3D Quantitative Structure-activity Relationship Research Articles

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.

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. method: CoMFA, CoMSIA combining HQSAR methods were employed on a series of flavonoids to generate 3D and 2D-QSAR models. 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. result: Comparative Molecular Field Analysis (CoMFA), Comparative Molecular Similarity Indices Analysis (CoMSIA) combining hologram quantitative structure-activity relationship (HQSAR) methods were employed on a series of flavonoids to generate 3D and 2D-QSAR models. Result: The obtained models can be used to predict the activities of new flavonoids and identify the key structural features affecting the NO inhibitory activities. 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. other: none

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.

Exploring the Transmembrane Behaviors of Dietary Flavonoids under Intestinal Digestive Products of Different Lipids: Insights into the Structure-Activity Relationship In Vitro.

This study aimed to investigate the transmembrane transport behavior and structure-activity relationships of various dietary flavonoids in the presence of dietary lipids derived from different sources in vitro. Results revealed that the digestion products of soybean oil (SOED) and lard (LOED) augmented the apparent permeability coefficients of most dietary flavonoids, and SOED exhibited higher transport compared with LOED. The structural properties of flavonoids and the potential interactions between fatty acids in these digestion products and flavonoids may influence the outcomes. 3D quantitative structure-activity relationship analyses revealed that incorporating small-volume groups at position 8 of the A-ring augmented the transmembrane transfer of flavonoids in the LOED system compared with the control group. By contrast, the integration of hydrophobic groups at position 5 of the A-ring and hydrogen bonding acceptor groups at position 6 of the A-ring enhanced the transmembrane transportation of flavonoids in the SOED system. Molecular dynamics simulations revealed that the SOED system may facilitate the interactions with flavonoids to form more stable and compact fatty acid-flavonoid complexes compared to the LOED system. These findings may provide valuable insights into flavonoid absorption to facilitate the development and utilization of functional foods or dietary supplements based on dietary flavonoids.

Chalcones from plants cause toxicity by inhibiting human and rat 11β-hydroxysteroid dehydrogenase 2: 3D-quantitative structure-activity relationship (3D-QSAR) and in silico docking analysis

Chalcones from licorice and its related plants have many pharmacological effects. However, the effects of chalcones on the activity of human and rat 11β-hydroxysteroid dehydrogenase 2 (11β-HSD2), and associated side effects remain unclear. The inhibition of 11 chalcones on human and rat 11β-HSD2 were evaluated in microsomes and a 3D-quantitative structure-activity relationship (3D-QSAR) was analyzed. Screening revealed that bavachalcone, echinatin, isobavachalcone, isobavachromene, isoliquiritigenin, licochalcone A, and licochalcone B significantly inhibited human 11β-HSD2 with IC50 values ranging from 15.62 (licochalcone A) to 38.33 (echinatin) μM. Screening showed that the above chemicals and 4-hydroxychalcone significantly inhibited rat 11β-HSD2 with IC50 values ranging from 6.82 (isobavachalcone) to 72.26 (4-hydroxychalcone) μM. These chalcones acted as noncompetitive/mixed inhibitors for both enzymes. Comparative analysis revealed that inhibition of 11β-HSD2 depended on the species. Most chemicals bind to the NAD+ binding site or both the NAD+ and substrate binding sites. Bivariate correlation analysis showed that lipophilicity and molecular weight determine inhibitory strength. Through our 3D-QSAR models, we identified that the hydrophobic region, hydrophobic aliphatic groups, and hydrogen bond acceptors are pivotal factors in inhibiting 11β-HSD2. In conclusion, many chalcones inhibit human and rat 11β-HSD2, possibly causing side effects and there is structure-dependent and species-dependent inhibition on 11β-HSD2.

Virtual screening approach for the discovery of selective 5α-reductase type II inhibitors for benign prostatic hyperplasia treatment.

