3D-QSAR Pharmacophore Model Research Articles

Discovery of Daclatasvir as a potential PD-L1 inhibitor from drug repurposing

This study employed a drug repositioning strategy to discover novel PD-L1 small molecule inhibitors. 3D-QSAR pharmacophore models were establishedand subsequently validated through various means to select a robust model, Hypo-1, suitable for virtual screening. Hypo1 was used toscreen a library of 7,475 compounds from the Drugbank database, leading to the identification of 283 molecules following molecular docking with PD-L1.19 compounds underwent HTRF assays, with 15 showing varying degrees of inhibition of the PD-1/PD-L1 interaction. Compounds2202,2204,2207, and2208were further confirmed to bind to PD-L1 using SPR experiments. Among them, compound2204(Daclatasvir, KD = 11.4 μM) showeda higher affinity for human PD-L1 than the control compound BMS-1. In the HepG2/Jurkat cell co-culture model, Daclatasvir effectively activated Jurkat cells to kill HepG2 cells. In the mouse H22 hepatocellular tumor model, Daclatasvir significantly inhibited tumor growth (TGI = 53.4 % at a dose of 100 mg/kg). Its anti-tumor effect was more pronounced when combined with Lenvatinib (TGI = 85.1 %). Flow cytometry analysis of splenocytes and tumor cells indicated that Daclatasvir activated the immune system in both models. In summary, Daclatasvir was identified as a novel PD-L1small molecule inhibitor.

Xanthine oxidase inhibitors: Virtual screening and mechanism of inhibition studies

Xanthine oxidase (XO), which plays a key role in purine metabolism, is an important target enzyme for the prevention and treatment of hyperuricemia. Inhibitory activity against XO is a common criterion for the screening of compounds with potential anti-hyperuricemic activity. In this study, 22 XO inhibitors were used to construct a 3D-QSAR pharmacophore model. Subsequently, molecular docking and in vitro activity evaluations were used to identify strong XO inhibitors from a list of 2000 natural compounds. The interaction mechanisms of these compounds with XO were analyzed based on inhibition kinetics and multi-spectral analyses. The pharmacophore model was composed of three hydrogen bond receptors and a hydrophobic center. The screened compounds — Diosmetin, Fisetin, and Genistein — all showed good XO inhibitory activity, with IC50 values of 1.86 ± 0.11 μM, 5.83 ± 0.08 μM, and 7.56 ± 0.10 μM, respectively. Kinetic analysis, fluorescence quenching assays, and molecular docking experiments showed that Diosmetin, Fisetin, and Genistein docked near the same active site of XO, mainly affecting the microenvironment of tryptophan residues. These molecules showed static binding to XO via hydrogen bonds, hydrophobic interactions, and van der Waals forces. Diosmetin and Genistein were competitive inhibitors, whereas Fisetin was a mixed inhibitor. Infrared spectroscopy showed that Diosmetin, Fisetin, and Genistein increased the α-helix content of XO from 7.4 % to 16.6 %, 21.4 %, and 11.2 %, respectively, thereby enhancing its stability. In summary, the pharmacophore model constructed in this study was accurate. The flavonoids Diosmetin, Fisetin, and Genistein effectively inhibited the activity of XO, and the amino acid residues LEU257, ILE353, and VAL259 played a key role in the interaction between the flavonoids and XO. These findings are of great significance for the screening and development of new XO inhibitors.

Integrating computational 3D-QSAR and pharmacophore modelling in diverse natural phytochemicals library for targeted therapy against breast cancer estrogen receptors

