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Journal articles on the topic 'PHARMACOPHORE MODELING'

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Author: Grafiati
Published: 11 September 2023

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1

Kutlushina, Alina, Aigul Khakimova, Timur Madzhidov, and Pavel Polishchuk. "Ligand-Based Pharmacophore Modeling Using Novel 3D Pharmacophore Signatures." Molecules 23, no. 12 (November 27, 2018): 3094. http://dx.doi.org/10.3390/molecules23123094.

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Pharmacophore modeling is a widely used strategy for finding new hit molecules. Since not all protein targets have available 3D structures, ligand-based approaches are still useful. Currently, there are just a few free ligand-based pharmacophore modeling tools, and these have a lot of restrictions, e.g., using a template molecule for alignment. We developed a new approach to 3D pharmacophore representation and matching which does not require pharmacophore alignment. This representation can be used to quickly find identical pharmacophores in a given set. Based on this representation, a 3D pharmacophore ligand-based modeling approach to search for pharmacophores which preferably match active compounds and do not match inactive ones was developed. The approach searches for 3D pharmacophore models starting from 2D structures of available active and inactive compounds. The implemented approach was successfully applied for several retrospective studies. The results were compared to a 2D similarity search, demonstrating some of the advantages of the developed 3D pharmacophore models. Also, the generated 3D pharmacophore models were able to match the 3D poses of known ligands from their protein-ligand complexes, confirming the validity of the models. The developed approach is available as an open-source software tool: http://www.qsar4u.com/pages/pmapper.php and https://github.com/meddwl/psearch.
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Mortier, Jérémie, Pratik Dhakal, and Andrea Volkamer. "Truly Target-Focused Pharmacophore Modeling: A Novel Tool for Mapping Intermolecular Surfaces." Molecules 23, no. 8 (August 6, 2018): 1959. http://dx.doi.org/10.3390/molecules23081959.

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Pharmacophore models are an accurate and minimal tridimensional abstraction of intermolecular interactions between chemical structures, usually derived from a group of molecules or from a ligand-target complex. Only a limited amount of solutions exists to model comprehensive pharmacophores using the information of a particular target structure without knowledge of any binding ligand. In this work, an automated and customable tool for truly target-focused (T²F) pharmacophore modeling is introduced. Key molecular interaction fields of a macromolecular structure are calculated using the AutoGRID energy functions. The most relevant points are selected by a newly developed filtering cascade and clustered to pharmacophore features with a density-based algorithm. Using five different protein classes, the ability of this method to identify essential pharmacophore features was compared to structure-based pharmacophores derived from ligand-target interactions. This method represents an extremely valuable instrument for drug design in a situation of scarce ligand information available, but also in the case of underexplored therapeutic targets, as well as to investigate protein allosteric pockets and protein-protein interactions.
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Madzhidov, Timur I., Assima Rakhimbekova, Alina Kutlushuna, and Pavel Polishchuk. "Probabilistic Approach for Virtual Screening Based on Multiple Pharmacophores." Molecules 25, no. 2 (January 17, 2020): 385. http://dx.doi.org/10.3390/molecules25020385.

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Pharmacophore modeling is usually considered as a special type of virtual screening without probabilistic nature. Correspondence of at least one conformation of a molecule to pharmacophore is considered as evidence of its bioactivity. We show that pharmacophores can be treated as one-class machine learning models, and the probability the reflecting model’s confidence can be assigned to a pharmacophore on the basis of their precision of active compounds identification on a calibration set. Two schemes (Max and Mean) of probability calculation for consensus prediction based on individual pharmacophore models were proposed. Both approaches to some extent correspond to commonly used consensus approaches like the common hit approach or the one based on a logical OR operation uniting hit lists of individual models. Unlike some known approaches, the proposed ones can rank compounds retrieved by multiple models. These approaches were benchmarked on multiple ChEMBL datasets used for ligand-based pharmacophore modeling and externally validated on corresponding DUD-E datasets. The influence of complexity of pharmacophores and their performance on a calibration set on results of virtual screening was analyzed. It was shown that Max and Mean approaches have superior early enrichment to the commonly used approaches. Thus, a well-performing, easy-to-implement, and probabilistic alternative to existing approaches for pharmacophore-based virtual screening was proposed.
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Kumar, Saurav, Deepika Deepika, and Vikas Kumar. "Pharmacophore Modeling Using Machine Learning for Screening the Blood–Brain Barrier Permeation of Xenobiotics." International Journal of Environmental Research and Public Health 19, no. 20 (October 18, 2022): 13471. http://dx.doi.org/10.3390/ijerph192013471.

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Daily exposure to xenobiotics affects human health, especially the nervous system, causing neurodegenerative diseases. The nervous system is protected by tight junctions present at the blood–brain barrier (BBB), but only molecules with desirable physicochemical properties can permeate it. This is why permeation is a decisive step in avoiding unwanted brain toxicity and also in developing neuronal drugs. In silico methods are being implemented as an initial step to reduce animal testing and the time complexity of the in vitro screening process. However, most in silico methods are ligand based, and consider only the physiochemical properties of ligands. However, these ligand-based methods have their own limitations and sometimes fail to predict the BBB permeation of xenobiotics. The objective of this work was to investigate the influence of the pharmacophoric features of protein–ligand interactions on BBB permeation. For these purposes, receptor-based pharmacophore and ligand-based pharmacophore fingerprints were developed using docking and Rdkit, respectively. Then, these fingerprints were trained on classical machine-learning models and compared with classical fingerprints. Among the tested footprints, the ligand-based pharmacophore fingerprint achieved slightly better (77% accuracy) performance compared to the classical fingerprint method. In contrast, receptor-based pharmacophores did not lead to much improvement compared to classical descriptors. The performance can be further improved by considering efflux proteins such as BCRP (breast cancer resistance protein), as well as P-gp (P-glycoprotein). However, the limited data availability for other proteins regarding their pharmacophoric interactions is a bottleneck to its improvement. Nonetheless, the developed models and exploratory analysis provide a path to extend the same framework for environmental chemicals, which, like drugs, are also xenobiotics. This research can help in human health risk assessment by a priori screening for neurotoxicity-causing agents.
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AFFI, Sopi Thomas, Doh SORO, Souleymane COULIBALY, Bibata KONATE, and Nahossé ZIAO. "Modeling anticancer pharmacophore based on inhibition of HDAC7." SDRP Journal of Computational Chemistry & Molecular Modeling 5, no. 3 (2021): 657–63. http://dx.doi.org/10.25177/jccmm.5.3.ra.10776.

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Histone deacetylases (HDACs) are the target inhibition enzymes in cancer treatment via chemotherapy. Application of this therapeutic technique requires the use of drugs whose side effects are reduced and tiny with necessary safety. In this study, the methods and tools of pharmacophore modeling were used to investigate ten molecules known for their anticancer properties. Particular attention has been given to pinpoint a promising anti-cancer pharmacophore in order to lead new effective inhibitors. Using Discovery Studio 2.5 software, the ten compounds were docked within the active site of the HDAC7 enzyme. Analysis of the binding characteristics of all the compounds collected and tested in the model resulted in the characteristics produced by the 3D pharmacophore of the selected hypothesis. This led to note that the efficiency of any HDAC enzyme inhibitor was related to the characteristics of the designed pharmacophore. At the end, the pharmacophore hypothesis used here was presented as a useful basis for the development of anticancer compounds. Keywords: Pharmacophore, 3c0z, HDAC7, QSAR
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Mansi, Iman A., Mahmoud A. Al-Sha'er, Nizar M. Mhaidat, Mutasem O. Taha, and Rand Shahin. "Investigation of Binding Characteristics of Phosphoinositide-dependent Kinase-1 (PDK1) Co-crystallized Ligands Through Virtual Pharmacophore Modeling Leading to Novel Anti-PDK1 Hits." Medicinal Chemistry 16, no. 7 (November 6, 2020): 860–80. http://dx.doi.org/10.2174/1573406415666190724131048.

