1. Rezaee R, Hosseinzadeh H. Safranal: from an aromatic natural product to a rewarding pharmacological agent. Iranian journal of basic medical sciences. 2013;16(1):12.

2. Heidarbeigi K, Mohtasebi SS, Foroughirad A, Ghasemi-Varnamkhasti M, Rafiee S, Rezaei K. Detection of adulteration in saffron samples using electronic nose. International Journal of Food Properties. 2015;18(7):1391-401. [DOI:10.1080/10942912.2014.915850]

3. Hong KL, Sooter LJ. Single‐stranded DNA aptamers against pathogens and toxins: identification and biosensing applications. BioMed research international. 2015;2015(1):419318. [DOI:10.1155/2015/419318]

4. Kim S-H, Choi J-W, Kim A-R, Lee S-C, Yoon M-Y. Development of ssDNA aptamers for diagnosis and inhibition of the highly pathogenic avian influenza virus subtype H5N1. Biomolecules. 2020;10(8):1116. [DOI:10.3390/biom10081116]

5. Sun J, Zhang M, Gao Q, Shao B. Screening biotoxin aptamer and their application of optical aptasensor in food stuff: a review. Frontiers in Chemistry. 2024;12:1425774. [DOI:10.3389/fchem.2024.1425774]

6. Zahraee H, Mehrzad A, Abnous K, Chen C-H, Khoshbin Z, Verdian A. Recent advances in aptasensing strategies for monitoring phycotoxins: promising for food safety. Biosensors. 2022;13(1):56. [DOI:10.3390/bios13010056]

7. Yoo H, Jo H, Oh SS. Detection and beyond: Challenges and advances in aptamer-based biosensors. Materials Advances. 2020;1(8):2663-87. [DOI:10.1039/D0MA00639D]

8. Liang G, Song L, Gao Y, Wu K, Guo R, Chen R, et al. Aptamer sensors for the detection of antibiotic residues-A mini-review. Toxics. 2023;11(6):513. [DOI:10.3390/toxics11060513]

9. Shaban SM, Kim D-H. Recent advances in aptamer sensors. Sensors. 2021;21(3):979. [DOI:10.3390/s21030979]

10. Jeddi I, Saiz L. Three-dimensional modeling of single stranded DNA hairpins for aptamer-based biosensors. Scientific reports. 2017;7(1):1178. [DOI:10.1038/s41598-017-01348-5]

11. Morris GM, Goodsell DS, Halliday RS, Huey R, Hart WE, Belew RK, et al. Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. Journal of computational chemistry. 1998;19(14):1639-62. https://doi.org/10.1002/(SICI)1096-987X(19981115)19:14<1639::AID-JCC10>3.0.CO;2-B [DOI:10.1002/(SICI)1096-987X(19981115)19:143.0.CO;2-B]

12. Douaki A, Garoli D, Inam AS, Angeli MAC, Cantarella G, Rocchia W, et al. Smart approach for the design of highly selective aptamer-based biosensors. Biosensors. 2022;12(8):574. [DOI:10.3390/bios12080574]

13. Khoshbin Z, Housaindokht MR, Izadyar M, Bozorgmehr MR, Verdian A. Recent advances in computational methods for biosensor design. Biotechnology and Bioengineering. 2021;118(2):555-78. [DOI:10.1002/bit.27618]

14. Oliveira R, Pinho E, Sousa AL, Dias Ó, Azevedo NF, Almeida C. Modelling aptamers with nucleic acid mimics (NAM): From sequence to three-dimensional docking. Plos one. 2022;17(3):e0264701. [DOI:10.1371/journal.pone.0264701]

15. Fadeev M, O'Hagan MP, Biniuri Y, Willner I. Aptamer-Protein Structures Guide In Silico and Experimental Discovery of Aptamer-Short Peptide Recognition Complexes or Aptamer-Amino Acid Cluster Complexes. The Journal of Physical Chemistry B. 2022;126(44):8931-9. [DOI:10.1021/acs.jpcb.2c05624]

