Growing Science » Current Chemistry Letters » Integrated computer aided methods to designing potent α-Glucosidase inhibitors based on quinoline scaffold derivative

Journals


CCL Volumes


Current Chemistry Letters

ISSN 1927-730x (Online) - ISSN 1927-7296 (Print)
Quarterly Publication
Volume 14 Issue 1 pp. 79-106 , 2025

Integrated computer aided methods to designing potent α-Glucosidase inhibitors based on quinoline scaffold derivative Pages 79-106 Right click to download the paper Download PDF

Authors: Ayoub Khaldan, Soukaina Bouamrane, Reda El-Mernissi, Marwa Alaqarbeh, Hamid Maghat, Mohammed Bouachrine, Tahar Lakhlifi, Abdelouahid Sbai

DOI: 10.5267/j.ccl.2024.9.003

Keywords:

Abstract: Diabetes mellitus is a serious health disease that affects people all over the world. The number of persons identified with diabetes mellitus rises each year. α -Glucosidase is a digestive enzyme used to control diabetes mellitus. The searching for new potent α-glucosidase inhibitors capable of delaying carbohydrate digestion in the human body is an important strategy towards control of diabetes mellitus. In this work, a series of quinoline-based Schiff base derivatives already identified as α-glucosidase inhibitory activity was studied by using 2D/3D-QSAR approach. The best HQSAR/A-B-C-H-Ch-DA and CoMSIA/SEDA models were constructed using thirteen molecules in the training set, resulting in favorable values of Q2 (0.834 and 0.607), and high values of R2 (0.985 and 0.912), respectively. The generated HQSAR/A-B-C-H-Ch-DA and CoMSIA/SEDA contour plots were precious for designing and enhancing the α-glucosidase inhibitory activity of quinoline-based Schiff base molecules. Considering these results, two novel α-glucosidase compounds were designed to possess significant activity. The newly suggested molecules showed good outcomes in the preliminary in silico ADME/Tox evaluations. Molecular docking results revealed that the new designed inhibitors have a good stability in the active pocket of the studied receptor compared to voglibose, clinically used as an α-glucosidase inhibitor. MD simulation and MM-GBSA results confirmed the molecular docking outcomes. Finally, DFT analysis was useful in determining the most electrophilic and nucleophilic centers of the two designed α-glucosidase inhibitors.



How to cite this paper
Khaldan, A., Bouamrane, S., El-Mernissi, R., Alaqarbeh, M., Maghat, H., Bouachrine, M., Lakhlifi, T & Sbai, A. (2025). Integrated computer aided methods to designing potent α-Glucosidase inhibitors based on quinoline scaffold derivative.Current Chemistry Letters, 14(1), 79-106.

