Abstract
The virtual environment within the computer using software performed on the computer is known as in-silico studies. These drugs designing software play a vital task in discovering new drugs in the field of pharmaceuticals. These designing programs and software are employed in gene sequencing, molecular modeling, and in assessing the three-dimensional structure of the molecule, which can further be used in drug designing and development. Drug development and discovery is not only a powerful, extensive, and an interdisciplinary system but also a very complex and time-consuming method. This book chapter mainly focused on different types of in-silico approaches along with their pharmaceutical applications in numerous diseases.
Acknowledgments
The authors are thankful to the Vice-chancellor of Punjabi University Patiala, India for their encouragement. The authors are also thankful to Er. S. K. Punj, Chairman, Sri Sai Group of Institutes and Smt. Tripta Punj, Managing Director, Sri Sai Group of Institutes for their constant moral support.
-
Author Contributions: Conceptualization and methodology-DK, PS and AM; writing-original draft preparation, Ravi Dhawan RD and Kamal Dua KD software, data curation, writing—review and editing, visualization, supervision-DK and RD; project administration-DK, RD, and KD. All authors have read and agreed to the published version of the manuscript.
-
Research funding: None declared.
-
Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
References
1. Salo-Ahen, OMH, Alanko, I, Bhadane, R, Bonvin, AMJJ, Honorato, RV, Hossain, S, et al.. Molecular dynamics simulations in drug discovery and pharmaceutical development. Processes 2021;9:71–8. https://doi.org/10.3390/pr9010071.Suche in Google Scholar
2. Park, DS, Kim, JM, Lee, YB, Ahn, CH. QSID Tool: a new three-dimensional QSAR environmental tool. J Comput Aided Drug Des 2008;22:873–83. https://doi.org/10.1007/s10822-008-9219-2.Suche in Google Scholar PubMed
3. McGregor, MJ, Muskal, SM. Pharmacophore finger printing: application to QSAR and focused library design. J Chem Inf Comput Sci 1999;39:569–74. https://doi.org/10.1021/ci980159j.Suche in Google Scholar PubMed
4. Macalino, SJ, Gosu, V, Hong, S, Choi, S. Role of computer-aided drug design in modern drug discovery. Arch Pharm Res 2015;38:1686–701. https://doi.org/10.1007/s12272-015-0640-5.Suche in Google Scholar PubMed
5. Wang, T, Wu, MB, Lin, JP, Yang, LR. Quantitative structure-activity relationship: promising advances in drug discovery platforms. Expet Opin Drug Discov 2015;10:1283–300. https://doi.org/10.1517/17460441.2015.1083006.Suche in Google Scholar PubMed
6. Geromichalos, GD. Importance of molecular computer modeling in anticancer drug development. J BUON 2007;12:101–18.Suche in Google Scholar
7. Yang, SY. Pharmacophore modeling and applications in drug discovery: challenges and recent advances. Drug Discov Today 2010;15:444–50. https://doi.org/10.1016/j.drudis.2010.03.013.Suche in Google Scholar PubMed
8. Joseph-McCarthy, D, Baber, JC, Feyfant, E, Thompson, DC, Humblet, C. Lead optimization via high-throughput molecular docking. Curr Opin Drug Discov Dev 2007;10:264–74.Suche in Google Scholar