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Background: 3-Phosphoinositide Dependent Protein Kinase-1 (PDK1) is being lately considered as an attractive and forthcoming anticancer target. A Protein Data Bank (PDB) cocrystallized crystal provides not only rigid theoretical data but also a realistic molecular recognition data that can be explored and used to discover new hits. Objective: This incited us to investigate the co-crystallized ligands' contacts inside the PDK1 binding pocket via a structure-based receptor-ligand pharmacophore generation technique in Discovery Studio 4.5 (DS 4.5). Methods: Accordingly, 35 crystals for PDK1 were collected and studied. Every single receptorligand interaction was validated and the significant ones were converted into their corresponding pharmacophoric features. The generated pharmacophores were scored by the Receiver Operating Characteristic (ROC) curve analysis. Results: Consequently, 169 pharmacophores were generated and sorted, 11 pharmacophores acquired good ROC-AUC results of 0.8 and a selectivity value above 8. Pharmacophore 1UU3_2_01 was used in particular as a searching filter to screen NCI database because of its acceptable validity criteria and its distinctive positive ionizable feature. Several low micromolar PDK1 inhibitors were revealed. The most potent hit illustrated anti-PDK1 IC50 values of 200 nM with 70% inhibition against SW480 cell lines. Conclusion: Eventually, the active hits were docked inside the PDK1 binding pocket and the recognition points between the active hits and the receptor were analyzed that led to the discovery of new scaffolds as potential PDK1 inhibitors.
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Kadu, Nilesh S., and Atul V. Ingle. "Three-Dimensional Pharmacophore Modeling of Betulonic Acid Derivatives as a Strong Inhibitor of Human Coronavirus-229E Replication." International Journal of Science and Healthcare Research 6, no. 2 (July 1, 2021): 356–61. http://dx.doi.org/10.52403/ijshr.20210462.

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These-days, pharmacophore approaches have become one of the foremost tools in drug discovery after the past century’s development. Numerous ligand-based and structure-based strategies are developed for improved pharmacophore modeling with success and extensively applied in virtual screening, de novo design and lead improvement. Till now, there is little information on 3D-pharmacophore studies of 1,2,3-triazolo-fused betulonic acid derivatives as a strong inhibitor for human coronavirus-229E replication. Here, we tend to report the appliance of pharmacophore modeling for betulonic acid derivatives as an inhibitor. This study has been undertaken to realize intuitions into molecular mechanisms and structural necessities crucial for potential inhibition of betulonic acid derivatives. The standard procedure was adapted to develop the pharmacophoric models. It is found that the pharmacophore model of active betulonic acid derivative (compound 5h) unveils the importance of five- and six-member aliphatic cyclic hydrocarbon moiety, aromatic ring, –OH group of carboxylic acid, five-member heterocyclic rings (triazolo) and aliphatic alkene group and their correlation with the biological activity. It may be helpful within the design of novel betulonic acid derivatives inhibitors for human coronavirus-229E replication. Keywords: Pharmacophore Model, Antiviral activity, HCoV-229E replication, Betulonic acid derivatives, nsp15.
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Mendez, Nixon, and Md Afroz Alam. "Structural Features of Quercetin Derivatives by Using Pharmaco-phore Modeling Approach." Open Pharmaceutical Sciences Journal 3, no. 1 (June 6, 2016): 79–98. http://dx.doi.org/10.2174/1874844901603010079.

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Background:Quercetin which is a natural occurring flavonoid, exert a direct pro-apoptotic effect on tumor cells by blocking the growth of several cancer cell lines at different phases of the cell cycle. Quercetin derivatives have attracted considerable attention for their cytotoxity against human cancer cell lines. In this study the derivatives of Quercetin were used for docking followed by pharmacophore modeling for studying the 3D features and configurations responsible for biological activity of structurally diverse compounds.Objective:To develop a model which depicts the crucial structural features responsible for anti-lung cancer activities.Method:A robust pharmacophore developed for the receptor have been analyzed to identify potential areas of selectivity in the hyperspace of 3D pharmacophores that may lead to the discovery of anti-lung cancer drug or such compounds which could serve as templates for the design of new molecules as potential anti lung cancer agents.Results:The generated best pharmacophore hypothesis yielded a statistically significant 3D-QSAR model, with a correlation coefficient of R2= 0.86 for training set and R2= 0.76 for the test set molecules. The Cross validation regression coefficient is Q2= 0.84 for training set and Q2= 0.5 for test set molecules.Conclusion:The R2and Q2reveals that pharmacophore model provide insights into the structural and chemical features of the EGFR inhibitors of Quercetin derivatives that can be used as lead compound for further synthesis as well as for screening other similar novel inhibitors of EGFR.
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9

Thai, Khac-Minh, Trieu-Du Ngo, Thanh-Dao Tran, and Minh-Tri Le. "Pharmacophore Modeling for Antitargets." Current Topics in Medicinal Chemistry 13, no. 9 (May 1, 2013): 1002–14. http://dx.doi.org/10.2174/1568026611313090004.

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10

Guner, Osman, and J. Bowen. "Pharmacophore Modeling for ADME." Current Topics in Medicinal Chemistry 13, no. 11 (June 1, 2013): 1327–42. http://dx.doi.org/10.2174/15680266113139990037.

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11

Engels, Maida, Se Balaji B, Divakar S., and Geetha G. "LIGAND BASED PHARMACOPHORE MODELING, VIRTUAL SCREENING AND MOLECULAR DOCKING STUDIES TO DESIGN NOVEL PANCREATIC LIPASE INHIBITORS." International Journal of Pharmacy and Pharmaceutical Sciences 9, no. 4 (February 14, 2017): 48. http://dx.doi.org/10.22159/ijpps.2017v9i4.16392.

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Objective: To understand the essential structural features required for pancreatic lipase (PL) inhibitory activity and to design novel chemical entities, ligand-based pharmacophore modeling, virtual screening and docking studies were carried out.Methods: The pharmacophore model was generated based on 133 compounds with PL inhibitory activity using PHASE. An external test set and decoy dataset methods were applied to validate the hypothesis and to retrieve potential PL inhibitors. The generated hypothesis model was further subjected to virtual screening and molecular docking studies.Results: A five point pharmacophoric hypothesis model which consists of three hydrogen bond acceptor sites and two hydrophobic sites was developed. The generated pharmacophore gave significant 3D QSAR (three-dimensional Quantitative Structural Activity Relationship) model with r2 of 0.9389 and Q2 value of 0.4016. After database screening, five molecules were found to have better glide scores and binding interactions with the active site amino acid residues.Conclusion: As an outcome of this study, five hit molecules were suggested as potent PL inhibitors as they showed good glide scores as well as binding interactions with required active site amino acids. The five molecules obtained from this study may serve as potential leads for the development of promising anti-obesity agents.
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de Groot, Marcel J., and Sean Ekins. "Pharmacophore modeling of cytochromes P450." Advanced Drug Delivery Reviews 54, no. 3 (March 2002): 367–83. http://dx.doi.org/10.1016/s0169-409x(02)00009-1.

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13

Hariono, Maywan, and Habibah A. Wahab. "Pharmacophore Modeling of N1-alkyltheobromine as Histamine-H1 Receptor Antagonist." International Journal of Modeling and Optimization 5, no. 2 (April 2015): 98–103. http://dx.doi.org/10.7763/ijmo.2015.v5.443.

