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Journal articles on the topic 'Pharmacophore mapping'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

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 statisticall

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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 (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

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3

Vaddavalli, Radha, Bagyanaryana Gaddam, and Pradeep Kumar Maddikara. "Discovery of Novel Inhibitors Against Resistant Streptococcus pneumoniae MurF Enzyme using Phamracophore Modeling and QSAR Analysis." International Journal of Drug Design and Discovery 2, no. 1 (2024): 403–12. https://doi.org/10.37285/ijddd.2.1.7.

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Main aim of the current study was to discover novel inhibitors against Streptococcus pneumoniae MurF enzyme using analogue based drug design. Pharmacophore mapping and 3D-QSAR analysis was performed on some previously synthesized and evaluated sulfonamide derivatives which are known to be potent inhibitors of penicillin resistant Streptococcus pneumoniae MurF receptor. 3D-QSAR study based on the principle of alignment of pharmacophoric features was performed on the same set of inhibitors using the PHASE module of Schrodinger suite. A five point pharmacophore, ADHRR, with one hydrogen bond acce

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Patel, Vijay K., and Harish Rajak. "Development of Structure Activity Correlation Model on Aroylindole Derivatives as Anticancer Agents." Letters in Drug Design & Discovery 15, no. 2 (2018): 143–53. http://dx.doi.org/10.2174/1570180814666170823161751.

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Background: Aroylindole derivatives, the structural analogs of Combretastatin A-4 has been found to possess potent growth inhibitory activity on several cancer cell lines due to its excellent antitumor and antivascular activities. The aim of present research work is to identify lead and establish structure activity correlation of trimethoxyaroylindole derivatives, using integrated ligand and structure based computational approaches. Materials and Methods: A correlation between structure and biological activity was established using computational approaches i.e., structure activity correlation

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Singh, Avineesh, and Harish Rajak. "STRUCTURAL EXPLORATION AND PHARMACOPHORIC INVESTIGATION OF PYRAZOLE BASED ANALOGS AS NOVEL HISTONE DEACETYLASE 1 INHIBITOR USING COMBINATORIAL STUDIES." International Journal of Pharmacy and Pharmaceutical Sciences 10, no. 3 (2018): 90. http://dx.doi.org/10.22159/ijpps.2018v10i3.22735.

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Objective: Histone deacetylase inhibitors (HDACi) have four essential pharmacophores as cap group, connecting unit, a linker moiety and zinc binding group for their anticancer and histone deacetylase (HDAC) inhibition activity. On the basis of this fact, the objective of this research was to evaluate the exact role of pyrazole nucleus as connecting unit and its role in the development of newer HDACi.Methods: Ligand and structure-based computer-aided drug design strategies such as pharmacophore and atom based 3D QSAR modelling, molecular docking and energetic based pharmacophore mapping have be

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Li, Rui-Juan, Ya-Li Wang, Qing-He Wang, Jian Wang, and Mao-Sheng Cheng. "In Silico Design of Human IMPDH Inhibitors Using Pharmacophore Mapping and Molecular Docking Approaches." Computational and Mathematical Methods in Medicine 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/418767.

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Inosine 5′-monophosphate dehydrogenase (IMPDH) is one of the crucial enzymes in thede novobiosynthesis of guanosine nucleotides. It has served as an attractive target in immunosuppressive, anticancer, antiviral, and antiparasitic therapeutic strategies. In this study, pharmacophore mapping and molecular docking approaches were employed to discover novel Homo sapiens IMPDH (hIMPDH) inhibitors. The Güner-Henry (GH) scoring method was used to evaluate the quality of generated pharmacophore hypotheses. One of the generated pharmacophore hypotheses was found to possess a GH score of 0.67. Ten poten

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Gajjar, Krishna A., and Anuradha K. Gajjar. "Combiphore (Structure and Ligand Based Pharmacophore) - Approach for the Design of GPR40 Modulators in the Management of Diabetes." Current Drug Discovery Technologies 17, no. 2 (2020): 233–47. http://dx.doi.org/10.2174/1570163815666181008165822.