9. Waterhouse, A, Bertoni, M, Bienert, S, Studer, G, Tauriello, G, Gumienny, R, et al.. SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res 2018;46:296–303. https://doi.org/10.1093/nar/gky427.Suche in Google Scholar PubMed PubMed Central
10. Luther, KB, Haltiwanger, RS. Role of unusual O-glycans in intercellular signalling. Int J Biochem Cell Biol 2009;41:1011–24. https://doi.org/10.1016/j.biocel.2008.10.001.Suche in Google Scholar PubMed PubMed Central
11. Cohen, NC. Structure-based drug design and the discovery of aliskiren (Tekturna): perseverance and creativity to overcome a R&D pipeline challenge. Chem Biol Drug Des 2007;70:557–65. https://doi.org/10.1111/j.1747-0285.2007.00599.x.Suche in Google Scholar PubMed
12. Hellmuth, K, Grosskopf, S, Lum, CT, Wurtele, M, Roder, N, Von Kries, JP, et al.. Specific inhibitors of the protein tyrosine phosphatase Shp2 identified by high-throughput docking. Proc Natl Acad Sci USA 2008;105:7275–80. https://doi.org/10.1073/pnas.0710468105.Suche in Google Scholar PubMed PubMed Central
13. Cozza, G, Gianoncelli, A, Montopoli, M, Laura, C, Venerando, A, Meggio, F, et al.. Identification of novel protein kinase CK1 delta (CK1δ) inhibitors through structure-based virtual screening. Bioorg Med Chem Lett 2008;18:5672–5. https://doi.org/10.1016/j.bmcl.2008.08.072.Suche in Google Scholar PubMed
14. Claudio, NC. Discovery of novel chemotypes to a G-protein-coupled receptor through ligand-steered homology modeling and structure-based virtual screening. J Med Chem 2008;51:581–8.10.1021/jm070759mSuche in Google Scholar PubMed
15. Park, H, Hwang, KY, Kim, YH, Hwan, K, Lee, JY, Kim, K. Discovery and biological evaluation of novel alpha-glucosidase inhibitors with in vivo antidiabetic effect. Bioorg Med Chem Lett 2008;18:3711–5. https://doi.org/10.1016/j.bmcl.2008.05.056.Suche in Google Scholar PubMed
16. Clem, B, Telang, S, Clem, A, Yalcin, A, Meier, J, Simmons, A, et al.. Small-molecule inhibition of 6-phosphofructo-2-kinase activity suppresses glycolytic flux and tumor growth. Mol Cancer Therapeut 2008;7:110–20. https://doi.org/10.1158/1535-7163.mct-07-0482.Suche in Google Scholar
17. Park, H, Bahn, YJ, Jung, SH, Jeong, DG, Lee, SH, Seo, I, et al.. Discovery of novel Cdc25 phosphatase inhibitors with micromolar activity based on the structure based virtual screening. J Med Chem 2008;51:5533–41. https://doi.org/10.1021/jm701157g.Suche in Google Scholar PubMed
18. Song, L, Kalyanaraman, C, Fedorov, A, Fedorov, EV, Glasner, ME, Brown, S, et al.. Prediction and assignment of function for a divergent N-succinyl amino acid racemase. Nat Chem Biol 2007;3:486–91. https://doi.org/10.1038/nchembio.2007.11.Suche in Google Scholar PubMed
19. Sun, W, Gerth, C, Maeda, A, Lodowski, DT, Van Der Kraak, L, Saperstein, DA, et al.. Novel RDH12 mutations associated with Leber congenital amaurosis and cone-rod dystrophy: biochemical and clinical evaluations. Vis Res 2007;47:2055–66. https://doi.org/10.1016/j.visres.2007.04.005.Suche in Google Scholar PubMed PubMed Central
20. Autin, L, Steen, M, Dahlback, B, Villoutreix, BO. Proposed structural models of the prothrombinase (FXa-FVa) complex. Proteins 2006;63:440–50. https://doi.org/10.1002/prot.20848.Suche in Google Scholar PubMed
