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Journal articles on the topic 'Computational chemistry|Pharmacology'

Author: Grafiati

Published: 9 July 2021

Last updated: 29 July 2025

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1

Gilson, Michael K., Tiqing Liu, Michael Baitaluk, George Nicola, Linda Hwang, and Jenny Chong. "BindingDB in 2015: A public database for medicinal chemistry, computational chemistry and systems pharmacology." Nucleic Acids Research 44, no. D1 (2015): D1045—D1053. http://dx.doi.org/10.1093/nar/gkv1072.

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2

Kunduracioglu, Ahmet. "A Computational (DFT) Study on the Anti-Malarial Drug: Lumefantrine." Applied Sciences 13, no. 16 (2023): 9219. http://dx.doi.org/10.3390/app13169219.

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This study aims to investigate the spectroscopic and structural properties of the compound Lumefantrine, which is important in pharmacology because of its anti-malarial effect. The structural and spectroscopic properties of this molecule, such as bond lengths, bond angles, FT-IR and NMR spectra were handled computationally using a computational chemistry suite: Spar-tan’14. Both HF and DFT methods were used with different basis sets for the calculations. The results calculated by the software were compared to experimental results from the literature. Both computational and experimental results

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3

Alsantali, Reem I., Abdulaziz M. Almohyawi, Manzoor A. Rather, et al. "From silico to benchtop: cosmosiin as a PD-1/PDL-1 immune checkpoint inhibitor revealed through DFT, network pharmacology analysis, and molecular docking integrated experimental verification." RSC Advances 15, no. 28 (2025): 22285–310. https://doi.org/10.1039/d5ra03831f.

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This study investigated the anti-PD-1/PD-L1 inhibition potential of the flavonoid cosmosiin against breast cancer (BC) using computational chemistry, network pharmacology, bioinformatics, and validated by experimental assays.

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4

Dahdouh, Elias, Lisa Allander, Linda Falgenhauer, et al. "Computational Modeling and Design of New Inhibitors of Carbapenemases: A Discussion from the EPIC Alliance Network." International Journal of Molecular Sciences 23, no. 17 (2022): 9746. http://dx.doi.org/10.3390/ijms23179746.

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The EPIC consortium brings together experts from a wide range of fields that include clinical, molecular and basic microbiology, infectious diseases, computational biology and chemistry, drug discovery and design, bioinformatics, biochemistry, biophysics, pharmacology, toxicology, veterinary sciences, environmental sciences, and epidemiology. The main question to be answered by the EPIC alliance is the following: “What is the best approach for data mining on carbapenemase inhibitors and how to translate this data into experiments?” From this forum, we propose that the scientific community thin

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5

Sayed, Mostafa, Ahmed M. Sayed, Ahmed A. El-Rashedy, et al. "Anti-inflammatory Activity and Computational Biology Study of Indole/Pyrimidine Hybrids." Current Organic Chemistry 28, no. 1 (2024): 56–64. http://dx.doi.org/10.2174/0113852728289430231227042754.

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Abstract: This research paper embarks on an interdisciplinary exploration encompassing synthetic chemistry, pharmacology, and computational biology. The development of novel anti-inflammatory agents is an imperative endeavor within pharmaceutical research. Pyrimidines and thienopyrimidines are class of heterocyclic compounds that have gained prominence for their diverse pharmacological properties, including potential anti-inflammatory effects. When augmented with an indole moiety, these compounds exhibit structural diversity that can profoundly influence their biological activities. The integr

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6

Lukaschuk, Tatyana. "Features of teaching analytical chemistry to pharmaceutical students." Journal of Pedagogical Studies 9, no. 1 (2024): 150–56. http://dx.doi.org/10.12737/2500-3305-2024-9-1-150-156.

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The article reveals some features of teaching analytical chemistry as one of the main subjects for training future pharmacists. It is shown that the course of analytical chemistry forms the basic practical skills necessary in subsequent professional activity. The difficulties of teaching analytical chemistry associated with time constraints and a large volume of material that is submitted for consideration at the lecture are noted. This requires a very clear structure and sequence from the teacher, as well as multimedia accompaniment, which contributes to a better perception of the material pr

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7

Pannequin, Anaïs, Erik Laurini, Laurent Giordano, Alain Muselli, Sabrina Pricl, and Aura Tintaru. "Caution: Chemical Instability of Natural Biomolecules During Routine Analysis." Molecules 25, no. 14 (2020): 3292. http://dx.doi.org/10.3390/molecules25143292.

