitro investigations on these compounds.

Essential Structural Profile of Novel Adenosine Derivatives as Antiplatelet Aggregation Inhibitors based on 3D-QSAR Analysis

Aims: In this research, 3D-QSAR evaluation on a set of fresh purinoid compounds that we produced was conducted. This analysis aims to illustrate the correlation between the structure of purine and its ability to prevent platelet aggregation. Our findings could pave the way to discovering novel antithrombotic medications. background: Cardiovascular disease caused by platelet aggregation is a serious threat to human health. Purine derivatives are important molecules with antiplatelet aggregation activity. Background: The incidence of cardiovascular disease triggered by the clumping of platelets poses a significant danger to human health. Purine derivatives are important molecules with antiplatelet aggregation activity. Objective: The objectives of this research are to establish the correlation between the structure of purine and its ability to prevent platelet aggregation. Such a correlation could aid in the development of innovative antithrombotic medications. method: In this study, 3D-QSAR analysis was performed on a series of novel purine derivatives synthesized by us based on comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA). Methods: In this study, 3D-QSAR investigation on a collection of 75 new purine derivatives, which we synthesized, was conducted, utilizing Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). result: Significant correlation coefficients (CoMFA, q2=0.843, r2=0.930, F value=266.755, SEE=0.165; CoMSIA, q2=0.869, r2=0.918, F value=222.571, SEE=0.179) were obtained, and the model prediction ability was validated using the test set. Results: Significant correlation coefficients (CoMFA, q2= 0.843, r2= 0.930, F value= 266.755, SEE= 0.165; CoMSIA, q2= 0.869, r2= 0.918, F value= 222.571, SEE= 0.179) were obtained, and assessed the model's predictive capabilities by validating it with the test set. conclusion: The results suggest that it is beneficial to introduce a group of appropriate size at position C-2 of the purine ring, and that an excessively large group is disadvantageous; a bulky substituent group cannot be directly attached at the C-6 position of the purine ring, and the attachment of a group with low electron cloud density increases the activity, and the attachment of a bulky group at the C-5' of the sugar ring is beneficial, and the presence of hydrogen bond receptors in this region also increases the activity. Conclusion: Our findings indicate that the introduction of an appropriately sized structure at position 2 of the compound yields significant benefits. Conversely, the attachment of an excessively large group is detrimental. Direct attachment of a bulky substituent at C-6 of the compound is not feasible, and its activity increases when the structure with low electron cloud density is added. Moreover, the presence of a voluminous functional group at the 5' position of the compound is advantageous, and its activity will be further increased by the presence of hydrogen bond receptors in this region. These discoveries furnish significant comprehension for the formation of innovative structures with heightened efficacy.

Development of 2D and 3D QSAR models of pyrazole derivatives as acetylcholine esterase inhibitors

The drugs that are the most useful in all stages of Alzheimer?s disease (AD) are acetylcholinesterase (AChE) inhibitors. The objectives of this work are to generate various QSAR models for such drugs and to select a robust predictive models from the corresponding models. Studies were then focused on finding a range of pyrazole-like AChE inhibitors by 2D and 3D QSAR analysis. The genetic algorithm-based multiple linear regression (GA-MLR) provided the statistically robust 2D QSAR model that depicted the significance of the molecular volume and the number of multiple bonds along with the presence/ absence of specific atom-centred fragments and topological distance between 2D pharmacophoric features. Furthermore, these results were correlated well with the electrostatic and steric contour maps retrieved from the 3D QSAR (i.e., alignment-dependent molecular field analysis). The 2D QSAR analysis developed a highly statistical and reliable model, which was compared with the mechanistic interpretation of 3D structures and their electrostatic and steric field contributions leading to a predictive 3D QSAR model. The molecule- protein interactions, elicited by molecular docking, corroborated with the field interactions, as revealed by 2D QSAR. Thus, the developed computational models and simulation analyses in the current work provide valuable information for the future design of pyrazole and spiropyrazoline analogs, as potent AChE inhibitors.

Comprehensive in silico discovery of c-Src tyrosine kinase inhibitors in cancer treatment: A unified approach combining pharmacophore modeling, 3D QSAR, DFT, and molecular dynamics simulation

ObjectiveTo investigate c-Src, a non-receptor tyrosine kinase dysregulated in various cancer types including colon, breast, and pancreatic cancers, as a potential drug target for cancer therapy. MethodsLigand-based pharmacophore modeling and 3D-QSAR analysis on a dataset of 34c-Src tyrosine kinase inhibitors were employed. The established pharmacophore model (DDRRR_1) features two hydrogen bond donor (D) and three aromatic ring (R) features, exhibiting favorable parameters (R2 = 0.926; Q2 = 0.895; F value = 47.9). Hypothesis validation, enrichment analysis, and contour plot analysis were conducted, followed by virtual screening of a PubChem database using the optimized pharmacophore model and filtering based on the Lipinski rule of five. ResultsThe most promising inhibitors underwent multistep molecular docking, density Functional Theory (DFT) analysis, ADMET assessments, molecular dynamics simulation, and PCA. CID_70144047 emerged as the most promising hit with all the above favorable properties. ConclusionThe study provides a comprehensive approach for identifying novel c-Src tyrosine kinase inhibitors, highlighting CID_70144047 as a promising leads with potential therapeutic applications in cancer treatment.

“2D, 3D QSAR and Pharmacophore Identification of Thieno[3,2-d]pyrimidines as Cholesterol inhibitors.”

