Journal of Polymer and Biopolymer Physics Chemistry
ISSN (Print): 2373-3403 ISSN (Online): 2373-3411 Website: https://www.sciepub.com/journal/jpbpc Editor-in-chief: Martin Alberto Masuelli
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Journal of Polymer and Biopolymer Physics Chemistry. 2026, 14(1), 1-14
DOI: 10.12691/jpbpc-14-1-1
Open AccessArticle

Computational Evaluation of a Novel Nitrothiophene-Derived Schiff Base Ligand Against Breast Cancer-Associated Proteins and Andes Hantavirus Nucleoprotein: Molecular Docking and ADMET Analysis

Temitayo O. Aiyelabola1, , Sayantan Pradhan2, and Takashiro Akitsu3

1Department of Chemistry, Faculty of Science, Obafemi Awolowo University, Ile-Ife, Osun State, Nigeria

2Chemical Science Division, Saha Institute of Nuclear Physics, Kolkata, West Bengal, India

3Department of Chemistry, Faculty of Science, Tokyo University of Science, 1-3 Kagurazaka, Shinjuku-ku, Tokyo 162-8601, Japan

Pub. Date: October 16, 2026

Cite this paper:
Temitayo O. Aiyelabola, Sayantan Pradhan and Takashiro Akitsu. Computational Evaluation of a Novel Nitrothiophene-Derived Schiff Base Ligand Against Breast Cancer-Associated Proteins and Andes Hantavirus Nucleoprotein: Molecular Docking and ADMET Analysis. Journal of Polymer and Biopolymer Physics Chemistry. 2026; 14(1):1-14. doi: 10.12691/jpbpc-14-1-1

Abstract

The identification of small molecules capable of interacting with multiple therapeutically relevant protein targets is an important strategy in early-stage drug discovery. In the present study, the Schiff base ligand ((N1E,N8E)-N1,N8-bis((5-nitrothiophen-2-yl)methylene)naphthalene-1,8-diamine; N1) was computationally evaluated against a panel of representative disease-associated proteins to investigate its target-binding profile. The primary focus was the assessment of N1 against breast cancer-associated receptor kinases, including EGFR, HER2, VEGFR2, and PI3K, which play central roles in oncogenic signaling pathways. In addition, the Andes hantavirus nucleoprotein was included as an exploratory antiviral target to examine the broader target recognition capability of the ligand rather than to propose a common therapeutic mechanism. Molecular docking studies were performed using AutoDock Vina with experimentally determined protein structures obtained from the Protein Data Bank. N1 demonstrated favorable binding affinities toward all investigated breast cancer targets, with docking scores of −8.708 kcal/mol for EGFR, −8.836 kcal/mol for HER2, −8.906 kcal/mol for VEGFR2, and −9.066 kcal/mol for PI3K. The ligand also exhibited a docking score of −8.8 kcal/mol against the Andes hantavirus nucleoprotein. Interaction analysis revealed multiple stabilizing hydrogen bonds, hydrophobic contacts, electrostatic interactions, and π-mediated interactions within the respective binding pockets.Drug-likeness assessment indicated compliance with Lipinski's and Ghose's rules, while ADMET prediction suggested favorable absorption and membrane permeability. Nevertheless, the predicted risks of hepatotoxicity, cardiotoxicity, and mutagenicity indicate that further structural optimization and experimental validation are required. Overall, this study demonstrates the potential of N1 as a promising scaffold for breast cancer-associated kinase inhibition while providing preliminary evidence of its broader protein-binding capability across biologically distinct targets.

Keywords:
Breast cancer Hantavirus Molecular docking Drug-likeness ADMET Schiff base

Creative CommonsThis work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References:

[1]  Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021; 71(3): 209–249.
 
[2]  De Clercq E, Li G. Approved antiviral drugs over the past 50 years. Clin Microbiol Rev. 2016; 29(3): 695–747.
 
[3]  Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2024. CA Cancer J Clin. 2024; 74(1): 12–49.
 
[4]  Yarden Y, Pines G. The ERBB network: at last, cancer therapy meets systems biology. Nat Rev Cancer. 2012; 12(8): 553–563.
 
[5]  Roskoski R Jr. The ErbB/HER family of protein-tyrosine kinases and cancer. Pharmacol Res. 2014; 79: 34–74.
 
[6]  Porta C, Paglino C, Mosca A. Targeting PI3K/Akt/mTOR signaling in cancer. Front Oncol. 2014; 4: 64.
 
[7]  Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011; 144(5): 646–674.
 
[8]  Normanno N, De Luca A, Bianco C, et al. Epidermal growth factor receptor (EGFR) signaling in cancer. Gene. 2006; 366(1): 2–16.
 
[9]  Slamon DJ, Clark GM, Wong SG, Levin WJ, Ullrich A, McGuire WL. Human breast cancer: correlation of relapse and survival with amplification of the HER-2/neu oncogene. Science. 1987; 235(4785): 177–182.
 
[10]  Ferrara N, Kerbel RS. Angiogenesis as a therapeutic target. Nature. 2005; 438(7070): 967– 974.
 