Background: 5α-Reductase type II (5αR2) inhibition is a promising strategy for benign prostatic hyperplasia treatment. A computational approach including virtual screening, ligand-based 3Dpharmacophore modeling, 2D quantitative structure-activity relationship and molecular docking simulations were adopted to develop novel inhibitors. Results: Hits were first filtered via the validated pharmacophore and 2D quantitative structure-activity relationship models. Docking on the recently determined cocrystallized structure of 5αR2 showed three promising hits. Visual inspection results were compared with finasteride ligand and dihydrotestosterone as reference, to explain the role of binding to Glu57 and Tyr91 for 5αR2 selective inhibition. Conclusion: Alignment between Hit 2 and finasteride in the binding pocket showed similar binding modes. The biological activity prediction showed antitumor and androgen targeting activity of the new hits.

Identification of potent small-molecule PCSK9 inhibitors based on quantitative structure-activity relationship, pharmacophore modeling, and molecular docking procedure

Abstract Background Proprotein convertase subtilisin/kexin type 9 (PCSK9) attaches to the domain of LDL receptor (LDLR), diminishing LDL-C influx and LDLR cell surface presentation in hepatocytes, resulting in higher circulating LDL-C levels. PCSK9 dysfunction has been linked to lower levels of plasma LDLC and a decreased CHD risk. Purpose To identify a potent small-molecule PCSK9 inhibitor in compounds that are currently being studied in clinical trials. Methods We first performed chemical absorption, distribution, metabolism, excretion, and toxicity filtering of 9800 clinical trial compounds obtained from the ZINC 15 database using Lipinski's rule of five and achieved 3853 compounds. Two-dimensional (2D) Quantitative Structure-Activity Relationship (QSAR) was initiated by computing molecular descriptors and selecting important descriptors of 23 PCSK9 inhibitors. Multivariate calibration was performed with the partial least square regression (PLS) method with 18 compounds for training to design the QSAR model and five compounds for the test set to assess the model. The best latent variables (LV) (LV=6) with the lowest value of Root-Mean-Square Error of Cross-Validation (RMSECV) of 0.48 and leave-one-out cross-validation correlation coefficient (R2CV)=0.83 were obtained for the QSAR model. The low RMSEC (0.21) with high R²cal (0.966) indicates the probability of fit between the experimental data and the calibration model. Results Using QSAR analysis of 3853 compounds, 2635 had a pIC50<1 and were considered for pharmacophore screening. The PHASE module designed the pharmacophore hypothesis through multiple ligands. The top 14 compounds (pIC50>1) were defined as active, whereas nine (pIC50<1) were considered as an inactive set. Three five-point pharmacophore hypotheses achieved the highest score: DHHRR1, DHHRR2, and DHRRR1. The highest and best model with survival scores (5.365) was DHHRR1, comprising one hydrogen donor (D), two hydrophobic groups (H), and two rings of aromatic (R) features. We selected the molecules with a higher 1.5 fitness score (257 compounds) in pharmacophore screening (DHHRR1) for molecular docking screening.Molecular docking indicates that ZINC000051951669, with a binding affinity: of -13.2 kcal/mol and two H-bonds, has the highest binding to the PCSK9 protein. ZINC000011726230 with energy binding: -11.4 kcal/mol and three H-bonds, ZINC000068248147 with binding affinity: -10.7 kcal/mol and one H-bond, ZINC000029134440 with a binding affinity: -10.6 kcal/mol and four H-bonds were ranked next, respectively. Conclusion The archived molecules identified as inhibitory PCSK9 candidates, and especially ZINC000051951669 may therefore significantly inhibit PCSK9 and should be considered in the newly designed trials.Figure 1.The six-top docking compound against PCSK9 with lowest binding affinity. A) ZINC000051951669, B) ZINC000011726230, C) ZINC000068248147, D) ZINC000029134440, E) ZINC000000602086, F) ZINC000034630885.

Convergent QSAR Models for the Prediction of Cruzain Inhibitors.