Refrences

1. Khan, I., W. Rehman, W., Rahim, F., Hussain, R., Khan, S., Rasheed, L., Alanazi, A.S., Hefnawy, M., Alanazi, M. M., Shah, S. A. A., Taha, M. (2023) Synthesis, in vitro biological analysis and molecular docking studies of new thiadiazole-based thiourea derivatives as dual inhibitors of α-amylase and α-glucosidase. Arabian Journal of Chemistry, 16 (9), 105078.
2. Khaldan, A., Bouamrane, S., El-mernissi, R., Ouabane, M., Alaqarbeh, M., Maghat, H., Ajana, M. A., Sekkat, C., Bouachrine, M., Lakhlifi, T., Sbai, A. (2024) Design of new α-glucosidase inhibitors through a combination of 3D-QSAR, ADMET screening, molecular docking, molecular dynamics simulations and quantum studies. Arabian Journal of Chemistry, 17(3) 105656
3. Mohamed Farhan, H., Nassar, M., Hassan Ahmed, M., Abougabal, K. Abd Elazim Taha, N. (2022) An association between the sarcolemmal membrane-associated protein gene and microvascular endothelial diabetic retinopathy in patients with type 2 diabetes mellitus: A preliminary case control study, Diabetes Metabolic Syndrome. 16(11), 102653.
4. Taha, M., Rahim, F., Zaman, K., Selvaraj, M., Uddin, N., Farooq, R.K., Nawaz, M., Sajid, M., Nawaz, F., Ibrahim, M., Khan, K.M. (2019) Synthesis, α-glycosidase inhibitory potential and molecular docking study of benzimidazole derivatives. Bioorganic Chemistry. 95, 103555.
5. Whiting, D.R., Guariguata, L., Weil, C., Shaw, J. (2011) IDF diabetes atlas: global estimates of the prevalence of diabetes for 2011 and 2030. Diabetes Research and Clinical Practice. 94(3), 311–321.
6. Tang, P.C., Lin, Z. G., Wang, Y. (2010) Design and synthesis of DPP-4 inhibitor for the treatment of type 2 diabetes. Chinese Chemical Letters. 21, 253–256.
7. Taha, M., Alshamrani, F.J., Rahim, F., Hayat, S., Ullah, H., Zaman, K., Imran, S., Khan, K.M., Naz, F. (2019) Synthesis of Novel Triazinoindole-Based Thiourea Hybrid: A Study on α-Glucosidase Inhibitors and Their Molecular Docking. Molecules. 24(21), 3819.
8. Khaldan, A., Bouamrane, S., El-Mernissi, R., Maghat, H., Ajana, M. A., Sbai, A., Bouachrine, M., Lakhlifi, T. (2022) In silico design of new α-glucosidase inhibitors through 3D-QSAR study, molecular docking modeling and ADMET analysis. Moroccan Journal of Chemistry. 10(1), 22-36.
9. Yeye, E. O., Kanwal, Mohammed Khan, K., Chigurupati, S., Wadood, A., Ur Rehman, A., Perveen, S., Kannan Maharajan, M., Shamim, S., Hameed, S., Aboaba, S.A., Taha, M. (2020) Syntheses, in vitro α-amylase and α-glucosidase dual inhibitory activities of 4-amino-1,2,4-triazole derivatives their molecular docking and kinetic studies. Bioorganic and Medicinal Chemistry. 28, 115467.
10. Taha, M., Rahim, F., Zaman, K., Selvaraj, M., Uddin, N., Farooq, R.K., Nawaz, M., Sajid, M., Nawaz, F., Ibrahim, M., Khan, K.M. (2019) Synthesis, α-glycosidase inhibitory potential and molecular docking study of benzimidazole derivatives. Bioorganic Chemistry. 95, 103555.
11. Lefebvre, P., Scheen, A. (1994) The use of acarbose in the prevention and treatment of hypoglycaemia. European Journal of Clinical Investigation. 3, 40–44.
12. Scott, L. J., Spencer, C.M. (2000) Miglitol: a review of its therapeutic potential in type 2 diabetes mellitus. Drugs, 59(3): 521–549.
13. Dong Y., Sui, L., Yang, F., Ren, X., Xing, Y., Xiu, Z. (2022) Reducing the intestinal side effects of acarbose by baicalein through the regulation of gut microbiota: An in vitro study. Food Chemistry, 394, 133561.
14. Chavda, V., Patel, S. (2022) Voglibose and saxagliptin ameliorate the post-surgical stress and cognitive dysfunction in chronic anaesthesia exposed diabetic MCAo induced ischemic rats. IBRO Neuroscience Reports. 13, 426-435.