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Natural products (NPs) constitute a significant source of active biomolecules widely used in medicine, pharmacology and cosmetics. However, NPs structural characterization has the drawback of their chemical instability during the extraction steps and their likely transformation during the analytical protocol. In particular, tamariscol and conocephalenol are two compounds largely used in the cosmetic industry for their odorant properties. Thus, in the present study, we focused on the evolution of these two metabolites (extracted from Frullania tamarisci and Conocephalum conicum, respectively),

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8

Murata, Shizuaki, and Sudhir S. Landge. "A Computational Study on Stabilities of Dihydropterins." HETEROCYCLES 68, no. 8 (2006): 1705. http://dx.doi.org/10.3987/com-06-10777.

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9

Xue, Weiwei, Tingting Fu, Guoxun Zheng, et al. "Recent Advances and Challenges of the Drugs Acting on Monoamine Transporters." Current Medicinal Chemistry 27, no. 23 (2020): 3830–76. http://dx.doi.org/10.2174/0929867325666181009123218.

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Background: The human Monoamine Transporters (hMATs), primarily including hSERT, hNET and hDAT, are important targets for the treatment of depression and other behavioral disorders with more than the availability of 30 approved drugs. Objective: This paper is to review the recent progress in the binding mode and inhibitory mechanism of hMATs inhibitors with the central or allosteric binding sites, for the benefit of future hMATs inhibitor design and discovery. The Structure-Activity Relationship (SAR) and the selectivity for hit/lead compounds to hMATs that are evaluated by in vitro and in viv

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10

Loschen, Christoph, and Andreas Klamt. "Computational Screening of Drug Solvates." Pharmaceutical Research 33, no. 11 (2016): 2794–804. http://dx.doi.org/10.1007/s11095-016-2005-2.

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11

Nascimento, Mayara, Stefany Moura, Lidia Parra, et al. "Ponatinib: A Review of the History of Medicinal Chemistry behind Its Development." Pharmaceuticals 17, no. 10 (2024): 1361. http://dx.doi.org/10.3390/ph17101361.

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The primary treatment for chronic myeloid leukemia (CML) involves first- and second-generation tyrosine kinase inhibitors (TKIs), such as imatinib, nilotinib, bosutinib, and dasatinib. However, these medications are ineffective against mutations in the kinase domain of the ABL1 protein, particularly in the protein with the T315I mutation. To address this, ponatinib (PNT), a third-generation inhibitor, was developed. Despite its efficacy in treating the BCR-ABL1T315I mutation, the use of PNT was briefly suspended in 2013 due to serious adverse effects but was subsequently reintroduced to the ma

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12

Schmidt, W. F., and George Gassner. "Chirality and Computational Chemistry: A New Direction." Current Medicinal Chemistry 1, no. 6 (1995): 502–10. http://dx.doi.org/10.2174/092986730106220216115714.

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Abstract: Physical and biological responses to natural and synthetic chemicals occur only at molecular distances and at molecular dimensions. Computational chemistry calculates results in molecular dimensions, but has serious difficulties in accurately predicting chiral interactions between diastereoisomers and/or enantiomers. Prediction of structures and conformations for more complicated chiral interactions such as in protein folding and protein binding may be intrinsically inaccurate because the molecular consequences of the forces between these spatially close chiral centers are not explic

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13

Stevens, John, and John T. Blair. "A Computational Examination of the Anomeric Effect in 1,3-Diazanes." HETEROCYCLES 37, no. 3 (1994): 1473. http://dx.doi.org/10.3987/com-93-s54.

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14

Parikesit, A. A., and U. S. F. Tambunan. "COMPUTATIONAL PROTEIN DESIGN IN GREEN CHEMISTRY." Rasayan Journal of Chemistry 11, no. 3 (2018): 1133–38. http://dx.doi.org/10.31788/rjc.2018.1133038.

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15

Cavalli, Andrea. "Computational Drug Design: A Guide for Computational and Medicinal Chemists. By D. C. Young." ChemMedChem 5, no. 2 (2010): 305–6. http://dx.doi.org/10.1002/cmdc.200900460.

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16

Caflisch, Amedeo, and Martin Karplus. "Computational combinatorial chemistry for de novo ligand design: Review and assessment." Perspectives in Drug Discovery and Design 3, no. 1 (1995): 51–84. http://dx.doi.org/10.1007/bf02174467.