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Background: Pharmacophore mapping and molecular docking can be synergistically integrated to improve the drug design and discovery process. A rational strategy, combiphore approach, derived from the combined study of Structure and Ligand based pharmacophore has been described to identify novel GPR40 modulators. Methods: DISCOtech module from Discovery studio was used for the generation of the Structure and Ligand based pharmacophore models which gave hydrophobic aromatic, ring aromatic and negative ionizable as essential pharmacophoric features. The generated models were validated by screening

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8

Polanski, Jaroslaw. "Self-organizing neural networks for pharmacophore mapping." Advanced Drug Delivery Reviews 55, no. 9 (2003): 1149–62. http://dx.doi.org/10.1016/s0169-409x(03)00116-9.

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9

Yang, Qian, Lupei Du, Keng-Chang Tsai, Xiaojian Wang, Minyong Li, and Qidong You. "Pharmacophore Mapping for Kv1.5 Potassium Channel Blockers." QSAR & Combinatorial Science 28, no. 1 (2008): 59–71. http://dx.doi.org/10.1002/qsar.200810050.

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10

Smriti, Khanna, Sundriyal Sandeep та V. Bharatam Prasad. "Pharmacophore mapping and virtual screening for the identification of new PPARγ agonists". Journal of Indian Chemical Society Vol. 97, Aug 2020 (2020): 1191–97. https://doi.org/10.5281/zenodo.5656159.

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Department of Medicinal Chemistry, National Institute of Pharmaceutical Education and Research (NIPER), Sector-67, S. A. S. Nagar-160 062, Punjab, India Department of Pharmacy, Birla Institute of Technology and Science-Pilani (BITS), Pilani Campus, Vidya Vihar, Pilani-333 031, Rajasthan, India <em>E-mail</em>: pvbharatam@niper.ac.in <em>Manuscript received online 07 July 2020, accepted 20 July 2020</em> Pharmacophore mapping using a DISCO-based method and virtual screening using molecular docking was employed to identify new leads as anti-hyperglycemic agents for the target PPAR<sub>&upsih;</s

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11

Reza, Rumman, Niaz Morshed, Md Nazmus Samdani, and Md Selim Reza. "Pharmacophore mapping approach to find anti-cancer phytochemicals with metformin-like activities against transforming growth factor (TGF)-beta receptor I kinase: An in silico study." PLOS ONE 18, no. 11 (2023): e0288208. http://dx.doi.org/10.1371/journal.pone.0288208.

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The most frequently prescribed first-line treatment for type II diabetes mellitus is metformin. Recent reports asserted that this diabetes medication can also shield users from cancer. Metformin induces cell cycle arrest in cancer cells. However, the exact mechanism by which this occurs in the cancer system is yet to be elucidated. Here, we investigated the impact of metformin on cell cycle arrest in cancer cells utilizing transforming growth factor (TGF)-beta pathway. TGF-ß pathway has significant effect on cell progression and growth. In order to gain an insight on the underlying molecular m

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Ujiantari, Navista Sri Octa, and Cintya Nurul Apsari. "Pharmacophore mapping of Angiotensin Converting Enzymes (ACEs): Insight to Binding Site of ACE1 and ACE2." Biology, Medicine, & Natural Product Chemistry 13, no. 2 (2024): 337–41. http://dx.doi.org/10.14421/biomedich.2024.132.337-341.

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Angiotensin Converting Enzymes (ACEs) are carboxypeptidase enzymes involved in the renin-angiotensin system (RAS), which catalyze angiotensin I by cleavage of the peptide bond. ACE1 has been known as a target for antihypertensive drugs. Another homolog of ACE1, ACE2 has been popular since 2020 because this enzyme is responsible for the SARS-COV2 infection in the human body. Interestingly, it was found that ACE1 inhibitors did not inhibit ACE2. Hence, this study aims to elucidate the pharmacophore of ACE1 and ACE2 in order to understand the mechanism of these different inhibitions. Pharmacophor

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13

Zhao, Shizhen, Xinping Li, Wenjing Peng, et al. "Ligand-based pharmacophore modeling, virtual screening and biological evaluation to identify novel TGR5 agonists." RSC Advances 11, no. 16 (2021): 9403–9. http://dx.doi.org/10.1039/d0ra10168k.