15. Noori, M., Rastak, M., Halimi, M., Ghomi, M. K., Mollazadeh, M., Mohammadi-Khanaposhtani, M., Sayahi, M. H., Rezaei, Z., Mojtabavi, S., Ali Faramarzi, M., Larijani, B., Biglar, M., Amanlou, M., Mahdavi, M. (2022) Design, synthesis, in vitro, and in silico enzymatic evaluations of thieno[2,3-b]quinoline-hydrazones as novel inhibitors for α-glucosidase. Bioorganic Chemistry. 127, 105996.
16. Khaldan, A., Bouamrane, S., El-mernissi, R., El Mchichi, L., Maghat, H., Bouachrine, M., Lakhlifi, T., Sbai, A. (2022). In search of new potent α-glucosidase inhibitors: molecular docking and ADMET prediction. Moroccan Journal of Chemistry. 10 (4), 772-786.
17. Foley, M., Tilley, L. (1998) Quinoline antimalarials: mechanisms of action and resistance and prospects for new agents. Pharmacology Therapeutics. 79 (1), 55-87.
18. Chen, Y., Hsien-Ming, H., Lu, C., Li, K., Tzeng, C. (2004) Synthesis and anticancer evaluation of certain indolo[2,3-b]quinoline derivatives. Bioorganic Medicinal Chemistry. 12(24), 6539-6546.
19. Bekhit, A. A., El-Sayed, O. A., Aboulmagd, E., Park, J. Y. (2004) Tetrazolo[1,5-a]quinoline as a potential promising new scaffold for the synthesis of novel anti-inflammatory and antibacterial agents. European Journal of Medicinal Chemistry. 39 (3), 249-255.
20. Taha, M., Ismail, N. H., Imran, S., Wadood, A., Fazal Rahim, F., Ali, M., Ur Rehman, A. (2015) Novel Quinoline Derivatives as Potent In Vitro α-Glucosidase Inhibitors: In Silico Studies and SAR Predictions. MedChemComm, 6(10), 1826-1836.
21. Khaldan, A., Bouamrane, S., El-mernissi, R., Maghat, H., Ajana, M. A., Sbai, A., Bouachrine, M., Lakhlifi, T. (2021) Identification of potential α-glucosidase inhibitors: 3D-QSAR modeling, molecular docking approach. Rhazes: Green and applied chemistry. 12, 60-75
22. Khaldan, A., Bouamrane, S., El-mernissi, R., Maghat, H., Ajana, M.A., Sbai, A., Bouachrine, M. Lakhlifi, T. (2021) 3D-QSAR modeling, molecular docking and ADMET properties of benzothiazole derivatives as a-glucosidase inhibitors. Material Today: Preceding. 45(8), 7643- 7652.
23. Vilar, S., Cozza, G. & Moro, S. (2008) Medicinal chemistry and the molecular operating environment (MOE): application of QSAR and molecular docking to drug discovery. Current Topics in Medicinal Chemistry, 8(18), 1555–1572.
24. Khaldan, A., El khatabi, K., El-mernissi, R., Sbai, A., Bouachrine, M., Lakhlifi, T. (2020) Combined 3D-QSAR Modeling and Molecular Docking Study on metronidazole-triazole-styryl hybrids as antiamoebic activity. Moroccan Journal of Chemistry. 8(1), 527-539.
25. El Khatabi, K., Aanouz, I., EL-Mernissi, R., Khaldan, A., Ajana, M.A., Bouachrine, M., Lakhlifi, T. (2020) Design of Novel Benzimidazole Derivatives as Potential α-amylase Inhibitors by 3D-QSAR Modeling and Molecular Docking Studies. Journal of the Turkish Chemical Society Section A: Chemistry. 7(2): 471-480.
26. Khaldan, A., Bouamrane, S., El-mernissi, R., Maghat, H., Ajana, M.A., Sbai, A., Bouachrine, M., Lakhlifi, T. (2022) In silico study of 2,4,5-trisubstituted thiazoles as inhibitors of tuberculosis using 3D-QSAR, molecular docking, and ADMET analysis. El-Cezerî Journal of Science and Engineering. 9(2), 452-468.
27. EL-Mernissi, R., El Khatabi, K., Khaldan, A., El Mchichi, L., Ajana, M.A., Bouachrine, M., Lakhlifi, T. (2021) Design of new 3, 5-disubstituted indole as hematological anticancer agents using 3D-QSAR, molecular docking and drug-likeness studies. Materials Today: Proceedings. 45, 7608–7614.
28. Bouamrane, S., Khaldan, A., Hajji, H., El-mernissi, R., Maghat, H., Ajana, M.A., Sbai, A., Bouachrine, M., Lakhlifi, T. (2022) 3D-QSAR, molecular docking, molecular dynamic simulation, and ADMET study of bioactive compounds against candida albicans. Moroccan Journal of Chemistry. 10(3), 523-541.