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17

Matsumura, Shuji, Ryukichi Takagi, Satoshi Kojima, Katsuo Ohkata та Manabu Abe. "Computational Study on the Cyclopropanation Reaction of Ylides Generated from α-Pyridiniumacetates". HETEROCYCLES 81, № 11 (2010): 2479. http://dx.doi.org/10.3987/com-10-11926.

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18

A. Kheder, Nabila, Ahmad M. Farag, Kamal M. Dawood, and Ahmed M. El Defrawy. "A Facile Access and Computational Studies of Some New 4,5'-Bipyrazole Derivatives." HETEROCYCLES 94, no. 7 (2017): 1245. http://dx.doi.org/10.3987/com-17-13707.

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19

C. Tice, Nathan, Steven Wild, Hannah Dendinger, Bangbo Yan, and Pauline Norris. "Synthesis, Characterization, and Computational Studies of Some Perfluorinated Cyclopentapyridazines for Biological Applications." HETEROCYCLES 106, no. 8 (2023): 1355. http://dx.doi.org/10.3987/com-23-14878.

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20

Hatanaka, Miho, Takayoshi Yoshimura, Maneeporn Puripat, and Vudhichai Parasuk. "Stereoselectivity of the Biginelli Reaction Catalyzed by Chiral Primary Amine: A Computational Study." HETEROCYCLES 103, no. 2 (2021): 893. http://dx.doi.org/10.3987/com-20-s(k)55.

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21

Orena, Mario, Roberta Galeazzi, Cesare Marucchini, and Gianni Porzi. "The Cleavage of Sulfonylurea Herbicide Rimsulfuron® undr Basic Conditions: A Computational Investigation." HETEROCYCLES 53, no. 11 (2000): 2517. http://dx.doi.org/10.3987/com-00-8986.

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22

Stumpfe, Dagmar, Maxim Frizler, Mihiret T Sisay, et al. "Hit Expansion through Computational Selectivity Searching." ChemMedChem 4, no. 1 (2009): 52–54. http://dx.doi.org/10.1002/cmdc.200800304.

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23

Reid, Darren L., C. Jeffrey Calvitt, Mark T. Zell, Kenneth G. Miller, and Carol A. Kingsmill. "Early Prediction of Pharmaceutical Oxidation Pathways by Computational Chemistry and Forced Degradation." Pharmaceutical Research 21, no. 9 (2004): 1708–17. http://dx.doi.org/10.1023/b:pham.0000041469.96466.12.

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24

Du, Di, Chia-Hua Chang, Yumeng Wang, et al. "Response envelope analysis for quantitative evaluation of drug combinations." Bioinformatics 35, no. 19 (2019): 3761–70. http://dx.doi.org/10.1093/bioinformatics/btz091.

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Abstract Motivation The concept of synergy between two agents, over a century old, is important to the fields of biology, chemistry, pharmacology and medicine. A key step in drug combination analysis is the selection of an additivity model to identify combination effects including synergy, additivity and antagonism. Existing methods for identifying and interpreting those combination effects have limitations. Results We present here a computational framework, termed response envelope analysis (REA), that makes use of 3D response surfaces formed by generalized Loewe Additivity and Bliss Independ

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25

Ramasami, Ponnadurai, John A. Joule, Lydia Rhyman, Sabina Jhaumeer-Laulloo, and Luis R. Domingo. "Computational Assessment of 1,3-Dipolar Cycloaddition of Nitrile Oxides with Ethene and [60]Fullerene." HETEROCYCLES 84, no. 2 (2012): 719. http://dx.doi.org/10.3987/com-11-s(p)46.

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26

Mori, Atsunori, Shiomi Ashida, Yukiko Ito, et al. "Computational Studies on the Racemization Barriers of Winding Vine-Shaped Heterobiaryls with Molecular Asymmetry." HETEROCYCLES 99, no. 1 (2019): 294. http://dx.doi.org/10.3987/com-18-s(f)23.

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27

Tiezza, Marco Dalla, Giovanni Ribaudo, and Laura Orian. "Organodiselenides: Organic Catalysis and Drug Design Learning from Glutathione Peroxidase." Current Organic Chemistry 23, no. 13 (2019): 1381–402. http://dx.doi.org/10.2174/1385272822666180803123137.