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14

J.T, Patrisha, Madu Battu, Sriram D., and Yogeeswari P. "3D-QSAR Studies Combined with Virtual Screening to Identify Novel Inhibitors of N-Acetyl Glucosamine 1-Phosphate Uridyltransferase from Mycobacterium Tuberculosis." International Journal of Drug Design and Discovery 4, no. 3 (2025): 1134–48. https://doi.org/10.37285/ijddd.4.3.4.

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The emergence of drug resistant tuberculosis poses a serious concern globally and researchers are in rigorous search for new drugs to fight against the disease. Recently, the bacterial GlmU protein (N-acetylglucosamine-1-phosphate uridyltransferase), involved in peptidoglycan, lipopolysaccharide and techoic acid synthesis, has been identified to be crucial for cell wall synthesis and survival of bacteria. The absence of this enzyme in humans makes it an important and attractive target to develop drugs for the treatment of this lethal disease. In the present study, pharmacophore mapping studies

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15

Ghate, Manjunath D., Ahmed Baksariya, Nirzari Gupta, and Bhumika D. Patel. "Pharmacophore Mapping, Virtual Screening and Molecular Docking Studies of DPP-IV Inhibitors." International Journal of Drug Design and Discovery 4, no. 4 (2025): 1231–48. https://doi.org/10.37285/ijddd.4.4.7.

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Dipeptidyl peptidase IV (DPP-IV) is a valid drug target for type-2 diabetes and DPP-IV inhibitors have been proven to efficiently improve glucose tolerance. Chemical features based 3D pharmacophore model for DPP-IV was developed using a training set of 16 structurally diverse known DPP-IV inhibitors using DISCOtech module of Tripos Sybyl X1.2. The best pharmacophore hypothesis, which identified inhibitors with a highest score of 0.5859, tolerance of 0.500 and D-mean of 3.9179, contained five features, namely, one positive nitrogen (PN), one hydrophobic point (HY), two acceptor sites (AS) and o

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16

Yao, Ting-Ting, Jiang-Feng Xie, Xing-Guo Liu, et al. "Integration of pharmacophore mapping and molecular docking in sequential virtual screening: towards the discovery of novel JAK2 inhibitors." RSC Advances 7, no. 17 (2017): 10353–60. http://dx.doi.org/10.1039/c6ra24959k.

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17

Mesiti, Francesco, Alexandra Gaspar, Daniel Chavarria, et al. "Mapping Chromone-3-Phenylcarboxamide Pharmacophore: Quid Est Veritas?" Journal of Medicinal Chemistry 64, no. 15 (2021): 11169–82. http://dx.doi.org/10.1021/acs.jmedchem.1c00510.

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18

Equbal, Tabish, Om Silakari, Gundla Rambabu, and Muttineni Ravikumar. "Pharmacophore mapping of diverse classes of farnesyltransferase inhibitors." Bioorganic & Medicinal Chemistry Letters 17, no. 6 (2007): 1594–600. http://dx.doi.org/10.1016/j.bmcl.2006.12.087.

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19

Nagar, Shuchi, Md Ataul Islam, Suvadra Das, Arup Mukherjee, and Achintya Saha. "Pharmacophore mapping of flavone derivatives for aromatase inhibition." Molecular Diversity 12, no. 1 (2008): 65–76. http://dx.doi.org/10.1007/s11030-008-9077-9.

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20

Mizukoshi, Yumiko, Aya Abe, Takeshi Takizawa, et al. "An Accurate Pharmacophore Mapping Method by NMR Spectroscopy." Angewandte Chemie International Edition 51, no. 6 (2011): 1362–65. http://dx.doi.org/10.1002/anie.201104905.

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21

Mizukoshi, Yumiko, Aya Abe, Takeshi Takizawa, et al. "An Accurate Pharmacophore Mapping Method by NMR Spectroscopy." Angewandte Chemie 124, no. 6 (2011): 1391–94. http://dx.doi.org/10.1002/ange.201104905.

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22

Zhao, Shizhen, Wenjing Peng, Xinping Li, et al. "Pharmacophore modeling and virtual screening studies for discovery of novel farnesoid X receptor (FXR) agonists." RSC Advances 11, no. 4 (2021): 2158–66. http://dx.doi.org/10.1039/d0ra09320c.