21. Navarrete, F, Garcia-Gutierrez, MS, Gasparyan, A, Austrich-Olivares, A, Manzanares, J. Role of cannabidiol in the therapeutic intervention for substance use disorders. Front Pharmacol 2021;12:626010. https://doi.org/10.3389/fphar.2021.626010.Suche in Google Scholar PubMed PubMed Central
22. Gagnidze, K, Rozenfeld, R, Mezei, M, Zhou, MM, Devi, LA. Homology modeling and site-directed mutagenesis to identify selective inhibitors of endothelin-converting enzyme-2. J Med Chem 2008;51:3378–87. https://doi.org/10.1021/jm7015478.Suche in Google Scholar PubMed PubMed Central
23. Proell, M, Riedl, SJ, Fritz, JH, Rojas, AM, Schwarzenbacher, R. The nod-like receptor (NLR) family: a tale of similarities and differences. PLoS One 2008;3:2119–25. https://doi.org/10.1371/journal.pone.0002119.Suche in Google Scholar PubMed PubMed Central
24. Guimaraes, AJ, Hamilton, AJ, Guedes, HL, Nosanchuk, JD, Zancope-Oliveira, RM. Biological function and molecular mapping of M antigen in yeast phase of histoplasma capsulatum. PLoS One 2008;3:3449–57. https://doi.org/10.1371/journal.pone.0003449.Suche in Google Scholar PubMed PubMed Central
25. Salomone-Stagni, M, Zambelli, B, Musiani, F, Ciurli, S. A model-based proposal for the role of UreF as a GTPase-activating protein in the urease active site biosynthesis. Proteins 2007;68:749–61. https://doi.org/10.1002/prot.21472.Suche in Google Scholar PubMed
26. Mueckler, M, Thorens, B. The SLC2 (GLUT) family of membrane transporters. Mol Aspect Med 2013;34:121–38. https://doi.org/10.1016/j.mam.2012.07.001.Suche in Google Scholar PubMed PubMed Central
27. Landau, M, Herz, K, Padan, E, Ben-Tal, N. Model structure of the Na+/H+ exchanger 1 (NHE1): functional and clinical implications. J Biol Chem 2007;282:37854–63. https://doi.org/10.1074/jbc.m705460200.Suche in Google Scholar
28. Nguyen, TL, Gussio, R, Smith, JA, Lannigan, DA, Hecht, SM, Scudiero, DA, et al.. Homology model of RSK2 N-terminal kinase domain, structure-based identification of novel RSK2 inhibitors, and preliminary common pharmacophore. Bioorg Med Chem 2006;14:6097–7105. https://doi.org/10.1016/j.bmc.2006.05.001.Suche in Google Scholar PubMed
29. Shoichet, BK, McGovern, SL, Wei, B, Irwin, JJ. Lead discovery using molecular docking. Curr Opin Chem Biol 2002;6:439–46. https://doi.org/10.1016/s1367-5931(02)00339-3.Suche in Google Scholar PubMed
30. Kumar, D, Jain, SK. A comprehensive review of N-heterocycles as cytotoxic agents. Curr Med Chem 2016;23:4338–94. https://doi.org/10.2174/0929867323666160809093930.Suche in Google Scholar PubMed
31. Sharma, P, Sharma, R, Rao, HS, Kumar, D. Phytochemistry and medicinal attributes of A. Scholaris: a review. Int J Pharmaceut Sci Res 2015;6:505–13. https://doi.org/10.1016/j.jare.2014.11.002.Suche in Google Scholar PubMed PubMed Central
32. Kumar, D, Sharma, P, Singh, H, Nepali, K, Gupta, GK, Jain, SK, et al.. The value of pyrans as anticancer scaffolds in medicinal chemistry. RSC Adv 2017;7:36977–99. https://doi.org/10.1039/c7ra05441f.Suche in Google Scholar
33. Kaur, T, Sharma, P, Gupta, G, Ntie-Kang, F, Kumar, D. Treatment of tuberculosis by natural drugs: a review. Plant Arch 2019;19:2168–76.Suche in Google Scholar