29. Ouabane, M., Tabti, K., Hajji, H., Elbouhi, M., Khaldan, A., Elkamel, K., Sbai, A., Ajana, M.A., Sekkat, C., Bouachrine, M., Lakhlifi, T. (2023) Structure-odor relationship in pyrazines and derivatives: A physicochemical study using 3D-QSPR, HQSPR, Monte Carlo, molecular docking, ADME-Tox and molecular dynamics. Arabian Journal of Chemistry. 2023, 16, 105207.
30. Khaldan, A., Agorram, A., Ghaleb, A. Aouidate, A. Sbai, A., M. Bouachrine, M., Lakhlifi, T. (2019) 3D QSAR Modeling and Molecular Docking Studies on a series of quinolone-triazole derivatives as antibacterial agents. Rhazes: Green and Applied Chemistry. 6, 11-26.
31. Klebe, G., Abraham, U. & Mietzner, T. 1994. Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity. Journal of Medicinal Chemistry, 37(24): 4130–4146.
32. Khaldan, A., Bouamrane, S., El-Mernissi, R., El Khatabi, K., Aanouz, I., Aggoram, A., Sbai, A., Bouachrine, M., Lakhlifi, T. (2021) QSAR study of α-Glucosidase inhibitors for benzimidazole bearing bis-Schiff bases using CoMFA, CoMSIA, and molecular docking. International Journal of Quantitative Structure-Property Relationships. 6(1), 9–24.
33. Khaldan, A., Bouamrane, S., El-mernissi, R., Maghat, H., Sbai, A., M. Bouachrine, M., Lakhlifi, T. (2023) Molecular docking, ADMET prediction and quantum computational on 2-methoxy benzoyl hydrazone compounds as potential antileishmanial inhibitors. Biointerface Research in Applied Chemistry. 4, 302.
34. El-Mernissi, R., Khaldan, A., ElMchichi, L., Ajana, M.A., Lakhlifi, T., Bouachrine, M. (2022) 3D-QSAR, ADMET, and molecular docking studies for designing new 1, 3, 5-triazine derivatives as anticancer agents. Egyptian Journal of Chemistry. 65(132), 9-18.
35. Pires, D.E.V., Blundell, T.L., Ascher, D.B. (2015) pkCSM: Predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. Journal of Medicinal Chemistry. 58(9), 4066–4072.
36. Daina, A., Michielin, O., Zoete, V. (2017). SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Scientific Reports. 7, 42717.
37. EL-Mernissi, R., El Khatabi, K., Khaldan, A., Bouamrane, S., El Mchichi, L., Ajana, M.A., Bouachrine, M., Lakhlifi T. (2021) 3D-QSAR, ADMET and Docking Studies for design new 5,5-Diphenylimidazolidine-2,4-dione derivatives agents against cervical cancer. Orbital: Electron Journal of Chemistry. 14(1), 24-32.
38. Veber, D.F., Johnson, S.R., Cheng, H.Y., Smith, B.R., Ward, K.W., Kopple, K.D. (2002) Molecular properties that influence the oral bioavailability of drug candidates. Journal of Medicinal Chemistry. 45(12), 2615-2623.
39. Khaldan, A., Bouamrane, S., El-mernissi, R., Alaqarbeh, M., Alsakhen, N., Maghat, H., Ajana, M.A., Sbai, A., Bouachrine, M., Lakhlifi, T. (2022) Computational study of quinoline-based thiadiazole compounds as potential antileishmanial inhibitors. New Journal of Chemistry. 46, 17554.
40. Khaldan, A., Bouamrane, S., En-Nahli, F., El-Mernissi, R., El Khatabi, K., Hmamouchi, R., Maghat, H., Ajana, M. A, Sbai, A., Bouachrine, M., Lakhlifi, T. (2021) Prediction of potential inhibitors of SARS-CoV-2 using 3D-QSAR, molecular docking modeling and ADMET properties. Heliyon, 7, e06603.
41. Sultana, S., Hossain, Md. A., Islam, Md. M., Kawsar, S. M. A. (2024) Antifungal potential of mannopyranoside derivatives through computational and molecular docking studies against Candida albicans 1IYL and 1AI9 proteins. Current Chemistry Letters. 13(1), 1-14.
42. Anees Pangal, Pranav Tambe, Khursheed Ahmed. (2023) Screening of 3-acetylcoumarin derivatives as multifunctional biological agents. Current Chemistry Letters. 12(2), 343-352
43. Šrejber, M., Navrátilová, V., Paloncýová, M., Bazgier, V., Berka, K., Anzenbacher, P. & Otyepka, M. 2018. Membrane-attached mammalian cytochromes P450: an overview of the membrane’s effects on structure, drug binding, and interactions with redox partners. J. Inorg. Biochem. 183: 117–136.