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14

Lu, Xin, Hongyu Yang, Yao Chen, Qi Li, Si-yu He, Xueyang Jiang, Feng Feng, Wei Qu, and Haopeng Sun. "The Development of Pharmacophore Modeling: Generation and Recent Applications in Drug Discovery." Current Pharmaceutical Design 24, no. 29 (December 8, 2018): 3424–39. http://dx.doi.org/10.2174/1381612824666180810162944.

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Background: The pharmacophore concept in modern drug research is of great importance and promotes the development of drug industry as indicated by the number of publications available. Methods: : In this article, we reviewed and highlighted some successful examples of pharmacophore modeling, which was applied either in virtual screening for efficient hit discovery or in the optimization of the lead compounds. Meanwhile, the analysis of some important aspects of pharmacophore modeling such as a database, the software was listed as well. <p> Results: Based on the analysis of these examples, we intended to provide an overview of pharmacophore-based virtual screening, which we hope to help readers gain insight into this powerful technique. Conclusion: Owing to its’ convenience and efficiency, pharmacophore presents an essential method for drug discovery.
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Shirbhate E., Divya, V. K. Patel, P. Patel, R. Veerasamy, T. Jawaid, M. Kamal, and H. Rajak. "LEAD IDENTIFICATION OF HYDROXAMATE DERIVATIVE AS SELECTIVE HDAC2 INHIBITOR USING COMPUTATIONAL APPROACHES." INDIAN DRUGS 57, no. 07 (October 8, 2020): 26–39. http://dx.doi.org/10.53879/id.57.07.12042.

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Histone deacetylase (HDAC) inhibitors have been established as a novel class of anticancer agents. The HDAC enzyme plays a vital role in gene transcription for regulation of cell proliferation, migration and apoptosis, immune pathways and angiogenesis. In this work, a series of 49 hydroxamate derivatives with available IC50 data were analyzed by computational method for the identification of leads. 3D-QSAR and pharmacophore modeling investigation were accomplished to identify the crucial pharmacophoric features and correlate 3D-chemical structure with HDAC inhibitory activity. The e-pharmacophore script and phase module were used for development of pharmacophore hypotheses, which characterized the 3D arrangement of molecular features necessary for the presence of biological activity. The 3D-QSAR analyses were carried out for five partial least square (PLS) factor model with excellent information and predictive ability, acquired R2 =0.9824, Q2 =0.8473 and with low standard deviation SD=0.2161. Molecular docking studies showed intermolecular interactions between small molecules and some amino acids, such as GLY140, Zn501, HIS132 and PHE 141 with good GlideScore as compared with that of vorinostat (SAHA).
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Kumar, Sivakumar Prasanth, and Prakash Chandra Jha. "Multi-Pharmacophore Modeling of Caspase-3 Inhibitors using Crystal, Dock and Flexible Conformation Schemes." Combinatorial Chemistry & High Throughput Screening 21, no. 1 (March 20, 2018): 26–40. http://dx.doi.org/10.2174/1386207321666180102114917.

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Aim and Objective: Numerous caspase-3 drug discovery projects were found to have relied on single receptor as the template to recognize most promising small molecule candidates using docking approach. Alternatively, some researchers were contingent upon ligand-based alignment to build up an empirical relationship between ligand functional groups and caspase-3 inhibitory activity quantitatively. To connect both caspase-3 receptor details and its inhibitors chemical functionalities, this study was undertaken to develop receptor- and ligand-pharmacophore models based on different conformational schemes. Material and Methods: A multi-pharmacophore modeling strategy is carried out based on three conformational schemes of pharmacophore hypothesis generation to screen caspase-3 inhibitors from database. The schemes include (i) flexible (conformations unrestricted or flexible during pharmacophore mapping), (ii) dock (conformations obtained using FlexX docking method) and (iii) crystal (extracted from multiple caspase-3-ligand complexes from PDB repository) conformations of query ligands. The pharmacophore models developed using these conformational schemes were then used to identify probable caspase-3 inhibitors from ZINC database. Results: We noticed better sensitivity with good specificity measures returned by candidate pharmacophore hypotheses across each conformation type and recognized crucial pharmacophore features that enable caspase-3 binding. Pharmacophore modeling based on flexible conformational scheme indicated that the crystal structure 3KJF (AAAADH) is the best receptor structure to perform receptor-based pharmacophore screening of caspase-3 inhibitors. When multiple crystal structures were included, the hypothesis (HAAA) is more generalized. Superimposition of multiple co-crystal ligands from various caspase-3 PDB entries in crystallographic binding mode revealed similar hypothesis (HAAA). Further, FlexX-guided dock conformations of validation dataset showed that the crystal structure 1RE1 is the best-suited for dock-based pharmacophore models. Database screening using these pharmacophore hypotheses identified N'-[6-(benzimidazol-1-yl)-5-nitro-pyrimidin-4-yl]-4 methylbenzenesulfonohydrazide and 2-nitro-N'-[5-nitro-6-[N'-(p-tolylsulfonyl)hydrazino]pyrimidin-4- yl]benzohydrazide as the probable caspase-3 inhibitors. Conclusion: N'-[6-(benzimidazol-1-yl)-5-nitro-pyrimidin-4-yl]-4 methylbenzenesulfonohydrazide and 2-nitro-N'-[5-nitro-6-[N'-(p-tolylsulfonyl)hydrazino]pyrimidin-4-yl]benzohydrazide may be tested for caspase-3 inhibition. We believe that potential caspase-3 inhibitors can be recognized efficiently by adapting multi-pharmacophore models in database screening.
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Yamakawa, Yuko, Kazuharu Furutani, Atsushi Inanobe, Yuko Ohno, and Yoshihisa Kurachi. "Pharmacophore modeling for hERG channel facilitation." Biochemical and Biophysical Research Communications 418, no. 1 (February 2012): 161–66. http://dx.doi.org/10.1016/j.bbrc.2011.12.153.

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Noonan, Theresa, Katrin Denzinger, Valerij Talagayev, Yu Chen, Kristina Puls, Clemens Alexander Wolf, Sijie Liu, Trung Ngoc Nguyen, and Gerhard Wolber. "Mind the Gap—Deciphering GPCR Pharmacology Using 3D Pharmacophores and Artificial Intelligence." Pharmaceuticals 15, no. 11 (October 22, 2022): 1304. http://dx.doi.org/10.3390/ph15111304.

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G protein-coupled receptors (GPCRs) are amongst the most pharmaceutically relevant and well-studied protein targets, yet unanswered questions in the field leave significant gaps in our understanding of their nuanced structure and function. Three-dimensional pharmacophore models are powerful computational tools in in silico drug discovery, presenting myriad opportunities for the integration of GPCR structural biology and cheminformatics. This review highlights success stories in the application of 3D pharmacophore modeling to de novo drug design, the discovery of biased and allosteric ligands, scaffold hopping, QSAR analysis, hit-to-lead optimization, GPCR de-orphanization, mechanistic understanding of GPCR pharmacology and the elucidation of ligand–receptor interactions. Furthermore, advances in the incorporation of dynamics and machine learning are highlighted. The review will analyze challenges in the field of GPCR drug discovery, detailing how 3D pharmacophore modeling can be used to address them. Finally, we will present opportunities afforded by 3D pharmacophore modeling in the advancement of our understanding and targeting of GPCRs.
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Chidambaram, Kumarappan. "Identification of BACE-1 Inhibitors against Alzheimer’s Disease through E-Pharmacophore-Based Virtual Screening and Molecular Dynamics Simulation Studies: An Insilco Approach." Life 13, no. 4 (April 5, 2023): 952. http://dx.doi.org/10.3390/life13040952.