Chagas disease is a parasitosis caused by Trypanosoma cruzi. Cruzain, the major cysteine protease from T. cruzi, is an excellent therapeutic target in the search for antichagasic drugs. It is important in the role of cell invasion, replication, differentiation, and metabolism of the parasite. In this work, we developed and assessed multiple quantitative structure-activity relationship (QSAR) models for a set of 61 cruzain inhibitors. These models include two-dimensional (2D) QSAR, three-dimensional (3D) QSAR, such as comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA), and Hologram QSAR (HQSAR). In total, we generated 10 major and 114 minor model variations. Molecular docking was used to successfully align the molecules. All CoMFA and CoMSIA models, which incorporate multiple fields, demonstrated robustness in our analysis. Steric fields exhibited satisfactory convergence in the contour maps, while the electrostatic field converged into a single small region. The HQSAR model taking into consideration only Atoms and Connectivity, with fragment sizes ranging from two to five atoms, was considered the best of the HQSAR variations, despite exhibiting a higher level of deviance. In total, 78 model variations meet the minimum requirements to be considered acceptable. We found that using as few as five descriptors it is possible to obtain robust results with 2D-QSAR. Models such as Random Forest, Tree Ensemble, Linear Regression, and HQSAR are recommended for working with large data sets, while the 3D-QSAR models are intended to study the geometry of the ligands, to optimize them into new and better performing antichagasics. Virtual Screening of a set of hydrazones, guided by the top-performing models, identified promising candidates for experimental validation. Among them, dv007 and dv015 exhibited consistently high predicted pIC50 values (7.26 and 7.24, respectively), making them compelling candidates for further drug development.

‘3D-QSAR-based, pharmacophore modelling, virtual screening, and molecular docking studies for identification of hypoxia-inducible factor-1 inhibitor with potential bioactivity

Iron overload is a primary cause of vital organ failure in patients with blood transfusion-dependent beta-thalassemia, and the hypoxia-inducible factor-1 α (HIF-1α) plays an important role in iron homeostasis pathway. HIF-1α modulation as a potential therapeutic target approach for iron chelation in hepatocyte cells. In this study, we used a 3D quantitative structure-activity relationship (QSAR) analysis to predict the inhibitory activity of HIF-1α inhibitors for iron chelation in liver cells. These feature descriptors were used to build a 3D-QSAR model, which was validated using Cost analysis and Fischer's randomization test. The model was used to virtually search the chemical compound libraries for potential inhibitor candidates with least inhibitory activity. The High-throughput Docking (Libdock) approach was used to dock large repositories of chemical molecules. Following Libdock score screening, the protein-ligand poses were docked using docking optimization (Cdocker) method. Binding energy were calculated for the protein-ligand poses of lowest –Cdocker Energy and –Cdocker Interaction. Further, side chain hopping method was used to generate lead novel ligand from best hit pose of ligand. Molecular dynamics simulation study to evaluate the lead novel ligand. Our study demonstrates the utility of 3D-QSAR pharmacophore screening in predicting the inhibitory activity for target. Inhibition strategy for iron chelation provides an alternative routes for reducing the iron content.

Bisphenol analogues inhibit human and rat 17β-hydroxysteroid dehydrogenase 1: 3D-quantitative structure-activity relationship (3D-QSAR) and in silico docking analysis

Bisphenols, estrogenic endocrine-disrupting chemicals, disrupt at least one of three endocrine pathways (estrogen, androgen, and thyroid). 17β-Hydroxysteroid dehydrogenase 1 (17β-HSD1) is a steroidogenic enzyme that catalyzes the activation of estradiol from estrone in human placenta and rat ovary. However, whether bisphenols inhibit 17β-HSD1 and the mode of action remains unclear. This study we screened 17 bisphenols for inhibiting human 17β-HSD1 in placental microsomes and rat 17β-HSD1 in ovarian microsomes and determined 3D-quantitative structure-activity relationship (3D-QSAR) and mode of action. We observed some bisphenols with substituents were found to significantly inhibit both human and rat 17β-HSD1 with the most potent inhibition on human enzyme by bisphenol H (IC50 = 0.90 μM) when compared to bisphenol A (IC50 = 113.38 μM). Rat enzyme was less sensitive to the inhibition of bisphenols than human enzyme with bisphenol H (IC50 = 32.94 μM) for rat enzyme. We observed an inverse correlation between IC50 and hydrophobicity (expressed as Log P). Docking analysis showed that they bound steroid-binding site of 17β-HSD1. The 3D-QSAR models demonstrated that hydrophobic region, hydrophobic aromatic, ring aromatic, and hydrogen bond acceptor are key factors for the inhibition of steroid synthesis activity of 17β-HSD1.