Breast cancer stands as a pervasive global health challenge, profoundly impacting the physical and emotional well-being of individuals. The relentless growth of malignant cells within breast tissues necessitates comprehensive research into the disease's origin, early detection, and treatment strategies. This study focused on constructing pharmacophores and 3D-QSAR models to dissect the crucial structure attributes essential for inhibiting Estrogen receptors. By employing ligand-based pharmacophore modeling and Atom-based-3D-QSAR techniques within the Schrödinger Phase module, we analyzed the inhibitory activity of 202 natural compounds and Tamoxifen as the control drug. To identify promising candidates, five molecules (Genistein, Coumestrol, Apigenin, Emodin, and Daidzein) were strategically chosen based on prediction from the 3D-QSAR pharmacophore model. These molecules are further assessed through model validation and virtual screening. The generated pharmacophore model (ADHR.10) reveals key features like hydrogen bond donors (HBD), hydrogen bond acceptors (HBA), and hydrophobic interactions. The resulting 3D-QSAR model predicted and stability was validated through the Xternal validation plus tool. The ADHR.10 model guided the systematic screening of natural compounds, resulting in the identification of the top five hits with promising pharmacological activity. Molecular docking scores demonstrated the compound's binding affinity with ESR1 (PDB ID: 6PSJ) and ESR2 (PDB ID: 1QKM). One standout compound, Coumestrol, displayed an exceptional binding affinity with ESR2 outperforming Tamoxifen. The top compounds exhibited both strong binding and stability, making them a promising candidate. Furthermore, Molecular Dynamics Simulation (MDS) demonstrated Coumestrol's superior stability compared to the control drug against the ESR1 and ERS2 proteins. Coumestrol exhibits lower average fluctuation in RMSD and residue-specific dynamics (RMSF) indicating stability than control. Specifically, designed compounds, rooted in natural sources, demonstrated excellent potential as breast cancer treatment candidates. These findings highlight them as promising lead candidates for the development of targeted breast cancer therapies and emphasize the potential of computational approaches in discovering new therapeutic avenues.

Identification of novel PHGDH inhibitors based on computational investigation: an all-in-one combination strategy to develop potential anti-cancer candidates.

Biological studies have elucidated that phosphoglycerate dehydrogenase (PHGDH) is the rate-limiting enzyme in the serine synthesis pathway in humans that is abnormally expressed in numerous cancers. Inhibition of the PHGDH activity is thought to be an attractive approach for novel anti-cancer therapy. The development of structurally diverse novel PHGDH inhibitors with high efficiency and low toxicity is a promising drug discovery strategy. A ligand-based 3D-QSAR pharmacophore model was developed using the HypoGen algorithm methodology of Discovery Studio. The selected pharmacophore model was further validated by test set validation, cost analysis, and Fischer randomization validation and was then used as a 3D query to screen compound libraries with various chemical scaffolds. The estimated activity, drug-likeness, molecular docking, growing scaffold, and molecular dynamics simulation processes were applied in combination to reduce the number of virtual hits. The potential candidates against PHGDH were screened based on estimated activity, docking scores, predictive absorption, distribution, metabolism, excretion, and toxicity (ADME/T) properties, and molecular dynamics simulation. Finally, an all-in-one combination was employed successfully to design and develop three potential anti-cancer candidates.

Mechanism of emergency phytoremediation technology based on a 3D-QSAR pharmacological model.

The ability of transgenic plants to respond to sudden environmental pollution accidents has become viable. Nonetheless, there is a dearth of research regarding the mechanism by which transgenic plants degrade organic pollutants. Hence, this study aimed to elucidate the process of organic pollutant degradation by plants, offering theoretical support for the application of transgenic plant emergency phytoremediation technology. In this investigation, we developed a 3D-QSAR pharmacophore model to represent the collective impact of plant resistance and phytodegradation. This was achieved by employing integrated effect values following treatment with a sine function approach. Moreover, we have undertaken an inaugural exploration of the coregulatory mechanism involved in plant resistance and pollutant degradation within plants. Additionally, we applied virtual molecular modification techniques for analysis and validation, striving for a more indepth understanding of the molecular-level enhancement mechanism related to the degradation of pollutants within plant organisms. The mechanism analysis results of the Hypo 1 pharmacophore model were verified, indicating that hydrophobic characteristics affect the resistance and degradation of PCBs in plants, significantly affecting the degradation effect of pollutants in plants.