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23

Zhao, Shizhen, Wenjing Peng, Xinping Li, et al. "Pharmacophore modeling and virtual screening studies for discovery of novel farnesoid X receptor (FXR) agonists." RSC Advances 11, no. 4 (2021): 2158–66. http://dx.doi.org/10.1039/d0ra09320c.

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24

Auer, Renate, Karin Kloiber, Andrea Vavrinska, Leonhard Geist, Nicolas Coudevylle, and Robert Konrat. "Pharmacophore Mapping via Cross-Relaxation during Adiabatic Fast Passage." Journal of the American Chemical Society 132, no. 5 (2010): 1480–81. http://dx.doi.org/10.1021/ja910098s.

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25

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,

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26

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 (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 s

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27

Samad, Abdul, Moawiah M. Naffaa, Mohammed Afroz Bakht, Manav Malhotra, and Majid A. Ganaie. "Target Based Designing of Anthracenone Derivatives as Tubulin Polymerization Inhibiting Agents: 3D QSAR and Docking Approach." International Journal of Medicinal Chemistry 2014 (April 17, 2014): 1–15. http://dx.doi.org/10.1155/2014/658016.

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Novel anthracenone derivatives were designed through in silico studies including 3D QSAR, pharmacophore mapping, and molecular docking approaches. Tubulin protein was explored for the residues imperative for activity by analyzing the binding pattern of colchicine and selected compounds of anthracenone derivatives in the active domain. The docking methodology applied in the study was first validated by comparative evaluation of the predicted and experimental inhibitory activity. Furthermore, the essential features responsible for the activity were established by carrying out pharmacophore mappi

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Borota, Ana, Sorin Avram, Ramona Curpan, et al. "In silico studies on smoothened human receptor and its antagonists in search of anticancer effects." Journal of the Serbian Chemical Society 85, no. 3 (2020): 335–46. http://dx.doi.org/10.2298/jsc190403085b.

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Lately, the cancers related with abnormal hedgehog (Hh) signalling pathway are targeted by smoothened (SMO) receptor inhibitors that are rapidly developing. Still, the problems of known inhibitors such as severe side effects, weak potency against solid tumors or even the acquired resistance need to be overcome by developing new suitable inhibitors. To explore the structural requirements of antagonists needed for SMO receptor inhibition, pharmacophore mapping, 3D-QSAR models, database screening and docking studies were performed. The best selected pharmacophore hypothesis based on which statist

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Ouassaf, Mebarka, Faizan Abul Qais, Salah Belaidi, Mohamed Bakhouch, Ahmed Said Mohamed, and Samir Chtita. "Combined Pharmacophore Modeling, 3D-QSAR, Molecular Docking and Molecular Dynamics Study on Indolyl-aryl-sulfone Derivatives as New HIV1 Inhibitors." Acta Chimica Slovenica 69, no. 2 (2022): 489–506. http://dx.doi.org/10.17344/acsi.2022.7427.

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The present study deals with the in silico of 45 indolyl-aryl-sulfones known as anti-HIV1. The data were collected from recent previously reported inhibitors and divided into a sub-set of 33 compounds as the training set and the remaining 12 compounds were kept in the test set. The selected pharmacophore–ADRRR–yielded a statistically significant 3D-QSAR model containing high confidence scores (R2 = 0.930, Q2 = 0.848, and RMSE = 0.460). The predictive power of the established pharmacophore model was validated with an external test (r2 = 0.848). A systematic virtual screening workflow shows an e

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Brown, Benjamin P., Jeffrey Mendenhall, and Jens Meiler. "BCL::MolAlign: Three-Dimensional Small Molecule Alignment for Pharmacophore Mapping." Journal of Chemical Information and Modeling 59, no. 2 (2019): 689–701. http://dx.doi.org/10.1021/acs.jcim.9b00020.

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31

Schütz, Anne K., Simone Hornemann, Marielle A. Wälti, et al. "Binding of Polythiophenes to Amyloids: Structural Mapping of the Pharmacophore." ACS Chemical Neuroscience 9, no. 3 (2017): 475–81. http://dx.doi.org/10.1021/acschemneuro.7b00397.

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Sharma, Vikas, and Vipin Kumar. "Pharmacophore mapping studies on indolizine derivatives as 15-LOX inhibitors." Bulletin of Faculty of Pharmacy, Cairo University 53, no. 1 (2015): 63–68. http://dx.doi.org/10.1016/j.bfopcu.2015.03.001.