44. EL-Mernissi, R., Khaldan, A., Bouamrane, S., Rehman, H.M., Alaqarbeh, M. Ajana, M.A. Lakhlifi, T., Bouachrine, M. (2024) 3D-QSAR, molecular docking, simulation dynamic and ADMET studies on new quinolines derivatives against colorectal carcinoma activity, Biomolecular Structure and Dynamics. 42(7), 3682-3699.
45. Ortiz, C.L.D., Completo, G. C., Nacario, R. C., Nellas, R. B. (2019) Potential Inhibitors of Galactofuranosyltransferase 2 (GlfT2): Molecular Docking, 3D-QSAR, and In Silico ADMETox Studies. Scientific Report. 9, 17096.
46. Kramer, B., Rarey, M., Lengauer, T. (1999) Evaluation of the FLEXX incremental construction algorithm for protein-ligand docking. Proteins, 37(2), 228–241.
47. Al-horaibi, S. A., Alghamdi, M.T., Gaikwad, S.T., Rajbhoj, A. S. (2018) Comparison and Determine Characteristics Potentials of HOMO/ LUMO and Relationship between Ea and Ip for Squaraine Dyes (SQ1, SQ2) by Using Cyclic Voltammetry and DFT/TD-DFT, Moroccan Journal of Chemistry. 6(3), 404-4013.
48. Jaramillo, P., Domingo, L.R., Chamorro, E., Pérez, P., (2008) A further exploration of a nucleophilicity index based on the gas-phase ionization potentials. Journal of Molecular Structure: Theochem, 865(1-3), 68–72.
49. Domingo, L.R.; Aurell, M.J.; Pérez, P.; Contreras, R. (2002) Quantitative characterization of the local electrophilicity of organic molecules. Understanding the regioselectivity on Diels-Alder reactions. J. Phys. Chem. A 106, 6871–6875.
50. Azaid, A., Abram, T., Alaqarbeh, M., Raftani, M., Kacimi, R., Sbai, A., Lakhlifi, T., Bouachrine, M. (2023) Design new organic material based on triphenylamine (TPA) with D-π-A-π-D structure used as an electron donor for organic solar cells: A DFT approach, Journal of Molecular Graphics and Modelling, 122, 108470..
51. Taha, M., Sultan, S., Imran, S., Rahim, F., Zaman, K., Wadood , A., Ur Rehman, A., Uddin, N. Khan, K.M. (2019) Synthesis of quinoline derivatives as diabetic II inhibitors and molecular docking studies. Bioorganic Medicinal Chemistry. 27(18), 1081-4088.
52. Sybyl 8.1; Tripos Inc.: St. Louis, MO, USA, 2008; Available online: http://www.tripos.com (accessed on 26 January 2011).
53. Clark, M., Cramer, R.D. & Van Opdenbosch, N. 1989. Validation of the general purpose tripos 5.2 force field. Journal of Computational Chemistry. 10, 982–1012.
54. Sainy, J., Sharma, R. (2015) QSAR analysis of thiolactone derivatives using HQSAR, CoMFA and CoMSIA. SAR. QSAR. Environmental Research. 26, 873-892.
55. Waller, C.L. (2004) A Comparative QSAR Study Using CoMFA, HQSAR, and FRED/SKEYS Paradigms for Estrogen Receptor Binding Affinities of Structurally Diverse Compounds. Journal of chemical information and computer sciences. 44, 758-765.
56. Jiao, L., Zhang, X., Qin, Y., Wang, X., Li, H. (2016) Hologram QSAR study on the electrophoretic mobility of aromatic acids. Chemometrics and Intelligent Laboratory Systems. 157, 202-207.
57. Wold, S. (1991). Validation of QSAR’s. Quantitative Structure Activity Relationships, 10(3), 191–193.
58. Golbraikh, A., Tropsha, A. (2002) Beware of q2!. Journal of Molecular Graphics and Modelling, 20, 269–276.
59. Roy, K., (2007) On some aspects of validation of predictive quantitative structure–activity relationship models. Expert Opinion on Drug Discovery, 2, 1567–1577.
60. Bouamrane, S., Khaldan, A., Hajji, H., El-mernissi, R., Alaqarbeh, M., Alsakhen, N., Maghat, H., Ajana, M.A., Sbai, A., Bouachrine, M., Lakhlif, T. (2023) In silico identification of 1,2,4-triazoles as potential Candida Albicans inhibitors using 3D-QSAR, molecular docking, molecular dynamics simulations, and ADMET profiling. Molecular Diversity. 27(5), 2111-2132