Discovery of novel CXCR4 inhibitors for the treatment of inflammation by virtual screening and biological evaluation

C-X-C chemokine receptor type 4 (CXCR4) exerts considerable influence on the pathogenesis of inflammatory disorders and offers a potent avenue for drug intervention. This research utilizes a hybrid virtual screening methodology constructed using computer-aided drug design to discover novel CXCR4 inhibitors for the treatment of inflammation. First, a compound library was screened by Lipinski's five rules and adsorption, distribution, metabolism, excretion and toxicity properties. Second, the HypoGen algorithm was used in constructing a 3D-QSAR pharmacophore model and verify it layer by layer, and the obtained optimal pharmacophore 1 (Hypo 1) was used as a 3D query for compound screening. Then, hit compounds were obtained through molecular docking (Libdock and CDOCKER). The toxicity of the compounds to MDA-MB-231 cells was evaluated in vitro, and their binding affinity to the target was evaluated according to how they compete with 12G5 antibody for CXCR4 on the surfaces of the MDA-MB-231 cells. Compound Hit14 showed the strongest binding affinity among the hit compounds and inhibited cell migration and invasion in Matrigel invasion and wound healing assay at a concentration of 100 nM, demonstrating a better effect than AMD3100. Western Blot experiments further showed that Hit14 blocked the CXCR4/CXCL12-mediated phosphorylation of Akt. Meanwhile, cellular thermal displacement assay analysis showed that CXCR4 protein bound to Hit14 had high thermal stability. Finally, through in vivo experiments, we found that Hit14 inhibited mouse ear inflammation and reduced ear swelling and damage. Therefore, Hit14 is a promising drug for the further development of CXCR4 inhibitors for inflammation treatment.

3D-QSAR pharmacophore modeling, virtual screening, molecular docking, MD simulations, in vitro and in vivo studies to identify potential anti-hyperplasia drugs.

Psoriasis is a common immune-mediated skin condition characterized by aberrant keratinocytes and cell proliferation. The purpose of this study was to explore the FDA-approved drugs by 3D-QSAR pharmacophore model and evaluate their efficiency by in-silico, in vitro, and in vivo psoriasis animal model. A 3D-QSAR pharmacophore model was developed by utilizing HypoGen algorithm using the structural features of 48 diaryl derivatives with diverse molecular patterns. The model was validated by a test set of 27 compounds, by cost analysis method, and Fischer's randomization test. The correlation coefficient of the best model (Hypo2) was 0.9601 for the training set while it was 0.805 for the test set. The selected model was taken as a 3D query for the virtual screening of over 3000 FDA-approved drugs. Compounds mapped with the pharmacophore model were further screened through molecular docking. The hits that showed the best docking results were screened through in silico skin toxicity approach. Top five hits were selected for the MD simulation studies. Based on MD simulations results, the best two hit molecules, that is, ebastine (Ebs) and mebeverine (Mbv) were selected for in vitro and in vivo antioxidant studies performed in mice. TNF-α and COX pro-inflammatory mediators, biochemical assays, histopathological analyses, and immunohistochemistry observations confirmed the anti-inflammatory response of the selected drugs. Based on these findings, it appeared that Ebs can effectively treat psoriasis-like skin lesions and down-regulate inflammatory responses which was consistent with docking predictions and could potentially be employed for further research on inflammation-related skin illnesses such as psoriasis.

3D-QSAR-based pharmacophore modelling of quinazoline derivatives for the identification of acetylcholinesterase inhibitors through virtual screening, molecular docking, molecular dynamics and DFT studies

Alzheimer’s disease (AD) is a progressive neurological disorder responsible for the cognitive dysfunction and cognitive impairment in the patients. Acetylcholinesterase inhibitors (AChEIs) are used to treat AD however, these only provided symptomatic relief and more efficient drug molecules are desired for the effective treatment of the disease. In this article, ligand-based drug-designing strategy was used to develop and validate a field-based 3D-QSAR pharmacophore model on quinazoline-based AChEIs reported in the literature. The validated pharmacophore model (AAAHR_1) was used as a prefilter to screen an ASINEX database via virtual screening workflow (VSW). The hits generated were subjected to MM-GBSA to identify potential AChEIs and top three scoring molecules (BAS 05264565, LEG 12727144 and SYN 22339886) were evaluated for thermodynamic stability at the target site using molecular dynamic simulations. Additionally, DFT study was performed to predict the reactivity of lead molecules towards acetylcholinesterase (AChE). Thus, by utilising various computational tools, three molecules were identified as potent AChEIs that can be developed as potential drug candidates for the treatment of AD. Communicated by Ramaswamy H. Sarma