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Sánchez-Pedregal, Víctor M., Marcel Reese, Jens Meiler, Marcel J. J. Blommers, Christian Griesinger, and Teresa Carlomagno. "The INPHARMA Method: Protein-Mediated Interligand NOEs for Pharmacophore Mapping." Angewandte Chemie 117, no. 27 (2005): 4244–47. http://dx.doi.org/10.1002/ange.200500503.

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Sánchez-Pedregal, Víctor M., Marcel Reese, Jens Meiler, Marcel J. J. Blommers, Christian Griesinger, and Teresa Carlomagno. "The INPHARMA Method: Protein-Mediated Interligand NOEs for Pharmacophore Mapping." Angewandte Chemie International Edition 44, no. 27 (2005): 4172–75. http://dx.doi.org/10.1002/anie.200500503.

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Mishra, Shweta, Rashmi Dahima та Rajesh Sharma. "Design of Some Benzimidazoles as Target for α-Glucosidase Inhibitors". Journal of Drug Delivery and Therapeutics 10, № 2-s (2020): 198–208. http://dx.doi.org/10.22270/jddt.v10i2-s.4017.

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Diabetes mellitus is rising globally touching more than 180 million people worldwide. This is prevailing mostly in type 2 diabetes and according to WHO report the incidence is likely to be more than doubled by 2030. α-Glucosidase inhibitors work by reducing the amount of glucose that the intestines absorb from food. In this work, forty-five benzimidazole analogues were studied using 3D QSAR, HQSAR, pharmacophore mapping and based on their results 60 compounds were designed. The results show that the best Comparative Molecular Field Analysis (CoMFA) model has q2 = 0.742 and r2 = 0.973, and the

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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 (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 a

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Batista, Victor S., Adriano Marques Gonçalves та Nailton M. Nascimento-Júnior. "Pharmacophore Mapping Combined with dbCICA Reveal New Structural Features for the Development of Novel Ligands Targeting α4β2 and α7 Nicotinic Acetylcholine Receptors". Molecules 27, № 23 (2022): 8236. http://dx.doi.org/10.3390/molecules27238236.

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The neuronal nicotinic acetylcholine receptors (nAChRs) belong to the ligand-gated ion channel (GLIC) group, presenting a crucial role in several biological processes and neuronal disorders. The α4β2 and α7 nAChRs are the most abundant in the central nervous system (CNS), being involved in challenging diseases such as epilepsy, Alzheimer’s disease, schizophrenia, and anxiety disorder, as well as alcohol and nicotine dependencies. In addition, in silico-based strategies may contribute to revealing new insights into drug design and virtual screening to find new drug candidates to treat CNS disor

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Lee, Kuan-Han, та Bor-Ruey Huang. "Three-Dimensional Pharmacophore Mapping of Certain Anticancer α-Methylene-γ-Butyrolactones". Oncology Research Featuring Preclinical and Clinical Cancer Therapeutics 13, № 11 (2003): 471–78. http://dx.doi.org/10.3727/000000003108747992.

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Nagamani, Selvaraman. "E-Pharmacophore mapping and docking studies on Vitamin D receptor (VDR)." Bioinformation 8, no. 15 (2012): 705–10. http://dx.doi.org/10.6026/97320630008705.

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Mottola, David M., Sergei Laiter, Val J. Watts, et al. "Conformational Analysis of D1Dopamine Receptor Agonists: Pharmacophore Assessment and Receptor Mapping†." Journal of Medicinal Chemistry 39, no. 1 (1996): 285–96. http://dx.doi.org/10.1021/jm9502100.

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Fukunishi, Yoshifumi, Yumiko Mizukoshi, Koh Takeuchi, Ichio Shimada, Hideo Takahashi, and Haruki Nakamura. "Protein–ligand docking guided by ligand pharmacophore-mapping experiment by NMR." Journal of Molecular Graphics and Modelling 31 (November 2011): 20–27. http://dx.doi.org/10.1016/j.jmgm.2011.08.002.