[11]  Fresno Vara JA, Casado E, de Castro J, Cejas P, Belda-Iniesta C, González-Barón M. PI3K/Akt signalling pathway and cancer. Cancer Treat Rev. 2004; 30(2): 193–204.
 
[12]  Jonsson CB, Figueiredo LT, Vapalahti O. A global perspective on hantavirus ecology, epidemiology, and disease. Clin Microbiol Rev. 2010; 23(2): 412–441.
 
[13]  Muyangwa M, Martynova EV, Khaiboullina SF, Morzunov SP, Rizvanov AA. Hantaviral proteins: structure, functions, and role in hantavirus infection. Front Microbiol. 2015; 6: 1326.
 
[14]  Brocato RL, Hooper JW. Progress on the prevention and treatment of hantavirus disease. Viruses. 2019; 11(7): 610.
 
[15]  da Silva CM, da Silva DL, Modolo LV, et al. Schiff bases: a short review of their antimicrobial activities. J Adv Res. 2011; 2(1): 1–8.
 
[16]  Mishra AP, Soni M, Verma S. Schiff bases and their metal complexes: a review on biological importance. J Chem Pharm Res. 2015; 7(9): 668–684.
 
[17]  Nepali K, Lee HY, Liou JP. Nitro-group-containing drugs. J Med Chem. 2019; 62(6): 2851–2893.
 
[18]  Lionta E, Spyrou G, Vassilatis DK, Cournia Z. Structure-based virtual screening for drug discovery: principles, applications and recent advances. Curr Top Med Chem. 2014; 14(16): 1923–1938.
 
[19]  Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ. AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem. 2009; 30(16): 2785–2791.
 
[20]  Hanwell MD, Curtis DE, Lonie DC, Vandermeersch T, Zurek E, Hutchison GR. Avogadro: an advanced semantic chemical editor, visualization, and analysis platform. J Cheminform. 2012; 4: 17.
 
[21]  Halgren TA. Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94. J Comput Chem. 1996; 17(5–6): 490–519.
 
[22]  Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem. 2010; 31(2): 455–461.
 
[23]  Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ. AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem. 2009; 30(16): 2785–2791.
 
[24]  Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev. 2001; 46(1–3): 3–26.
 
[25]  Ghose AK, Viswanadhan VN, Wendoloski JJ. A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. J Comb Chem. 1999; 1(1): 55–68.
 
[26]  Di L, Kerns EH. Drug-like Properties: Concepts, Structure Design and Methods from ADME to Toxicity Optimization. Academic Press; 2015.
 
[27]  Gleeson MP, Hersey A, Hannongbua S. In-silico ADME models: a general assessment of their utility in drug discovery applications. Curr Top Med Chem. 2011; 11(4): 358– 381.
 
[28]  Van de Waterbeemd H, Gifford E. ADMET in silico modelling: towards prediction paradise? Nat Rev Drug Discov. 2003; 2(3): 192–204.
 
[29]  Cheng F, Li W, Zhou Y, Shen J, Wu Z, Liu G, Lee PW, Tang Y. admetSAR: a comprehensive source and free tool for assessment of chemical ADMET properties. J Chem Inf Model. 2012; 52(11): 3099–3105.
 
[30]  Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev. 2001; 46(1–3): 3–26.
 
[31]  Pavia G, Lampman GM, Kriz GS. Introduction to Spectroscopy: A Guide for Students of Organic Chemistry. 3rd ed. Pacific Grove, CA: Brooks and Cole; 2001. p. 22–368.
 
[32]  Kemp W. Organic Spectroscopy. 3rd ed. Hong Kong: Macmillan; 1999. p. 19–98.
 
[33]  Nakamoto K. Infrared and Raman Spectroscopy of Inorganic and Coordination Compounds: Applications in Coordination, Organometallics and Bioinorganic Chemistry. 6th ed. New York: John Wiley and Sons; 2009. p. 67–69.
 
[34]  Artursson P, Karlsson J. Correlation between oral drug absorption in humans and apparent drug permeability coefficients in human intestinal epithelial (Caco-2) cells. Biochem Biophys Res Commun. 1991; 175(3): 880–885.
 
[35]  Smith DA, Di L, Kerns EH. The effect of plasma protein binding on in vivo efficacy: misconceptions in drug discovery. Nat Rev Drug Discov. 2010; 9(12): 929–939.
 
[36]  Gottesman MM, Pastan I. The role of multidrug resistance efflux pumps in cancer: revisiting a JNCI publication exploring expression of the MDR1 (P-glycoprotein) gene. J Natl Cancer Inst. 2015; 107(9): djv222.
 
[37]  Guengerich FP. Cytochrome P450 and chemical toxicology. Chem Res Toxicol. 2008; 21(1): 70–83.
 
[38]  Sanguinetti MC, Tristani-Firouzi M. hERG potassium channels and cardiac arrhythmia. Nature. 2006; 440(7083): 463–469.
 
[39]  Diamanti-Kandarakis E, Bourguignon JP, Giudice LC, et al. Endocrine-disrupting chemicals: an Endocrine Society scientific statement. Endocr Rev. 2009; 30(4): 293– 342.