Lead generation of UPPS inhibitors targeting MRSA: Using 3D-QSAR pharmacophore modeling, virtual screening, molecular docking, and molecular dynamic simulations

Undecaprenyl Pyrophosphate Synthase (UPPS) is a vital target enzyme in the early stages of bacterial cell wall biosynthesis. UPPS inhibitors have antibacterial activity against resistant strains such as MRSA and VRE. In this study, we used several consecutive computer-based protocols to identify novel UPPS inhibitors. The 3D QSAR pharmacophore model generation (HypoGen algorithm) protocol was used to generate a valid predictive pharmacophore model using a set of UPPS inhibitors with known reported activity. The developed model consists of four pharmacophoric features: one hydrogen bond acceptor, two hydrophobic, and one aromatic ring. It had a correlation coefficient of 0.86 and a null cost difference of 191.39, reflecting its high predictive power. Hypo1 was proven to be statistically significant using Fischer’s randomization at a 95% confidence level. The validated pharmacophore model was used for the virtual screening of several databases. The resulting hits were filtered using SMART and Lipinski filters. The hits were docked into the binding site of the UPPS protein, affording 70 hits with higher docking affinities than the reference compound (6TC, − 21.17 kcal/mol). The top five hits were selected through extensive docking analysis and visual inspection based on docking affinities, fit values, and key residue interactions with the UPPS receptor. Moreover, molecular dynamic simulations of the top hits were performed to confirm the stability of the protein–ligand complexes, yielding five promising novel UPPS inhibitors.Graphical

Unveiling Arformoterol as a potent LSD1 inhibitor for breast cancer treatment: A comprehensive study integrating 3D-QSAR pharmacophore modeling, molecular docking, molecular dynamics simulations and in vitro assays

Lysine Specific Demethylase 1 (LSD1) has been identified as a chromatin-modifying enzyme implicated in various cancer pathogeneses, highlighting the potential for novel epigenetic cancer treatments through the development of effective inhibitors. We employed 3D-QSAR pharmacophore modeling, molecular docking, and molecular dynamics simulations to identify a promising drug candidate for LSD1 inhibition. RMSD, RMSF, H-bond, and DSSP analysis demonstrated that ZINC02599970 (Arformoterol) and ZINC13453966 exhibited the highest LSD1 inhibitory potential.Experimental validation using MCF-7 and MDA-MB-231 cell lines revealed that Arformoterol displayed potent antiproliferative activity with IC50 values of 12.30 ± 1.48 μM and 19.69 ± 1.15 μM respectively. In contrast, the IC50 values obtained for the control (tranylcypromine) in exposure to MCF-7 and MDA-MB-231 cells were 104.6 ± 1.69 μM and 77 ± 0.67 μM, respectively. Arformoterol demonstrated greater LSD1 inhibitory potency in MCF-7 cells compared to MDA-MB-231 cells. Also, the expression of genes involved in chromatin rearrangement (LSD1), angiogenesis (VEGF1), cell migration (RORα), signal transduction (S100A8), apoptosis, and cell cycle (p53) were investigated. Arformoterol enhanced apoptosis and induced cell cycle arrest at the G2/M phase, both in MCF-7 and MDA-MB-231 cancer cells. Based on our findings, we propose that Arformoterol represents a promising candidate for breast cancer treatment, owing to its potent LSD1 inhibitory activity.

STRUCTURE-BASED DESIGN OF NOVEL PYRIMIDINE CARBONITRILES ANALOGS TARGETING THE CYSTEINE PROTEASE FALCIPAIN 2 OF PLASMODIUM FALCIPARUM (pfFP2) AT THE TROPHOZOÏTE STAGE WITH FAVORABLE ADME SPECIFICITIES