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Organodiselenides are an important class of compounds characterized by the presence of two adjacent covalently bonded selenium nuclei. Among them, diaryldiselenides and their parent compound diphenyl diselenide attract continuing interest in chemistry as well as in close disciplines like medicinal chemistry, pharmacology and biochemistry. A search in SCOPUS database has revealed that in the last three years 105 papers have been published on the archetypal diphenyl diselenide and its use in organic catalysis and drug tests. The reactivity of the Se-Se bond and the redox properties of selenium m

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28

Al Omari, Mahmoud M., Adnan A. Badwan, Mohammad B. Zughul, and J. Eric D. Davies. "Fexofenadine/Cyclodextrin Inclusion Complexation: Phase Solubility, Thermodynamic, Physicochemical, and Computational Analysis." Drug Development and Industrial Pharmacy 33, no. 11 (2007): 1205–15. http://dx.doi.org/10.1080/03639040701377672.

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29

Rohit Saxena. "EXPLORING APPROACHES FOR INVESTIGATING PHYTOCHEMISTRY: METHODS AND TECHNIQUES." MEDALION JOURNAL: Medical Research, Nursing, Health and Midwife Participation 4, no. 2 (2023): 65–73. http://dx.doi.org/10.59733/medalion.v4i2.76.

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The extraction, isolation, and analysis of bioactive compounds from plants are fundamental in the study of medicinal plants. This review explores various techniques and methods used for the extraction, isolation, and analysis of bioactive compounds from plant materials. Traditional methods such as maceration, percolation, and Soxhlet extraction are commonly used, but novel techniques have emerged to enhance efficiency and selectivity. Microwave-assisted extraction (MAE), ultrasound-assisted extraction (UAE), pressurized liquid extraction (PLE), supercritical fluid extraction (SFE), and enzyme-

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30

Orobator, Enibokun, Chidinma Nnodumele, Negasi Tsegay, et al. "Applications of Artificial Intelligence in Plant-Based Anticancer Drug Discovery and Development." Journal of Pharma Insights and Research 3, no. 2 (2025): 203–10. https://doi.org/10.69613/fbzjfk64.

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The discovery of novel anticancer therapeutics faces significant challenges, including extended development timelines and high failure rates. Plants serve as an important source for anticancer compounds like paclitaxel and vincristine, yet traditional phytochemical discovery methods remain inefficient. Artificial intelligence (AI) and machine learning (ML) present innovative solutions to expedite the identification, validation, and optimization of plant-derived anticancer agents. Advanced computational techniques, including virtual screening, molecular modeling, and network pharmacology, enabl

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31

Persson, Linda C., Christopher J. H. Porter, William N. Charman, and Christel A. S. Bergström. "Computational Prediction of Drug Solubility in Lipid Based Formulation Excipients." Pharmaceutical Research 30, no. 12 (2013): 3225–37. http://dx.doi.org/10.1007/s11095-013-1083-7.

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32

Oh, Byeongtaek, Yugyung Lee, Mingui Fu, and Chi H. Lee. "Computational Analysis on Down-Regulated Images of Macrophage Scavenger Receptor." Pharmaceutical Research 34, no. 10 (2017): 2066–74. http://dx.doi.org/10.1007/s11095-017-2211-6.

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33

Bednárová, Lucie, Petr Maloň, and Petr Bouř. "Spectroscopic properties of the nonplanar amide group: A computational study." Chirality 19, no. 10 (2007): 775–86. http://dx.doi.org/10.1002/chir.20462.

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34

Egidi, Franco, Marco Fusè, Alberto Baiardi, Julien Bloino, Xiaosong Li, and Vincenzo Barone. "Computational simulation of vibrationally resolved spectra for spin-forbidden transitions." Chirality 30, no. 7 (2018): 850–65. http://dx.doi.org/10.1002/chir.22864.

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35

Alhalaweh, Amjad, Ahmad Alzghoul, and Waseem Kaialy. "Data mining of solubility parameters for computational prediction of drug–excipient miscibility." Drug Development and Industrial Pharmacy 40, no. 7 (2013): 904–9. http://dx.doi.org/10.3109/03639045.2013.789906.

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36

Okada, Etsuji, Norio Ota, Souma Nakagawa, and Yasuhiro Kamitori. "Computational Study for the Aromatic Nucleophilic Substitution of 4-Dimethylamino-3-trifluoroacetyl- quinoline with Various Nucleophiles." HETEROCYCLES 103, no. 2 (2021): 918. http://dx.doi.org/10.3987/com-20-s(k)62.

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37

Khandelwal, Akash, Praveen M. Bahadduri, Cheng Chang, James E. Polli, Peter W. Swaan, and Sean Ekins. "Computational Models to Assign Biopharmaceutics Drug Disposition Classification from Molecular Structure." Pharmaceutical Research 24, no. 12 (2007): 2249–62. http://dx.doi.org/10.1007/s11095-007-9435-9.