16. Park D-Y, Shin W-R, Kim SY, Nguyen Q-T, Lee J-P, Kim D-Y, et al. In silico molecular docking validation of procalcitonin-binding aptamer and sepsis diagnosis. Molecular & Cellular Toxicology. 2023;19(4):843-55. [DOI:10.1007/s13273-023-00384-9]

17. Lee SJ, Cho J, Lee B-H, Hwang D, Park J-W. Design and prediction of aptamers assisted by in silico methods. Biomedicines. 2023;11(2):356. [DOI:10.3390/biomedicines11020356]

18. Nguyen M-D, Osborne MT, Prevot GT, Churcher ZR, Johnson PE, Simine L, et al. Truncations and in silico docking to enhance the analytical response of aptamer-based biosensors. Biosensors and Bioelectronics. 2024;265:116680. [DOI:10.1016/j.bios.2024.116680]

19. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, et al. The protein data bank. Nucleic acids research. 2000;28(1):235-42. [DOI:10.1093/nar/28.1.235]

20. Bolton EE, Chen J, Kim S, Han L, He S, Shi W, et al. PubChem3D: a new resource for scientists. Journal of cheminformatics. 2011;3:1-15. [DOI:10.1186/1758-2946-3-32]

21. Van Rossum G, Drake FL. Python/C Api Manual-Python 3: CreateSpace; 2009.

22. Pilot BP. Release 2020. Dassault Systèmes: San Diego, CA, USA. 2020.

23. Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, et al. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. Journal of computational chemistry. 2009;30(16):2785-91. [DOI:10.1002/jcc.21256]

24. Abraham MJ, Murtola T, Schulz R, Páll S, Smith JC, Hess B, et al. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX. 2015;1:19-25. [DOI:10.1016/j.softx.2015.06.001]

25. Goodsell DS, Olson AJ. Automated docking of substrates to proteins by simulated annealing. Proteins: Structure, Function, and Bioinformatics. 1990;8(3):195-202. [DOI:10.1002/prot.340080302]

26. Schüttelkopf AW, Van Aalten DM. PRODRG: a tool for high-throughput crystallography of protein-ligand complexes. Acta Crystallographica Section D: Biological Crystallography. 2004;60(8):1355-63. [DOI:10.1107/S0907444904011679]

27. Oostenbrink C, Villa A, Mark AE, Van Gunsteren WF. A biomolecular force field based on the free enthalpy of hydration and solvation: the GROMOS force‐field parameter sets 53A5 and 53A6. Journal of computational chemistry. 2004;25(13):1656-76. [DOI:10.1002/jcc.20090]

28. Wu Y, Tepper HL, Voth GA. Flexible simple point-charge water model with improved liquid-state properties. The Journal of chemical physics. 2006;124(2). [DOI:10.1063/1.2136877]

29. Bas DC, Rogers DM, Jensen JH. Very fast prediction and rationalization of pKa values for protein-ligand complexes. Proteins: Structure, Function, and Bioinformatics. 2008;73(3):765-83. [DOI:10.1002/prot.22102]

30. Zhang S, Hahn DF, Shirts MR, Voelz VA. Expanded ensemble methods can be used to accurately predict protein-ligand relative binding free energies. Journal of chemical theory and computation. 2021;17(10):6536-47. [DOI:10.1021/acs.jctc.1c00513]

31. Martoňák R, Laio A, Parrinello M. Predicting crystal structures: the Parrinello-Rahman method revisited. Physical review letters. 2003;90(7):075503. [DOI:10.1103/PhysRevLett.90.075503]

32. Toukmaji A, Sagui C, Board J, Darden T. Efficient particle-mesh Ewald based approach to fixed and induced dipolar interactions. The Journal of chemical physics. 2000;113(24):10913-27. [DOI:10.1063/1.1324708]

33. Grubmüller H, Heller H, Windemuth A, Schulten K. Generalized Verlet algorithm for efficient molecular dynamics simulations with long-range interactions. Molecular Simulation. 1991;6(1-3):121-42. [DOI:10.1080/08927029108022142]