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Alzheimer is a severe memory and cognitive impairment neurodegenerative disease that is the most common cause of dementia worldwide and characterized by the pathological accumulation of tau protein and amyloid-beta peptides. In this study, we have developed E-pharmacophore modeling to screen the eMolecules database with the help of a reported co-crystal structure bound with Beta-Site Amyloid Precursor Protein Cleaving Enzyme 1 (BACE-1). Flumemetamol, florbetaben, and florbetapir are currently approved drugs for use in the clinical diagnosis of Alzheimer’s disease. Despite the benefits of commercially approved drugs, there is still a need for novel diagnostic agents with enhanced physicochemical and pharmacokinetic properties compared to those currently used in clinical practice and research. In the E-pharmacophore modeling results, it is revealed that two aromatic rings (R19, R20), one donor (D12), and one acceptor (A8) are obtained, and also that similar pharmacophoric features of compounds are identified from pharmacophore-based virtual screening. The identified screened hits were filtered for further analyses using structure-based virtual screening and MM/GBSA. From the analyses, top hits such as ZINC39592220 and en1003sfl.46293 are selected based on their top docking scores (−8.182 and −7.184 Kcal/mol, respectively) and binding free energy (−58.803 and −56.951 Kcal/mol, respectively). Furthermore, a molecular dynamics simulation and MMPBSA study were performed, which revealed admirable stability and good binding free energy throughout the simulation period. Moreover, Qikprop results revealed that the selected, screened hits have good drug-likeness and pharmacokinetic properties. The screened hits ZINC39592220 and en1003sfl.46293 could be used to develop drug molecules against Alzheimer’s disease.
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Sugumar, Shobana. "VIRTUAL SCREENING, PHARMACOPHORE MODELING, AND QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP STUDIES ON HISTAMINE 4 RECEPTOR." Asian Journal of Pharmaceutical and Clinical Research 10, no. 12 (December 1, 2017): 150. http://dx.doi.org/10.22159/ajpcr.2017.v10i12.19991.

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Objective: To find out novel inhibitors for histamine 4 receptor (H4R), the target for various allergic and inflammatory pathophysiological conditions.Methods: Homology modeling of H4R was performed using easy modeler and validated using structure analysis and verification server, and with the modeled structure, virtual screening, pharmacophore modeling, and quantitative structure activity relationship (QSAR) studies were performed using the Schrodinger 9.3 software.Results: Among all the synthetic and natural ligands, hesperidin, vitexin, and diosmin were found to have the highest dock score, and with that, a five-point pharmacophore model was developed consisting of two hydrogen bond acceptor and three ring atoms, and the pharmacophore hypothesis yielded a statistically significant three-dimensional QSAR (3D-QSAR) model with a correlation coefficient of r2=0.8962 as well as good predictive power.Conclusion: The pharmacophore-based 3D-QSAR model generated from natural antihistamines can provide intricate structural knowledge about a new class of anti-allergic and anti-inflammatory drug research.
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Jb, Sheema, and Waheeta Hopper. "ENERGY-BASED PHARMACOPHORE MODELING, VIRTUAL SCREENING, AND MOLECULAR DYNAMICS TO IDENTIFY POTENTIAL INHIBITORS FOR GLYCOGEN SYNTHASE KINASE 3 BETA." Asian Journal of Pharmaceutical and Clinical Research 11, no. 2 (February 1, 2018): 181. http://dx.doi.org/10.22159/ajpcr.2018.v11i2.22962.

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Objective: Glycogen synthase kinase 3 beta (GSK3β) is one of the main targets for wound healing activity. Our objective is to identify novel inhibitors for GSK3β using in silico approach.Methods: Grid-based molecular docking, energy-based pharmacophore (e-pharmacophore) modeling, and molecular dynamics (MD) studies were performed for phytocompounds with GSK3β and compared with standard drugs using Schrodinger software.Results: The glide scores and the molecular interactions of the phytocompounds were well comparable to the standard drugs. The MD was performed for the target bound to the best scoring ligand, entagenic acid. The pharmacophore features of this docked complex were modeled as e-pharmacophore. The constructed e-pharmacophore model was screened against phytocompounds retrieved from literature to identify the ligands with similar pharmacophore features.Conclusion: The glide scores of fukinolic acid, cimicifugic acid, and linarin were −10.99, −8.28, and −7.25 kcal/mol, respectively. The further 50 nanoseconds MD study determined the stability of GSK3β-linarin complex. Nitrofurazone and sulfathiazole drugs can lead to systemic side effects. Hence, it is concluded that linarin could be a potent wound healing compound against GSK3β.
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Zou, Jun, Huan-Zhang Xie, Sheng-Yong Yang, Jin-Juan Chen, Ji-Xia Ren, and Yu-Quan Wei. "Towards more accurate pharmacophore modeling: Multicomplex-based comprehensive pharmacophore map and most-frequent-feature pharmacophore model of CDK2." Journal of Molecular Graphics and Modelling 27, no. 4 (November 2008): 430–38. http://dx.doi.org/10.1016/j.jmgm.2008.07.004.

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Dong, Xialan, Jerry O. Ebalunode, Sheng-Yong Yang, and Weifan Zheng. "Receptor-Based Pharmacophore and Pharmacophore Key Descriptors for Virtual Screening and QSAR Modeling." Current Computer Aided-Drug Design 7, no. 3 (September 1, 2011): 181–89. http://dx.doi.org/10.2174/157340911796504332.

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Todkar, S. S., and A. H. Hoshmani. "DESIGN OF POTENTIAL CYCLOOXYGENASE INHIBITORS USING PHARMACOPHORE OPTIMIZATION BY MOLECULAR MODELING STUDIES." INDIAN DRUGS 52, no. 12 (December 28, 2015): 16–22. http://dx.doi.org/10.53879/id.52.12.10154.

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Recently discovery of relation between cyclooxygenase–2 (COX–2) inhibition and prevention of growth of cansar cells is a major area for research in medicinal chemistry, as it is free from side effects which are genetically shown by developed anticancer agents. In an attempt to develop potent and nontoxic COX–2 inhibitors, we have optimized the 1,5- diaryl pyrazole pharmacophore by using molecular modeling studies. In this paper we present results of 2D and 3D QSAR studies of a series of 22 molecules containing 1,5- diaryl pyrazole pharmacophore as selective COX–2 inhibitors. The 3D QSAR studies were performed using two different methods, stepwise variable selection k–nearest neighbor molecular field analysis (SW kNN–MFA) and simulated annealing k–nearest neighbor molecular field analysis (SA kNN–MFA) methods. The 2D QSAR studies were performed using multiple regressions. 3D QSAR studies produced reasonably good predictive models with high cross–validated r2cv value of 0.732 and 0.783 and predicted r2 value of 0.882 and 0.794 values using the models SW kNN–MFA and SA kNN–MFA method, respectively, whereas the r2 & predicted r2 value in 2D QSAR studies was found to be 0.84914 & 0.9157, respectively. the 2D QSAR studies indicated contribution of different physicochemical descriptors and the result of 3D QSAR studies indicated the exact steric and electronic requirement in the ranges at various positions in the 1,5- diaryl pyrazole pharmacophore. The pharmacophore requirement for selective COX–2 inhibition was optimized and requirement at various positions around 1, 5- diaryl pyrazole pharmacophore were defined.
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Mansi, Iman, Mahmoud A. Al-Sha'er, Nizar Mhaidat, and Mutasem Taha. "Ligand Based Pharmacophore Modeling Followed by Biological Screening Lead to Discovery of Novel PDK1 Inhibitors as Anticancer Agents." Anti-Cancer Agents in Medicinal Chemistry 20, no. 4 (May 15, 2020): 476–85. http://dx.doi.org/10.2174/1871520620666191224110600.