Introduction:: The present study reveals the 2D, 3D-QSAR analysis of Thieno[3,2-d]pyrimidine to express the biological activity against Cholesterol, structurally different ligands can fit the common receptor site, and Pharmacophore models well describe safety considerations of the said chemical entities Methods:: The organic exercises of the atoms were changed over into log IC50. The measurably huge of 2D-QSAR and 3D QSAR models are r2 = 0 .9762, q2 = 0.9379 and internal (q2 = 0.8837) and external (predictive r2 = 0.9162) respectively. 2D QSAR studies revealed that the Positive coefficient value of Quadrupole2 and Negative coefficient value of T_2_Cl_7 descriptors were major contributing descriptors. Results:: The 3D QSAR models indicate that steric and electrostatic effects primarily determine binding affinities. Conclusion:: From the best model obtained from the QSAR analyses, some newer compounds of the same series were developed, having better activity than the earlier compounds that have been reported.

In silico development of potential InhA inhibitors through 3D-QSAR analysis, virtual screening and molecular dynamics

Tuberculosis is one of the most ancient infectious diseases known to mankind predating upper Paleolithic era. In the current scenario, treatment of drug resistance tuberculosis is the major challenge as the treatment options are limited, less efficient and more toxic. In our study we have developed an atom based 3D QSAR model, statistically validated sound with R2 > 0.90 and Q2 > 0.72 using reported direct inhibitors of InhA (2018–2022), validated by enzyme inhibition assay. The model was used to screen a library of 3958 molecules taken from Binding DB and candidates molecules with promising predicted activity value (pIC50) > 5) were selected for further analyzed screening by using molecular docking, ADME profiling and molecular dynamic simulations. The lead molecule, ZINC11536150 exhibited good docking score (glideXP = −11.634 kcal/mol) compared to standard triclosan (glideXP = −7.129 kcal/mol kcal/mol) and through molecular dynamics study it was observed that the 2nv6-complex of ZINC11536150 with Mycobacterium tuberculosis InhA (PDB entry: 2NV6) complex remained stable throughout the entire simulation time of 100 ns.Communicated by Ramaswamy H. Sarma

Multivariate Statistical 2D QSAR Analysis of Indenoisoquinoline-based Topoisomerase- I Inhibitors as Anti-lung Cancer Agents.

Indenoisoquinoline-based compounds have shown promise as topoisomerase-I inhibitors, presenting an attractive avenue for rational anticancer drug design. However, a detailed QSAR study on these derivatives has not been performed till date. This study aimed to identify crucial molecular features and structural requirements for potent topoisomerase- 1 inhibition. A comprehensive two-dimensional (2D) QSAR analysis was performed on a series of 49 indenoisoquinoline derivatives using TSAR3.3 software. A robust QSAR model based on a training set of 33 compounds was developed achieving favorable statistical values: r2 = 0.790, r2CV = 0.722, f = 36.461, and s = 0.461. Validation was conducted using a test set of nine compounds, confirming the predictive capability of the model (r2 = 0.624). Additionally, artificial neural network (ANN) analysis was employed to further validate the significance of the derived descriptors. The optimized QSAR model revealed the importance of specific descriptors, including molecular volume, Verloop B2, and Weiner topological index, providing essential insights into effective topoisomerase-1 inhibition. We also obtained a robust partial least-square (PLS) analysis model with high predictive ability (r2 = 0.788, r2CV = 0.743). The ANN results further reinforced the significance of the derived descriptors, with strong r2 values for both the training set (r2 = 0.798) and the test set (r2 = 0.669). The present 2D QSAR analysis offered valuable molecular insights into indenoisoquinoline-based topoisomerase- I inhibitors, supporting their potential as anti-lung cancer agents. These findings contribute to the rational design of more effective derivatives, advancing the development of targeted therapies for lung cancer treatment.

Tetrahydrocurcumin Derivatives Enhanced the Anti-Inflammatory Activity of Curcumin: Synthesis, Biological Evaluation, and Structure–Activity Relationship Analysis

Tetrahydrocurcumin, the most abundant curcumin transformation product in biological systems, can potentially be a new alternative therapeutic agent with improved anti-inflammatory activity and higher bioavailability than curcumin. In this article, we describe the synthesis and evaluation of the anti-inflammatory activities of tetrahydrocurcumin derivatives. Eleven tetrahydrocurcumin derivatives were synthesized via Steglich esterification on both sides of the phenolic rings of tetrahydrocurcumin with the aim of improving the anti-inflammatory activity of this compound. We showed that tetrahydrocurcumin (2) inhibited TNF-α and IL-6 production but not PGE2 production. Three tetrahydrocurcumin derivatives inhibited TNF-α production, five inhibited IL-6 production, and three inhibited PGE2 production. The structure–activity relationship analysis suggested that two factors could contribute to the biological activities of these compounds: the presence or absence of planarity and their structural differences. Among the tetrahydrocurcumin derivatives, cyclic compound 13 was the most active in terms of TNF-α production, showing even better activity than tetrahydrocurcumin. Acyclic compound 11 was the most effective in terms of IL-6 production and retained the same effect as tetrahydrocurcumin. Moreover, acyclic compound 12 was the most active in terms of PGE2 production, displaying better inhibition than tetrahydrocurcumin. A 3D-QSAR analysis suggested that the anti-inflammatory activities of tetrahydrocurcumin derivatives could be increased by adding bulky groups at the ends of compounds 2, 11, and 12.