34. Hess B, Bekker H, Berendsen HJ, Fraaije JG. LINCS: a linear constraint solver for molecular simulations. Journal of computational chemistry. 1997;18(12):1463-72. https://doi.org/10.1002/(SICI)1096-987X(199709)18:12<1463::AID-JCC4>3.0.CO;2-H [DOI:10.1002/(SICI)1096-987X(199709)18:123.0.CO;2-H]

35. Vidal-Limon A, Aguilar-Toalá JE, Liceaga AM. Integration of molecular docking analysis and molecular dynamics simulations for studying food proteins and bioactive peptides. Journal of Agricultural and Food Chemistry. 2022;70(4):934-43. [DOI:10.1021/acs.jafc.1c06110]

36. Arooj M, Shehadi I, Nassab CN, Mohamed AA. Physicochemical stability study of protein-benzoic acid complexes using molecular dynamics simulations. Amino acids. 2020;52(9):1353-62. [DOI:10.1007/s00726-020-02897-2]

37. Sharma J, Bhardwaj VK, Singh R, Rajendran V, Purohit R, Kumar S. An in-silico evaluation of different bioactive molecules of tea for their inhibition potency against non structural protein-15 of SARS-CoV-2. Food chemistry. 2021;346:128933. [DOI:10.1016/j.foodchem.2020.128933]

38. Dutta Dubey K, Kumar Tiwari R, Prasad Ojha R. Recent advances in protein− ligand interactions: Molecular dynamics simulations and binding free energy. Current Computer-Aided Drug Design. 2013;9(4):518-31. [DOI:10.2174/15734099113096660036]

39. Bosshard HR. Molecular recognition by induced fit: how fit is the concept? Physiology. 2001;16(4):171-3. [DOI:10.1152/physiologyonline.2001.16.4.171]

40. Okazaki K-i, Takada S. Dynamic energy landscape view of coupled binding and protein conformational change: induced-fit versus population-shift mechanisms. Proceedings of the National Academy of Sciences. 2008;105(32):11182-7. [DOI:10.1073/pnas.0802524105]

41. Cheatham III TE. Simulation and modeling of nucleic acid structure, dynamics and interactions. Current opinion in structural biology. 2004;14(3):360-7. [DOI:10.1016/j.sbi.2004.05.001]

42. Šponer Jí, Banáš P, Jurecka P, Zgarbová M, Kührová P, Havrila M, et al. Molecular dynamics simulations of nucleic acids. From tetranucleotides to the ribosome. The journal of physical chemistry letters. 2014;5(10):1771-82. [DOI:10.1021/jz500557y]

43. Kumar S, Nussinov R. Close‐range electrostatic interactions in proteins. ChemBioChem. 2002;3(7):604-17. https://doi.org/10.1002/1439-7633(20020703)3:7<604::AID-CBIC604>3.0.CO;2-X [DOI:10.1002/1439-7633(20020703)3:73.0.CO;2-X]

44. Nakano M, Tateishi-Karimata H, Tanaka S, Sugimoto N. Affinity of molecular ions for DNA structures is determined by solvent-accessible surface area. The Journal of Physical Chemistry B. 2014;118(32):9583-94. [DOI:10.1021/jp505107g]

45. Ropii B, Bethasari M, Anshori I, Koesoema AP, Shalannanda W, Satriawan A, et al. The molecular interaction of six single-stranded DNA aptamers to cardiac troponin I revealed by docking and molecular dynamics simulation. Plos one. 2024;19(5):e0302475. [DOI:10.1371/journal.pone.0302475]

46. Emami N, Pakchin PS, Ferdousi R. Computational predictive approaches for interaction and structure of aptamers. Journal of Theoretical Biology. 2020;497:110268. [DOI:10.1016/j.jtbi.2020.110268]

47. Sun D, Sun M, Zhang J, Lin X, Zhang Y, Lin F, et al. Computational tools for aptamer identification and optimization. TrAC Trends in Analytical Chemistry. 2022;157:116767. [DOI:10.1016/j.trac.2022.116767]