Aim and Objective: Structure-based drug design (SBDD) of new antimalarials at the moment of resistance of the most causative agent, Plasmodium falciparum to the more valuable artemisinin combination therapy (ACT) is even more urgent. Carbonitriles pyrimidine derivatives (CNP) has emerged as potential inhibitors of the cysteine protease falcipain 2 of Plasmodium falciparum (pfFP2), so here we report virtual pharmacophore based screening of the CNP chemical subspace yielding novel CNP analogs with predicted high inhibitory potency against pfFP2. Methods: A quantitative structure activity relationships (QSAR) complexation model has been developed from a series of fifteen carbonitriles pyrimidine derivatives to establish a linear correlation between the calculated Gibbs free energies (GFE: ΔΔGcom) of pfFP2-CNP complex formation and the experimental half-maximal enzymatic inhibition concentration ( ).The predictive power of the QSAR model was then validated with the generation of a 3D-QSAR-PH4 pharmacophore (PH4) model as CNP chemical subspace (exemplified as a virtual combinatorial library of more than 83.300 CNP analogs) explorer for novel predicted more potent CNP analogs. Finally the best PH4 hits were evaluated with the initial QSAR model for predicted potency ( ) and pharmacokinetic profile. Results: The QSAR model linear correlation equation: p = -0.1025 x ∆∆Gcom + 7.2867, R2=0.94, the subsequent PH4 model linear correlation between experiment and PH4-estimated IC50: p = 0.9366 x p + 0.2849, R2=0.91 documents the high predictive power of this approach. Finally the screening of the virtual library of CNP analogs yielded 52 orally bioavailable candidates the best reaching a predicted potency ( ) of 14 pM and displaying favorable pharmacokinetic profile. Conclusion: The combined use of one descriptor complexation QSAR model and 3D-QSAR Pharmacophore model performs well in identifying novel CNP analogs against pfFP2 and the handful of top predicted analogs are worth undergoing synthesis and biological evaluation. Peer Review History: Received: 12 August 2023; Revised: 17 September; Accepted: 27 October, Available online: 15 November 2023 Academic Editor: Prof. Dr. Gorkem Dulger, Duzce University, Turkey, gorkemdulger@yandex.com Received file: Reviewer's Comments: Average Peer review marks at initial stage: 7.0/10 Average Peer review marks at publication stage: 9.0/10 Reviewers: Prof. Hassan A.H. Al-Shamahy, Sana'a University, Yemen, shmahe@yemen.net.ye Prof. Cyprian Ogbonna ONYEJI, Obafemi Awolowo University, Ile-Ife, Nigeria, conyeji@oauife.edu.ng Dr. U. S. Mahadeva Rao, Universiti Sultan Zainal Abidin, Terengganu Malaysia, raousm@gmail.com Dr. Hayriye Eda Şatana Kara, Gazi University, Turkey, eda@gazi.edu.tr

Identification of novel curcumin derivatives against pancreatic cancer: a comprehensive approach integrating 3D-QSAR pharmacophore modeling, virtual screening, and molecular dynamics simulations

Pancreatic cancer, known as the “silent killer,” poses a daunting challenge in cancer therapy. The dysregulation of the PI3Kα signaling pathway in pancreatic cancer has attracted considerable interest as a promising target for therapeutic intervention. In this regard, the use of curcumin derivatives as inhibitors of PI3Kα has emerged, providing a novel and promising avenue for developing effective treatments for this devastating disease. Computational approaches were employed to explore this potential and investigate 58 curcumin derivatives with cytotoxic activity against the Panc-1 cell line. Our approach involved ligand-based pharmacophore modeling and atom-based 3D-QSAR analysis. The resulting QSAR model derived from the best-fitted pharmacophore hypothesis (AAHRR_1) demonstrated remarkable performance with high correlation coefficients (R2) of 0.990 for the training set and 0.977 for the test set. The cross-validation coefficient (Q2) of 0.971 also validated the model’s predictive power. Tropsha’s recommended criteria, including the Y-randomization test, were employed to ensure its reliability. Furthermore, an enrichment study was conducted to evaluate the model’s performance in identifying active compounds. AAHRR_1 was used to screen a curated PubChem database of curcumin-related compounds. Two molecules (CID156189304 and CID154728220) exhibited promising pharmacokinetic properties and higher docking scores than Alpelisib, warranting further investigation. Extensive molecular dynamics simulations provided crucial insights into the conformational dynamics within the binding site, validating their stability and behavior. These findings contribute to our understanding of the potential therapeutic effectiveness of these compounds as PI3Kα inhibitors in pancreatic cancer. Communicated by Ramaswamy H. Sarma

Discovery of novel S6K1 inhibitors by an ensemble-based virtual screening method and molecular dynamics simulation.