61. Bouamrane, S., Khaldan, A., Alaqarbeh, M., Sbai, A., Ajana, M.A., Lakhlifi, T., Bouachrine, M. (2024) Computational integration for antifungal 1,2,4-triazole inhibitors design: QSAR, molecular docking, molecular dynamics simulations, ADME/Tox, and retrosynthesis studies. Chemical Physics Impact. 8,100502.
62. Rahim, F., Ullah, H., Javid, M. T., Wadood, A., Taha, M., Ashraf, M., Shaukat, A., Junaid, M., Hussain, S., Rehman, W., Mehmood, R., Sajid, M., Khan, M. N., Khan, K.M., 2015. Synthesis, in vitro evaluation and molecular docking studies of thiazole derivatives as new inhibitors of α-glucosidase. Bioorganic Chemistry, 62, 15-21.
63. Trott, O., Olson, A.J. (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of Computational Chemistry. 3(2), 455–461.
64. Hamaamin Hussen, N., Hameed Hasan, A., Jamalis J., Shakya, S., Chander, S., Kharkwal, H., Murugesan, S., Ajit Bastikar, V., Pyarelal Gupta, P. (2022) Potential inhibitory activity of phytoconstituents against black fungus: In silico ADMET, molecular docking and MD simulation studies. Comput Toxicol, 24, 100247.
65. Dassault Systèmes BIOVIA. (2016). Discovery studio modeling environment, release 2017, San Diego: Dassault Systèmes. [WWW document], 2016. http://accelrys.com/products/collaborativescience/biovia-discovery-studio/. Accessed 25 Feb 17.
66. DeLano,W. (2002). The PyMOL Molecular Graphics System DeLano Scientific, Palo Alto, CA, USA, 2002. http://www.pymol.org. (Accessed 25 February 2017).
67. Jo, S., Kim, T., Iyer, V.G., Im, W. (2008) CHARMM-GUI: a web-based graphical user interface for CHARMM. Journal of Computational Chemistry, 29(11), 1859-1865.
68. Lee, J., Cheng, X., Swails, J.M., Yeom, M.S., Eastman, P.K., Lemkul, J.A, Wei, S., Buckner, J., Jeong, J. C., Qi, Y., Jo, S., Pande, V.S., Case, D.A., Brooks 3rd, C.L., MacKerell Jr, A.D., Klauda, J.B., Im, W., 2016. CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM Simulations Using the CHARMM36 Additive Force Field. Journal of Chemical Theory and Computation. 12(1), 405-413.
69. Best, R.B., Zhu, X., Shim, J., Lopes, P.E., Mittal, J., Feig, M., Mackerell Jr, A.D. (2012) Optimization of the additive CHARMM all-atom protein force field targeting improved sampling of the backbone φ, ψ and side-chain χ(1) and χ(2) dihedral angles . Journal of Chemical Theory and Computation. 8(9), 3257-3273.
70. Yu, W., He, X., Vanommeslaeghe, K., MacKerell Jr, A.D., (2012) Extension of the CHARMM General Force Field to sulfonyl-containing compounds and its utility in biomolecular simulations. Journal of Computational Chemistry. 33(31), 2451-68.
71. Jorgensen, W.L., Chandrasekhar, J., Madura, J.D. (1983) Comparison of simple potential functions for simulating liquid water. The Journal of Chemical Physics. 79, 926-935.
72. Darden, T., York, D., Pedersen, L. (1993) Particle mesh Ewald: An N⋅log(N) method for Ewald sums in large systems. The Journal of Chemical Physics, 98, 10089-10092.
73. Frisch, M. (2009) GAUSSIAN 09. Revision E. 01, Gaussian Inc.
74. Koopmans, T., (1934) Über die Zuordnung von Wellenfunktionen und Eigenwerten zu deninzelnen Elektronen Eines atoms, Physica, 1, 104–113
75. Domingo, L. R., Perez, P., Saez, J. A. (2013). Understanding the local
reactivity in polar organic reactions through electrophilic and nucleophilic Parr functions. RSC Advances., 3(5), 1486–1494.

Journal: Current Chemistry Letters | Year: 2025 | Volume: 14 | Issue: 1 | Views: 3 | Reviews: 0

Related Articles:

Add Reviews

Name:*
E-Mail:
Review:
Bold Italic Underline Strike | Align left Center Align right | Insert smilies Insert link URLInsert protected URL Select color | Add Hidden Text Insert Quote Convert selected text from selection to Cyrillic (Russian) alphabet Insert spoiler
Security Code: *

® 2010-2024 GrowingScience.Com