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38

Sivakumar, Ponnurengam Malliappan, Naga Vignesh, Gopal Ramesh Kumar, and Mukesh Doble. "Computational approaches to enhance activity of taxanes as antimitotic agent." Medicinal Chemistry Research 21, no. 9 (2011): 2557–70. http://dx.doi.org/10.1007/s00044-011-9779-x.

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39

Karthick, V., and K. Ramanathan. "Computational investigation of oseltamivir resistance in influenza A (H5N1) virus." Medicinal Chemistry Research 22, no. 12 (2013): 5764–71. http://dx.doi.org/10.1007/s00044-013-0551-2.

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40

Batista, José, Tim Friedrichson, Georg Schlechtingen, Tobias Braxmeier, Gary Jennings, and Jürgen Bajorath. "Computational screening for membrane-directed inhibitors of mast cell activation." European Journal of Medicinal Chemistry 45, no. 6 (2010): 2700–2704. http://dx.doi.org/10.1016/j.ejmech.2010.01.061.

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41

Lin, Hao. "Computational Methods and Resources in Biological and Medical Data." Current Medicinal Chemistry 29, no. 5 (2022): 786–88. http://dx.doi.org/10.2174/092986732905220214141331.

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42

Banks, Harold D., Alessandro Dondoni, Martin Kleban, and Alberto Marra. "Computational and experimental studies of di- and tetrasubstituted calix[4]arenes." Chirality 14, no. 2-3 (2002): 173–79. http://dx.doi.org/10.1002/chir.10059.

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43

Schefzick, Sabine, Wolfgang Lindner, Kenny B. Lipkowitz, and Mehran Jalaie. "Enantiodiscrimination by a quinine-based chiral stationary phase: A computational study." Chirality 12, no. 1 (2000): 7–15. http://dx.doi.org/10.1002/(sici)1520-636x(2000)12:1<7::aid-chir3>3.0.co;2-q.

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44

Bou?, Petr, Kamil Z�ruba, Marie Urbanov�, et al. "Vibrational circular dichroism of tetraphenylporphyrin in peptide complexes? A computational study." Chirality 12, no. 4 (2000): 191–98. http://dx.doi.org/10.1002/(sici)1520-636x(2000)12:4<191::aid-chir5>3.0.co;2-w.

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45

Wiwattanawongsa, Kamonthip, Vimon Tantishaiyakul, Luelak Lomlim, Yon Rojanasakul, Sirirat Pinsuwan, and Sanae Keawnopparat. "Experimental and Computational Studies of Epithelial Transport of Mefenamic Acid Ester Prodrugs." Pharmaceutical Research 22, no. 5 (2005): 721–27. http://dx.doi.org/10.1007/s11095-005-2587-6.

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46

Yamanaka, Masahiro, Seiji Shirakawa, Ayaka Mochizuki, Takumi Nakamura, and Keiji Maruoka. "Trialkylsulfonium and Tetraalkylammonium Salts as Hydrogen-Bonding Catalysts in an Aza-Diels-Alder Reaction: Experimental and Computational Studies." HETEROCYCLES 101, no. 2 (2020): 580. http://dx.doi.org/10.3987/com-19-s(f)48.

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47

Kahr, Bart, Brianne Chittenden, and Andrew Rohl. "Robert Boyle's chiral crystal chemistry: Computational re-evaluation of enantioselective adsorption on quartz." Chirality 18, no. 2 (2006): 127–33. http://dx.doi.org/10.1002/chir.20229.

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48

Correa-Basurto, J., L. Rodríguez-Páez, E. S. Aguiar-Moreno, et al. "Computational and experimental evaluation of ornithine derivatives as ornithine decarboxylase inhibitors." Medicinal Chemistry Research 18, no. 1 (2008): 20–30. http://dx.doi.org/10.1007/s00044-008-9103-6.

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49

Du-Cuny, Lei, Jörg Huwyler, Michael Wiese, and Manfred Kansy. "Computational aqueous solubility prediction for drug-like compounds in congeneric series." European Journal of Medicinal Chemistry 43, no. 3 (2008): 501–12. http://dx.doi.org/10.1016/j.ejmech.2007.04.009.

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50

Stanchev, Stancho, Georgi Momekov, Frank Jensen, and Ilia Manolov. "Synthesis, computational study and cytotoxic activity of new 4-hydroxycoumarin derivatives." European Journal of Medicinal Chemistry 43, no. 4 (2008): 694–706. http://dx.doi.org/10.1016/j.ejmech.2007.05.005.

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