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Background: Phosphoinositide-Dependent Kinase-1 (PDK1) is a serine/threonine kinase, which belongs to AGC kinase family required by cancer cells. Methods: harmacophoric space of 86 PDK1 inhibitors using six diverse sets of inhibitors was explored to identify high-quality pharmacophores. The best combination of pharmacophoric models and physicochemical descriptors was selected by genetic algorithm-based QSAR analysis that can elucidate the variation of bioactivity within the training inhibitors. Two successful orthogonal pharmacophores emerged in the optimum QSAR equation (r2 69 = 0.90, r2 LOO= 0.86, F= 51.92, r2 PRESS against 17 test inhibitors = 0.79). Receiver Operating Characteristic (ROC) curve analyses were used to estimate the QSAR-selected pharmacophores. Results: 5 out of 11 compounds tested had shown potential intracellular PDK1 inhibition with the highest inhibition percent for compounds 92 and 93 as follows; 90 and 92% PDK1 inhibition, respectively. Conclusion: PDK1 inhibitors are potential anticancer agents that can be discovered by combination method of ligand based design with QSAR and ROC analysis.
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Vadlakonda, Rajashekar, Sreenivas Enaganti, and Raghunandan Nerella. "INSILICO DISCOVERY OF HUMAN AURORA B KINASE INHIBITORS BY MOLECULAR DOCKING, PHARMACOPHORE VALIDATION AND ADMET STUDIES." Asian Journal of Pharmaceutical and Clinical Research 10, no. 2 (February 1, 2017): 165. http://dx.doi.org/10.22159/ajpcr.2017.v10i2.14974.

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Objectives: To predict the anticancer potentiality of some newly designed azaindole derivatives gainst human Aurora B kinase and to identify the critical features important for their activity.Methods: Initially, the derivatives of azaindoles, (Z)-2-(oxo-1 H-pyrrolo [2,3-b] pyridine-3 (2H)-ylidene)-N-(p-substituted) hydrazine carbothioamide (scaffold A), (E)-3-((E)-substituted benzylidene hydrazono)-1H-pyrrolo[2,3-b]pyridine-2(3H)-one (scaffold B), and 1-(2-substituted acetyl)-1H- pyrrolo [2,3-b]pyridine-2,3-dione are synthesized and sketched using ACD/ChemSketch (12.0). With the 3D converted compounds, docking into the active site of the retrieved protein Aurora B kinase is carried out using LibDock module of discovery studio (DS). Further absorption, distribution, metabolism, excretion and toxicity (ADMET) properties, ligand, and structure-based pharmacophore modeling are applied using DS.Results: Through docking and pharmacophore studies, it is revealed that compound C13 (N-{(Z)-2-[4-(dimethylamino)phenyl]ethenyl}-1H- pyrrolo[2,3-b]pyridine-3-carboxamide) shows the highest binding affinity and good pharmacophoric features with acceptable fit values of both ligand and structure-based pharmacophore models. Furthermore, the calculated ADMET properties are reliable.Conclusion: These studies suggest that the compound C13 (N-{(Z)-2-[4-(dimethylamino)phenyl]ethenyl}-1H-pyrrolo[2,3-b]pyridine-3-carboxamide)may act as a potent target in the anticancer therapy.Keywords: Aneuploidy, Aurora B kinase, Azaindole, Cancer, Cell cycle, Genome stability.
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AbdElmoniem, Nihal, Marwa H. Abdallah, Rua M. Mukhtar, Fatima Moutasim, Ahmed Rafie Ahmed, Alaa Edris, Walaa Ibraheem, et al. "Identification of Novel Natural Dual HDAC and Hsp90 Inhibitors for Metastatic TNBC Using e-Pharmacophore Modeling, Molecular Docking, and Molecular Dynamics Studies." Molecules 28, no. 4 (February 13, 2023): 1771. http://dx.doi.org/10.3390/molecules28041771.

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Breast cancer (BC) is one of the main types of cancer that endangers women’s lives. The characteristics of triple-negative breast cancer (TNBC) include a high rate of recurrence and the capacity for metastasis; therefore, new therapies are urgently needed to combat TNBC. Dual targeting HDAC6 and Hsp90 has shown good synergistic effects in treating metastatic TNBC. The goal of this study was to find potential HDAC6 and Hsp90 dual inhibitors. Therefore, several in silico approaches have been used. An e-pharmacophore model generation based on the HDAC6-ligand complex and subsequently a pharmacophore-based virtual screening on 270,450 natural compounds from the ZINC were performed, which resulted in 12,663 compounds that corresponded to the obtained pharmacophoric hypothesis. These compounds were docked into HDAC6 and Hsp90. This resulted in the identification of three compounds with good docking scores and favorable free binding energy against the two targets. The top three compounds, namely ZINC000096116556, ZINC000020761262, and ZINC000217668954, were further subjected to ADME prediction and molecular dynamic simulations, which showed promising results in terms of pharmacokinetic properties and stability. As a result, these three compounds can be considered potential HDAC6 and Hsp90 dual inhibitors and are recommended for experimental evaluation.
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Derz, Wiebke, Melita Fleischmann, and Paul W. Elsinghorst. "Guiding Molecularly Imprinted Polymer Design by Pharmacophore Modeling." Molecules 26, no. 16 (August 23, 2021): 5101. http://dx.doi.org/10.3390/molecules26165101.

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Molecularly imprinted polymers (MIP) combine the selectivity of immunoaffinity chromatography with the robustness of common solid-phase extraction in what is referred to as molecularly imprinted solid-phase extraction (MISPE). This contribution shows how MIP design may be guided by pharmacophore modeling for the example of citrinin, which is an emerging mycotoxin from cereals. The obtained pharmacophore model allowed searching public databases for a set of citrinin-mimicking molecular surrogates. Imprinted and non-imprinted polymers were subsequently obtained through bulk and core-shell polymerization in the presence of these surrogates. Evaluation of their binding ability for citrinin and structurally related ochratoxin A revealed a promising MIP derived from rhodizonic acid. A protocol for MISPE of citrinin from cereals was subsequently developed and compared to immunoaffinity chromatography with respect to clean-up efficiency and recovery.
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Pu, Yinglan, Shuqun Zhang, Zhe Chang, Yunqin Zhang, Dong Wang, Li Zhang, Yan Li, and Zhili Zuo. "Discovery of new dual binding TNKS inhibitors of Wnt signaling inhibition by pharmacophore modeling, molecular docking and bioassay." Molecular BioSystems 13, no. 2 (2017): 363–70. http://dx.doi.org/10.1039/c6mb00712k.

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30

Peng, Xiu Xiu, Kai Rui Feng, and Yu Jie Ren. "Molecular modeling studies of quinazolinone derivatives as novel PI3Kδ selective inhibitors." RSC Advances 7, no. 89 (2017): 56344–58. http://dx.doi.org/10.1039/c7ra10870b.

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Zhang, Chao, Junjie Xiang, Qian Xie, Jing Zhao, Hong Zhang, Erfang Huang, Pangchui Shaw, Xiaoping Liu, and Chun Hu. "Identification of Influenza PAN Endonuclease Inhibitors via 3D-QSAR Modeling and Docking-Based Virtual Screening." Molecules 26, no. 23 (November 25, 2021): 7129. http://dx.doi.org/10.3390/molecules26237129.