Ribosomal protein S6 kinase beta-1 (S6K1) is considered a potential target for the treatment of various diseases, such as obesity, type II diabetes, and cancer. Development of novel S6K1 inhibitors is an urgent and important task for the medicinal chemists. In this research, an effective ensemble-based virtual screening method, including common feature pharmacophore model, 3D-QSAR pharmacophore model, naïve Bayes classifier model, and molecular docking, was applied to discover potential S6K1 inhibitors from BioDiversity database with 29,158 compounds. Finally, 7 hits displayed considerable properties and considered as potential inhibitors against S6K1. Further, carefully analyzing the interactions between these 7 hits and key residues in the S6K1 active site, and comparing them with the reference compound PF-4708671, it was found that 2 hits exhibited better binding patterns. In order to further investigate the mechanism of the interactions between 2 hits and S6K1 at simulated physiological conditions, the molecular dynamics simulation was performed. The ΔGbind energies for S6K1-Hit1 and S6K1-Hit2 were - 111.47 ± 1.29 and - 54.29 ± 1.19kJmol-1, respectively. Furthermore, deep analysis of these results revealed that Hit1 was the most stable complex, which can stably bind to S6K1 active site, interact with all of the key residues, and induce H1, H2, and M-loop regions changes. Therefore, the identified Hit1 may be a promising lead compound for developing new S6K1 inhibitor for various metabolic diseases treatment.

Discovery of novel VEGFR2-TK inhibitors by phthalimide pharmacophore based virtual screening, molecular docking, MD simulation and DFT

Currently, numerous potent chemotherapeutic agents are available in the market but most of them show poor pharmacokinetics, lethal effects and drug resistance during their enduring use. The increased cancer cases, deaths and need of better treatment stimulates us to give newer lifesaving anticancer drugs. The phthalimide derivatives are structurally diverse and exert potential anticancer activity. In this regard, the 3D QSAR Pharmacophore model was developed and validated using fifty-eight phthalimide derivatives. The validation parameters corroborated the reliability and statistical robustness of CEASER Hypo 1. Three databases—NCI Open, Drug Bank, and Asinex were submitted to ADMET and drug-like filtering; 117893 drug-like compounds were mapped on CEASER Hypo 1; and 362 hits with IC50 <1 µM were discovered. These hits were docked on VEGFR2-TK, and in the form of results fifteen hits exhibited greater affinity than sorafenib. The top lead ASN 03206926 was subjected for MD simulation (100 ns) and RMSD, Rg, RMSF, number of hydrogen bonds, and SASA verified that the complex was stable, rigid and highly compact. Results demonstrated GLU885, PHE918, CYS919, LYS920, HIS1026, CYS1045, ASP1046 are the essential residues for favourable interactions. The binding free energy calculations support the affinity and stability revealed by docking and MD simulation. The DFT calculations, negative binding energy and lower HOMO-LUMO band gap revealed that the process is spontaneous and ASN 03206926 is very reactive. Following extensive analysis we suggest that the ASN 03206926 might be employed as a new VEGFR2-TK inhibitor for the treatment of breast and VEGFR2-TK associated cancers. Communicated by Ramaswamy H. Sarma

Study on the molecular mechanism of anti-liver cancer effect of Evodiae fructus by network pharmacology and QSAR model.