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Barbosa, Bárbara Lima Fonseca, Tulio Resende Freitas, Michell de Oliveira Almeida, et al. "Virtual Screening of Adenylate Kinase 3 Inhibitors Employing Pharmacophoric Model, Molecular Docking, and Molecular Dynamics Simulations as Potential Therapeutic Target in Chronic Lymphocytic Leukemia." Future Pharmacology 1, no. 1 (2021): 60–79. http://dx.doi.org/10.3390/futurepharmacol1010006.

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Adenylate kinase 3 (AK3) is an enzyme located in the mitochondrial matrix involved in purine homeostasis. This protein has been considered a potential therapeutic target in chronic lymphocytic leukemia (CLL), because the silencing of the AK3 gene has inhibited cell growth in CLL in vitro models. This study aimed to design potential AK3 inhibitors by applying molecular modeling techniques. Through the mapping of AK3 binding sites, essential interaction fields for pharmacophore design were identified. Online libraries were virtually screened by using a pharmacophore model, and 6891 compounds exh

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Bak, A., V. Kozik, A. Smolinski, and J. Jampilek. "Multidimensional (3D/4D-QSAR) probability-guided pharmacophore mapping: investigation of activity profile for a series of drug absorption promoters." RSC Advances 6, no. 80 (2016): 76183–205. http://dx.doi.org/10.1039/c6ra15820j.

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A hybrid approach that combines 3D and 4D-QSAR methods based on grid and neural paradigms with automated IVE-PLS procedure was examined to identify the pharmacophore pattern for cholic acid derivatives as potential drug absorption promoters.

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Ablise, M., A. Cartier, G. Siest, S. Visvikis, and V. Loppinet. "Molecular Pharmacophore Determination of Lipid Lowering Drugs with the Receptor Mapping Method." Mini-Reviews in Medicinal Chemistry 2, no. 2 (2002): 97–102. http://dx.doi.org/10.2174/1389557024605528.

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Kontogiorgis, C., P. Papaioannou, and D. Hadjipavlou-Litina. "Matrix Metalloproteinase Inhibitors: A Review on Pharmacophore Mapping and (Q)Sars Results." Current Medicinal Chemistry 12, no. 3 (2005): 339–55. http://dx.doi.org/10.2174/0929867053363243.

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Roy, Kunal. "Editorial (Hot Topic: Pharmacophore Mapping and High Throughput Screening in Drug Discovery)." Current Topics in Medicinal Chemistry 13, no. 9 (2013): 963–64. http://dx.doi.org/10.2174/1568026611313090001.

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47

Orts, Julien, Christian Griesinger, and Teresa Carlomagno. "The INPHARMA technique for pharmacophore mapping: A theoretical guide to the method." Journal of Magnetic Resonance 200, no. 1 (2009): 64–73. http://dx.doi.org/10.1016/j.jmr.2009.06.006.

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Mitra, Indrani, Achintya Saha, and Kunal Roy. "Pharmacophore mapping of arylamino-substituted benzo[b]thiophenes as free radical scavengers." Journal of Molecular Modeling 16, no. 10 (2010): 1585–96. http://dx.doi.org/10.1007/s00894-010-0661-4.

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Kalyaanamoorthy, Subha, and Yi-Ping Phoebe Chen. "Energy based pharmacophore mapping of HDAC inhibitors against class I HDAC enzymes." Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics 1834, no. 1 (2013): 317–28. http://dx.doi.org/10.1016/j.bbapap.2012.08.009.

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Pandey, Anubhuti, Sarvesh Kumar Paliwal, and Shailendra Kumar Paliwal. "Chemical Feature-Based Molecular Modeling of Urotensin-II Receptor Antagonists: Generation of Predictive Pharmacophore Model for Early Drug Discovery." Journal of Chemistry 2014 (2014): 1–16. http://dx.doi.org/10.1155/2014/921863.

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Abstract:

For a series of 35 piperazino-phthalimide and piperazino-isoindolinone based urotensin-II receptor (UT) antagonists, a thoroughly validated 3D pharmacophore model has been developed, consisting of four chemical features: one hydrogen bond acceptor lipid (HBA_L), one hydrophobe (HY), and two ring aromatic (RA). Multiple validation techniques like CatScramble, test set prediction, and mapping analysis of advanced known antagonists have been employed to check the predictive power and robustness of the developed model. The results demonstrate that the best model, Hypo 1, shows a correlation (r) of

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