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Structural and biochemical studies elucidate that PAN may contribute to the host protein shutdown observed during influenza A infection. Thus, inhibition of the endonuclease activity of viral RdRP is an attractive approach for novel antiviral therapy. In order to envisage structurally diverse novel compounds with better efficacy as PAN endonuclease inhibitors, a ligand-based-pharmacophore model was developed using 3D-QSAR pharmacophore generation (HypoGen algorithm) methodology in Discovery Studio. As the training set, 25 compounds were taken to generate a significant pharmacophore model. The selected pharmacophore Hypo1 was further validated by 12 compounds in the test set and was used as a query model for further screening of 1916 compounds containing 71 HIV-1 integrase inhibitors, 37 antibacterial inhibitors, 131 antiviral inhibitors and other 1677 approved drugs by the FDA. Then, six compounds (Hit01–Hit06) with estimated activity values less than 10 μM were subjected to ADMET study and toxicity assessment. Only one potential inhibitory ‘hit’ molecule (Hit01, raltegravir’s derivative) was further scrutinized by molecular docking analysis on the active site of PAN endonuclease (PDB ID: 6E6W). Hit01 was utilized for designing novel potential PAN endonuclease inhibitors through lead optimization, and then compounds were screened by pharmacophore Hypo1 and docking studies. Six raltegravir’s derivatives with significant estimated activity values and docking scores were obtained. Further, these results certainly do not confirm or indicate the seven compounds (Hit01, Hit07, Hit08, Hit09, Hit10, Hit11 and Hit12) have antiviral activity, and extensive wet-laboratory experimentation is needed to transmute these compounds into clinical drugs.
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32

Santos, Kelton L. B. dos, Jorddy N. Cruz, Luciane B. Silva, Ryan S. Ramos, Moysés F. A. Neto, Cleison C. Lobato, Sirlene S. B. Ota, et al. "Identification of Novel Chemical Entities for Adenosine Receptor Type 2A Using Molecular Modeling Approaches." Molecules 25, no. 5 (March 10, 2020): 1245. http://dx.doi.org/10.3390/molecules25051245.

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Adenosine Receptor Type 2A (A2AAR) plays a role in important processes, such as anti-inflammatory ones. In this way, the present work aimed to search for compounds by pharmacophore-based virtual screening. The pharmacokinetic/toxicological profiles of the compounds, as well as a robust QSAR, predicted the binding modes via molecular docking. Finally, we used molecular dynamics to investigate the stability of interactions from ligand-A2AAR. For the search for A2AAR agonists, the UK-432097 and a set of 20 compounds available in the BindingDB database were studied. These compounds were used to generate pharmacophore models. Molecular properties were used for construction of the QSAR model by multiple linear regression for the prediction of biological activity. The best pharmacophore model was used by searching for commercial compounds in databases and the resulting compounds from the pharmacophore-based virtual screening were applied to the QSAR. Two compounds had promising activity due to their satisfactory pharmacokinetic/toxicological profiles and predictions via QSAR (Diverset 10002403 pEC50 = 7.54407; ZINC04257548 pEC50 = 7.38310). Moreover, they had satisfactory docking and molecular dynamics results compared to those obtained for Regadenoson (Lexiscan®), used as the positive control. These compounds can be used in biological assays (in vitro and in vivo) in order to confirm the potential activity agonist to A2AAR.
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Agrawal, Nikhil, Balakumar Chandrasekaran, and Amal Al-Aboudi. "Recent Advances in the In-silico Structure-based and Ligand-based Approaches for the Design and Discovery of Agonists and Antagonists of A2A Adenosine Receptor." Current Pharmaceutical Design 25, no. 7 (June 17, 2019): 774–82. http://dx.doi.org/10.2174/1381612825666190306162006.

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A2A receptor belongs to the family of GPCRs, which are the most abundant membrane protein family. Studies in the last few decades have shown the therapeutic applications of A2A receptor in various diseases. In the present mini-review, we have discussed the recent progress in the in-silico studies of the A2A receptor. Herein, we described the different structures of A2A receptor, the discovery of new agonists and antagonists using virtualscreening/ docking, pharmacophore modeling, and QSAR based pharmacophore modeling. We have also discussed various molecular dynamics (MD) simulations studies of A2A receptor in complex with ligands.
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34

Damale, Manoj G., Shahebaaz K. Pathan, Rajesh B. Patil, and Jaiprakash N. Sangshetti. "Pharmacoinformatics approaches to identify potential hits against tetraacyldisaccharide 4′-kinase (LpxK) of Pseudomonas aeruginosa." RSC Advances 10, no. 54 (2020): 32856–74. http://dx.doi.org/10.1039/d0ra06675c.

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35

Brogi, Simone, Maria Kladi, Constantinos Vagias, Panagiota Papazafiri, Vassilios Roussis, and Andrea Tafi. "Pharmacophore Modeling for Qualitative Prediction of Antiestrogenic Activity." Journal of Chemical Information and Modeling 49, no. 11 (October 30, 2009): 2489–97. http://dx.doi.org/10.1021/ci900254b.

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36

Xu, Zhejun, Feixiong Cheng, Chenxiao Da, Guixia Liu, and Yun Tang. "Pharmacophore modeling of human adenosine receptor A2A antagonists." Journal of Molecular Modeling 16, no. 12 (March 12, 2010): 1867–76. http://dx.doi.org/10.1007/s00894-010-0690-z.

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37

Ghose, Arup K., Vellarkad N. Viswanadhan, and John J. Wendoloski. "THE FUNDAMENTALS OF PHARMACOPHORE MODELING IN COMBINATORIAL CHEMISTRY*." Journal of Receptors and Signal Transduction 21, no. 4 (January 2001): 357–75. http://dx.doi.org/10.1081/rrs-100107923.

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38

Markt, Patrick, Daniela Schuster, Johannes Kirchmair, Christian Laggner, and Thierry Langer. "Pharmacophore modeling and parallel screening for PPAR ligands." Journal of Computer-Aided Molecular Design 21, no. 10-11 (October 2007): 575–90. http://dx.doi.org/10.1007/s10822-007-9140-0.

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39

Bharatham, Kavitha, Nagakumar Bharatham, and Keun Woo Lee. "Pharmacophore modeling for protein tyrosine phosphatase 1B inhibitors." Archives of Pharmacal Research 30, no. 5 (May 2007): 533–42. http://dx.doi.org/10.1007/bf02977644.

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40

Faden, Alan I, Vilen A Movsesyan, Xueliang Fang, and Shaomeng Wang. "Identification of Novel Neuroprotective Agents Using Pharmacophore Modeling." Chemistry & Biodiversity 2, no. 11 (November 2005): 1564–70. http://dx.doi.org/10.1002/cbdv.200590127.

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41

Kaushik, Pawan, Sukhbir Lal Khokra, A. C. Rana, and Dhirender Kaushik. "Pharmacophore Modeling and Molecular Docking Studies on Pinus roxburghii as a Target for Diabetes Mellitus." Advances in Bioinformatics 2014 (July 10, 2014): 1–8. http://dx.doi.org/10.1155/2014/903246.