Introduction: Evodiae Fructus (EF) is the dried, near ripe fruit of Euodia rutaecarpa (Juss.) Benth in Rutaceae. Numerous studies have demonstrated its anti-liver cancer properties. However, the molecular mechanism of Evodiae fructus against liver cancer and its structure-activity connection still require clarification. Methods: We utilized network pharmacology and a QSAR (2- and 3-dimensional) model to study the anti-liver cancer effect of Evodiae fructus. First, by using network pharmacology to screen the active substances and targets of Evodiae fructus, we investigated the signaling pathways involved in the anti-liver cancer actions of Evodiae fructus. The 2D-QSAR pharmacophore model was then used to predict the pIC50 values of compounds. The hiphop method was used to create an ideal 3D-QSAR pharmacophore model for the prediction of Evodiae fructus compounds. Finally, molecular docking was used to validate the rationality of the pharmacophore, and molecular dynamics was used to disclose the stability of the compounds by assessing the trajectories in 10ns using RMSD, RMSF, Rg, and hydrogen bonding metrics. Results: In total, 27 compounds were acquired from the TCMSP and TCM-ID databases, and 45 intersection targets were compiled using Venn diagrams. Network integration analysis was used in this study to identify SRC as a primary target. Key pathways were discovered by KEGG pathway analysis, including PD-L1 expression and PD-1 checkpoint pathway, EGFR tyrosine kinase inhibitor resistance, and ErbB signaling pathway. Using a 2D-QSAR pharmacophore model and the MLR approach to predict chemical activity, ten highly active compounds were found. Two hydrophobic features and one hydrogen bond acceptor feature in the 3D-QSAR pharmacophore model were validated by training set chemicals. The results of molecular docking revealed that 10 active compounds had better docking scores with SRC and were linked to residues via hydrogen and hydrophobic bonds. Molecular dynamics was used to show the structural stability of obacunone, beta-sitosterol, and sitosterol. Conclusion:Pharmacophore 01 has high selectivity and the ability to distinguish active and inactive compounds, which is the optimal model for this study. Obacunone has the optimal binding ability with SRC. The pharmacophore model proposed in this study provides theoretical support for further screening effective anti-cancer Chinese herbal compounds and optimizing the compound structure.

Design and In silico Screening of Combinatorial Library of New Herbicidal Analogs of Cycloalka[d]quinazoline-2,4dione−Benzoxazinones Inhibiting Protoporphyrinogen IX Oxidase

We virtually design here an important new weedkiller Cycloalka[d]quinazoline-2,4dione−Benzoxazinones (CQB), inhibitors of Protoporphyrinogen IX Oxidase (PPO). Based on computer-assisted combinatorial chemistry techniques, docking, 3D-QSAR and pharmacophore models we first enumerate, focus and in silico screen a virtual library of CQB analogs substituted at positions R1, R2 and R3. By docking inhibitors into the target active site from the crystal structure (PDB ID: 1SEZ) of PPO in complex with a CQB ligand, 3D models of 29 PPO:CQBx complexes with known observed activity (Kiexp) were prepared to establish a quantitative structure–activity (QSAR) model and linear correlation between relative Gibbs free energy (GFE) of receptor-ligand complex formation (ΔΔGcom) and Kiexp: pKiexp = -0.1664 ×ΔΔGcom + 8.306 (1); R2 = 0.94. A 3D QSAR pharmacophore model (PH4) derived from the QSAR directed our effort to design novel CQB analogs. During the design, an initial virtual library of 118 CQB was focused down and PH4 screened to identify 28 promising novel analogs. Their Ki (Kipre) values were predicted by means of equation (1). The most active analog namely CQB22 display Kipre 22 times superior to that of the reported most active training set ligand 17i. Our survey proposes this compounds to the synthesis and to the assessment on herbicidal.

Identification of novel BRD4 inhibitors by pharmacophore screening, molecular docking, and molecular dynamics simulation

Bromodomain-containing protein 4 (BRD4), as an epigenetic reader, can regulate chromatin structure and gene expression via recognizing and binding acetylated lysine in histones. BRD4 promotes the expression of tumor genes, such as c-Myc and Bcl-2, so it has been regarded as an attractive target for cancers. In this work, a hierarchical virtual screening approach was applied to identify potential BRD4 inhibitors from ZINC database. 3D-QSAR pharmacophore model was firstly constructed, validated and used for virtual screening. Molecular docking was then followed to evaluate the binding mode and interactions of screened compounds with BRD4. On this basis, the best three hits ZINC02126438, ZINC16645732, ZINC00856805 were selected for molecular dynamics simulation and binding free energy calculation. Among them, ZINC16645732 had a better binding free energy than the positive drug PF-1. Our results suggested that ZINC16645732 might be a potential BRD4 inhibitor that should undergo further experimental analysis and modification.

Pharmacophore based virtual screening of cholinesterase inhibitors: search of new potential drug candidates as antialzheimer agents.