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The present study attempts to establish a relationship between ethnopharmacological claims and bioactive constituents present in Pinus roxburghii against all possible targets for diabetes through molecular docking and to develop a pharmacophore model for the active target. The process of molecular docking involves study of different bonding modes of one ligand with active cavities of target receptors protein tyrosine phosphatase 1-beta (PTP-1β), dipeptidyl peptidase-IV (DPP-IV), aldose reductase (AR), and insulin receptor (IR) with help of docking software Molegro virtual docker (MVD). From the results of docking score values on different receptors for antidiabetic activity, it is observed that constituents, namely, secoisoresinol, pinoresinol, and cedeodarin, showed the best docking results on almost all the receptors, while the most significant results were observed on AR. Then, LigandScout was applied to develop a pharmacophore model for active target. LigandScout revealed that 2 hydrogen bond donors pointing towards Tyr 48 and His 110 are a major requirement of the pharmacophore generated. In our molecular docking studies, the active constituent, secoisoresinol, has also shown hydrogen bonding with His 110 residue which is a part of the pharmacophore. The docking results have given better insights into the development of better aldose reductase inhibitor so as to treat diabetes related secondary complications.
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42

Singh, Karanveer, Manish Sinha, Shruti Kuletha, Baljeet Kaur, Amandeep Kaur, Dinesh K. Tripathi, Kishore K. Srivastava, Vanangamudi Murugesan, Rajala Srikala, and Amrendra K. Chaudhary. "Synthesis, Antitubercular Activity, Molecular Modeling and Docking Studies of Novel Thiazolidin-4-One Linked Dinitrobenzamide Derivatives." Current Bioactive Compounds 16, no. 1 (February 20, 2020): 64–71. http://dx.doi.org/10.2174/1573407214666180720150009.

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Background: Tuberculosis is a catastrophe sprawled across the world. The World Health Organization Global Tuberculosis Report 2017 inferred that there were an estimated 10.4 million people suffered from tuberculosis including 490000 Multidrug-Resistant TB (MDR-TB) cases. Several new lead molecules like dinitrobenzamide derivatives were found to be highly active against multidrugresistant strains of M. tuberculosis. To further explore the pharmacophoric space around the dinitobenzamide moiety, a series of compounds have been synthesized by linking it with the thiazolidin- 4-one. The presented work is an effort to study the biological effect of thiazolidin-4-one scaffold on dinitrobenzamide derivatives as antitubercular agents. A molecular modeling study was also performed on the synthesized molecules to reveal the requirements for further lead optimization. Methods: The thiazolidin-4-one linked 3,5-dinitrobenzamide derivatives have been synthesized by onepot three-component condensation reaction of an amine, substituted aldehydes and thioglycolic acid in presence of N, N'-Dicyclohexylcarbodiimide (DCC). These compounds were evaluated against Mycobacterium tuberculosis H37Ra. A pharmacophore modeling approach has been used in order to explore the collection of possible pharmacophore queries of thiazolidin-4-one linked 3, 5-dinitrobenzamide derivatives against M. tuberculosis. The synthesized compounds were docked on to the M. tuberculosis DprE1 enzyme to identify the structural features requirement of these analogs against this potential target of M. tuberculosis. Results: The synthesized compounds showed the antitubercular activity in the range of 6.25-50 μg/ml. The pharmacophore modeling suggests that the presence of aromatic moiety, thiazolidin-4-one ring and one of the nitro groups are significant for inhibiting the enzymatic activity. While docking studies showed that hydrophobic and hydrogen bond interactions of the aromatic moiety and nitro group crucial to inactivate the DprE1 enzyme. Conclusion: The study showed that the linking of thiazolidin-4-one with dinitrobenzamide leads to compounds active against M. tuberculosis. These findings also suggested that further lead optimization would be carried out by focusing on the aromatic system along with electron-rich substituents placed on the thiazolidin-4-one for making better hydrophobic and hydrogen bond interactions with the DprE1 target.
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43

Mathpal, Deepti, Tahani M. Almeleebia, Kholoud M. Alshahrani, Mohammad Y. Alshahrani, Irfan Ahmad, Mohammed Asiri, Mehnaz Kamal, et al. "Identification of 3-((1-(Benzyl(2-hydroxy-2-phenylethyl)amino)-1-oxo-3-phenylpropan-2-yl)carbamoyl)pyrazine-2-carboxylic Acid as a Potential Inhibitor of Non-Nucleosidase Reverse Transcriptase Inhibitors through InSilico Ligand- and Structure-Based Approaches." Molecules 26, no. 17 (August 30, 2021): 5262. http://dx.doi.org/10.3390/molecules26175262.

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Non-nucleosidase reverse transcriptase inhibitors (NNRTIs) are highly promising agents for use in highly effective antiretroviral therapy. We implemented a rational approach for the identification of promising NNRTIs based on the validated ligand- and structure-based approaches. In view of our state-of-the-art techniques in drug design and discovery utilizing multiple modeling approaches, we report here, for the first time, quantitative pharmacophore modeling (HypoGen), docking, and in-house database screening approaches in the identification of potential NNRTIs. The validated pharmacophore model with three hydrophobic groups, one aromatic ring group, and a hydrogen-bond acceptor explains the interactions at the active site by the inhibitors. The model was implemented in pharmacophore-based virtual screening (in-house and commercially available databases) and molecular docking for prioritizing the potential compounds as NNRTI. The identified leads are in good corroboration with binding affinities and interactions as compared to standard ligands. The model can be utilized for designing and identifying the potential leads in the area of NNRTIs.
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Kandakatla, Naresh, and Geetha Ramakrishnan. "Ligand Based Pharmacophore Modeling and Virtual Screening Studies to Design Novel HDAC2 Inhibitors." Advances in Bioinformatics 2014 (November 26, 2014): 1–11. http://dx.doi.org/10.1155/2014/812148.

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Histone deacetylases 2 (HDAC2), Class I histone deacetylase (HDAC) family, emerged as an important therapeutic target for the treatment of various cancers. A total of 48 inhibitors of two different chemotypes were used to generate pharmacophore model using 3D QSAR pharmacophore generation (HypoGen algorithm) module in Discovery Studio. The best HypoGen model consists of four pharmacophore features namely, one hydrogen bond acceptor (HBA), and one hydrogen donor (HBD), one hydrophobic (HYP) and one aromatic centres, (RA). This model was validated against 20 test set compounds and this model was utilized as a 3D query for virtual screening to validate against NCI and Maybridge database and the hits further screened by Lipinski’s rule of 5, and a total of 382 hit compounds from NCI and 243 hit compounds from Maybridge were found and were subjected to molecular docking in the active site of HDAC2 (PDB: 3MAX). Finally eight hit compounds, NSC108392, NSC127064, NSC110782, and NSC748337 from NCI database and MFCD01935795, MFCD00830779, MFCD00661790, and MFCD00124221 from Maybridge database, were considered as novel potential HDAC2 inhibitors.
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45

Munir, Anum, Shaukat I. Malik, and Khalid A. Malik. "De-Novo Ligand Design against Mutated Huntington Gene by Ligand-based Pharmacophore Modeling Approach." Current Computer-Aided Drug Design 16, no. 2 (March 25, 2020): 134–44. http://dx.doi.org/10.2174/1573409915666181207104437.

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Background: Huntington's disease is characterized by three side effects, including motor disturbances, psychiatric elements, and intellectual weakness. The onset for HD has nonlinear converse associations with the number of repeat sequences of the polyglutamine mutations, so that younger patients have a tendency for longer repeats length. This HD variation is because of the development of a polyglutamine (CAG) repeats in the exon 1 of the Huntingtin protein. Methods: In the present study, a few derivatives utilized as a part of the treatment of HD, are used to create the pharmacophore model and based on the features of the pharmacophore model; an attempt is made to design the de-novo drug for the HD protein. HD protein structure was built and docked with the novel ligand, based on shared feature pharmacophore model, through a ligand-based pharmacophore modeling approach. Results: The novel ligand contains 1 HBAs, 2 HBDs, and 2 aromatic rings. It fulfills all the properties of certain drug-likeness rules, non-toxic in nature. In the docked complex, the common interactive amino acids identified are SER 1035, ALA 1062, MET 1068, LEU 1031, and THR 1036, which confirmed the validity and stability of a ligand molecule to be used as a drug in the treatment of Huntington’s disease. Conclusion: A novel ligand can be used in clinical trials as a drug molecule against the mutations of HD gene and in laboratory procedures for efficacy analysis.
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46

Kaur, Paramjit, Vikas Sharma, and Vipin Kumar. "Pharmacophore Modelling and 3D-QSAR Studies on -Phenylpyrazinones as Corticotropin-Releasing Factor 1 Receptor Antagonists." International Journal of Medicinal Chemistry 2012 (May 31, 2012): 1–13. http://dx.doi.org/10.1155/2012/452325.

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Pharmacophore modelling-based virtual screening of compound is a ligand-based approach and is useful when the 3D structure of target is not available but a few known active compounds are known. Pharmacophore mapping studies were undertaken for a set of 50 N3-phenylpyrazinones possessing Corticotropin-releasing Factor 1 (CRF 1) antagonistic activity. Six point pharmacophores with two hydrogen bond acceptors, one hydrogen bond donor, two hydrophobic regions, and one aromatic ring as pharmacophoric features were developed. Amongst them the pharmacophore hypothesis AADHHR.47 yielded a statistically significant 3D-QSAR model with 0.803 as value and was considered to be the best pharmacophore hypothesis. The developed pharmacophore model was externally validated by predicting the activity of test set molecules. The squared predictive correlation coefficient of 0.91 was observed between experimental and predicted activity values of test set molecules. The geometry and features of pharmacophore were expected to be useful for the design of selective CRF 1 receptor antagonists.
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47

Crisan, Luminita, Daniela Varga, and Liliana Pacureanu. "Pharmacophore Modeling and Docking Study of Pyrazolylaminoquinazoline Derivatives as Highly Potent Fibroblast Growth Factor Receptor Inhibitors2 (FGFR2)." Revista de Chimie 70, no. 3 (April 15, 2019): 790–96. http://dx.doi.org/10.37358/rc.19.3.7008.

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In this study pharmacophore modeling and molecular docking investigations have been performed on pyrazolylaminoquinazoline derivatives, highly potent fibroblast growth factor receptor2 (FGFR2) inhibitors. The best pharmacophore hypotheses displaying five features (ADHRR.2051 and AADHR.798) were generated using a set of 28 compounds. The associated 3D atom-based quantitative structure � activity relationships (QSAR) models were statistically robust showing high correlation coefficients (R-squared = 0.981 / 0.982), and cross validation coefficients (Q-squared = 0.645 / 0.671). The R-Pearson values for the test set of 0.805 / 0.820 indicate that the models are robust and exhibit good predictive power. The interactions of pyrazolylaminoquinazoline with FGFR2 binding site revealed two hydrogen bonds with Ala567. The obtained pharmacophore, 3D atom-based QSAR models and binding features resulted from docking studies can help medicinal chemists to design new pyrazolylaminoquinazoline inhibitors with improved potency.
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48

Saeed, Mohd, Amir Saeed, Md Jahoor Alam, and Mousa Alreshidi. "Receptor-Based Pharmacophore Modeling in the Search for Natural Products for COVID-19 Mpro." Molecules 26, no. 6 (March 11, 2021): 1549. http://dx.doi.org/10.3390/molecules26061549.

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Considering the urgency of the COVID-19 pandemic, we developed a receptor-based pharmacophore model for identifying FDA-approved drugs and hits from natural products. The COVID-19 main protease (Mpro) was selected for the development of the pharmacophore model. The model consisted of a hydrogen bond acceptor, donor, and hydrophobic features. These features demonstrated good corroboration with a previously reported model that was used to validate the present model, showing an RMSD value of 0.32. The virtual screening was carried out using the ZINC database. A set of 208,000 hits was extracted and filtered using the ligand pharmacophore mapping, applying the lead-like properties. Lipinski’s filter and the fit value filter were used to minimize hits to the top 2000. Simultaneous docking was carried out for 200 hits for natural drugs belonging to the FDA-approved drug database. The top 28 hits from these experiments, with promising predicted pharmacodynamic and pharmacokinetic properties, are reported here. To optimize these hits as Mpro inhibitors and potential treatment options for COVID-19, bench work investigations are needed.
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Agrawal, Neetu. "Pharmacophore modeling and 3D-QSAR studies of 2,4-disubstituted pyrimidine derivatives as Janus kinase 3 inhibitors." Journal of Theoretical and Computational Chemistry 19, no. 01 (February 2020): 2050001. http://dx.doi.org/10.1142/s0219633620500017.

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A robust pharmacophore model was developed and the structure-activity relationship was analyzed using 71 pyrimidine derivatives reported for covalent Janus Kinase 3 (JAK3) inhibition. Pharmacophore modeling developed a five featured pharmacophore: one H-bond acceptor, two H-bond donors, one hydrophobic, and one aromatic ring features. The atom-based three-dimensional QSAR models with statistical significance were generated using the training set of 52 compounds. The excellent predictive correlation coefficients were obtained for 3D models determined using a test set of 19 molecules. The generated QSAR model implies that the hydrophobic character is important for the JAK3 inhibitory activity of these compounds. Additionally, electron-withdrawing and hydrogen bond donor groups at specific positions positively contribute to the JAK3 inhibition potency. These results provided essential three-dimensional structural requirements and the crucial binding features of 2,4-disubstituted pyrimidine derivatives, which may direct for the design and discovery of novel potent JAK3 inhibitors.
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50

Nncube, Nomagugu B., Pritika Ramharack, and Mahmoud E. S. Soliman. "Using bioinformatics tools for the discovery of Dengue RNA-dependent RNA polymerase inhibitors." PeerJ 6 (September 25, 2018): e5068. http://dx.doi.org/10.7717/peerj.5068.

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BackgroundDengue fever has rapidly manifested into a serious global health concern. The emergence of various viral serotypes has prompted the urgent need for innovative drug design techniques. Of the viral non-structural enzymes, the NS5 RNA-dependent RNA polymerase has been established as a promising target due to its lack of an enzymatic counterpart in mammalian cells and its conserved structure amongst all serotypes. The onus is now on scientists to probe further into understanding this enzyme and its mechanism of action. The field of bioinformatics has evolved greatly over recent decades, with updated drug design tools now being publically available.MethodsIn this study, bioinformatics tools were used to provide a comprehensive sequence and structural analysis of the two most prominent serotypes of Dengue RNA-dependent RNA polymerase. A list of popularflavivirusinhibitors were also chosen to dock to the active site of the enzyme. The best docked compound was then used as a template to generate a pharmacophore model that may assist in the design of target-specific Dengue virus inhibitors.ResultsComparative sequence alignment exhibited similarity between all three domains of serotype 2 and 3.Sequence analysis revealed highly conserved regions at residues Meth530, Thr543 Asp597, Glu616, Arg659 and Pro671. Mapping of the active site demonstrated two highly conserved residues: Ser710 and Arg729. Of the active site interacting residues, Ser796 was common amongst all ten docked compounds, indicating its importance in the drug design process. Of the ten dockedflavivirusinhibitors, NITD-203 showed the best binding affinity to the active site. Further pharmacophore modeling of NITD-203 depicted significant pharmacophoric elements that are necessary for stable binding to the active site.DiscussionThis study utilized publically available bioinformatics tools to provide a comprehensive framework on Dengue RNA-dependent RNA polymerase. Based on docking studies, a pharmacophore model was also designed to unveil the crucial pharmacophoric elements that are required when constructing an efficacious DENV inhibitor. We believe that this study will be a cornerstone in paving the road toward the design of target-specific inhibitors against DENV RdRp.
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