Alzheimer’s disease (AD) is a distinctive medical condition characterized by loss of memory, orientation, and cognitive impairments, which is an exceptionally universal form of neurodegenerative disease. The statistical data suggested that it is the 3rd major cause of death in older persons. Butyrylcholinesterase (BChE) and acetylcholinesterase (AChE) inhibitors play a vital role in the treatment of AD. Coumarins, natural derivatives, are reported as cholinesterase inhibitors and emerges as a promising scaffold for design of ligands targeting enzymes and pathological alterations related to AD. In this regard, the 3D QSAR pharmacophore models were developed for coumarin scaffold containing BChE and AChE inhibitors. Several 3D QSAR pharmacophore models were developed with FAST, BEST, and CEASER methods, and finally, statistically robust models (based on correlation coefficient, cost value, and RMSE value) were selected for further analysis for both targets. The important features ((HBA 1, HBA 2, HY, RA (BChE) HBA 1, HBA 2, HY, PI, (AChE)) were identified for good inhibitory activity of coumarin derivatives. Finally, the selected models were applied to various database compounds to find potential BChE and AChE inhibitors, and we found 13 for BChE and 1 potent compound for AChE with an estimated activity of IC50 < 10 µM. Further, the Lipinski filters, and ADMET analysis supports the selected compounds to become a drug candidate. These selected BChE and AChE inhibitors can be used in the treatment of AD.Graphical abstract Supplementary InformationThe online version contains supplementary material available at 10.1007/s40203-022-00133-1.

Development of pyrrolidine and isoindoline derivatives as new DPP8 inhibitors using a combination of 3D-QSAR technique, pharmacophore modeling, docking studies, and molecular dynamics simulations

ABSTRACT Dipeptidyl peptidase proteins (DPPs) are a family of serine proteases with an active, vital role in multiple pathological conditions, including different types of cancer and autoimmune diseases. DPP inhibitors can induce an inflammatory form of cell death by activating inflammasome sensors and pro-caspase 1. This study used docking calculations and extended molecular dynamics simulations to investigate the interaction patterns for DPP8-inhibitor complex structures. We also implemented a novel procedure to develop new DPP8 inhibitors using pharmacophore and CoMFA models, docking studies, and molecular dynamics simulations. Our results showed the critical role of Glu 275, Glu 276, and Tyr 788 residues in the active site of protein in DPP8-inhibitor interaction. We also provided a platform for comparing the effectiveness of the most important DPP8 inhibitors. Moreover, we introduced a new 1G244 derivative with the highest affinity among all the studied compounds to inhibit DPP8 protein.

Lead generation of cysteine based mesenchymal epithelial transition (c-Met) kinase inhibitors: Using structure-based scaffold hopping, 3D-QSAR pharmacophore modeling, virtual screening, molecular docking, and molecular dynamics simulation

Cysteine-based mesenchymal–epithelial transition (c-Met) is a receptor tyrosine kinase that plays a definitive role during cancer progression and was identified as a possible target for anti-angiogenesis drugs. In the present study, different protocols of computer-based drug design were performed. Construction of predictive pharmacophore model using HypoGen algorithm resulted in a validated model of four features of positive ionizable, hydrogen bond acceptor, hydrophobic, and ring aromatic features with a correlation coefficient of 0.87, a configuration cost of 14.95, and a cost difference of 357.92. The model revealed a promising predictive power and had >90% probability of representing true correlation with the activity data. The model was established using Fisher's validation test at the 95% confidence level and test set prediction (r = 0.96), furthermore, the model was validated by mapping of set of compounds undergoing clinical trials as class Ⅱ c-met inhibitors. The generated valid pharmacophore model was then anticipated for virtual screening of three data bases. Moreover, scaffold hopping using replace fragments protocol was implemented. Hits generated were filtered according to Lipinski's rule; 510 selected hits were anatomized and subjected to molecular docking studies into the crystal structure of c-Met kinase. The good correlation between docking scores and ligand pharmacophore mapping fit values provided a reliable foundation for designing new potentially active candidates that may target c-Met kinase. Eventually, eight hits were selected as potential leads. Subsequently, seven (Hits) have displayed a higher dock score and demonstrated key residue interactions with stable molecular dynamics simulation. Therefore, these c-Met kinase inhibitors may further serve as new chemical spaces in designing new compounds.