34. Kumar, D, Singh, G, Sharma, P, Qayum, A, Mahajan, G, Mintoo, MJ, et al.. 4-aryl/heteroaryl-4H-fused pyrans as anti-proliferative agents: design, synthesis and biological evaluation. Anti Cancer Agents Med Chem 2018;18:57–73. https://doi.org/10.2174/1871520617666170918143911.Suche in Google Scholar
35. Sharma, P, Shri, R, Ntie-Kang, F, Kumar, S. Phytochemical and ethnopharmacological perspectives of Ehretia laevis. Molecules 2021;26:3489. https://doi.org/10.3390/molecules26123489.Suche in Google Scholar
36. Hussain, H, Krohn, K, Uddin, VU, Miana, GA, Greend, IR. Lapachol: an overview. Arkivoc 2007;2:145–71. https://doi.org/10.3998/ark.5550190.0008.204.Suche in Google Scholar
37. Kumar, PP, Siva, B, Rao, BV, Dileep Kumar, G, Lakshma Nayak, V, Nishant Jain, S, et al.. Synthesis and biological evaluation of bergenin-1,2,3-triazole hybrids as novel class of anti-mitotic agents. Bioorg Chem 2019;91:103161–8. https://doi.org/10.1016/j.bioorg.2019.103161.Suche in Google Scholar
38. Kaur, R, Sharma, P, Gupta, GK, Ntie-Kang, F, Kumar, D. Structure activity relationship and mechanistic insights for anti-HIV natural products. Molecules 2020;25:1–49. https://doi.org/10.3390/molecules25092070.Suche in Google Scholar
39. Kumar, D, Sharma, P, Shabu Kaur, R, Lobe, MMM, Gupta, GK, Ntie-Kang, F. In search of therapeutic candidates for HIV/AIDS: rational approaches, design strategies, structure–activity relationship and mechanistic insights. RSC Adv 2021;11:17936–64. https://doi.org/10.1039/d0ra10655k.Suche in Google Scholar
40. Pawar, R, Das, T, Mishra, S, Nutan Pancholi, B, Gupta, SK, Bhat, SV. Synthesis, anti-HIV activity, integrase enzyme inhibition and molecular modeling of cetchol, hydroquinone and quinol labdane analogs. Bioorg Med Chem Lett 2014;24:302–7. https://doi.org/10.1016/j.bmcl.2013.11.014.Suche in Google Scholar
41. Regine, S, Bohacek, Colin, MM, Wayne, CG. The art and practice of structure-based drug design: a molecular modeling perspective. Med Res Rev 1996;16:3–50. https://doi.org/10.1002/(SICI)1098-1128(199601)16:1<3::AID-MED1>3.0.CO;2-6.10.1002/(SICI)1098-1128(199601)16:1<3::AID-MED1>3.0.CO;2-6Suche in Google Scholar
42. Biagini, GA, Fisher, N, Shone, AE, Mubaraki, MA, Srivastava, A, Hill, A, et al.. Generation of quinolone antimalarials targeting the Plasmodium falciparum mitochondrial respiratory chain for the treatment and prophylaxis of malaria. Proc Natl Acad Sci USA 2012;109:8298–303. https://doi.org/10.1073/pnas.1205651109.Suche in Google Scholar
43. Spadaro, A, Negri, M, Marchais-Oberwinkler, S, Bey, E, Frotscher, M. Hydroxybenzothiazoles as new nonsteroidal inhibitors of 17β-hydroxysteroid dehydrogenase type 1 (17β-HSD1). PLoS One 2012;7:292–302. https://doi.org/10.1371/journal.pone.0029252.Suche in Google Scholar
44. Lin, X, Huang, XP, Chen, G, Whaley, R, Peng, S, Wang, Y, et al.. Life beyond kinases: structure-based discovery of sorafenib as nanomolar antagonist of 5-HT receptors. J Med Chem 2012;55:5749–59. https://doi.org/10.1021/jm300338m.Suche in Google Scholar PubMed PubMed Central
45. Xing, L, McDonald, JJ, Kolodziej, SA, Kurumbail, RG, Williams, JM, Warren, CJ, et al.. Discovery of potent inhibitors of soluble epoxide hydrolase by combinatorial library design and structure-based virtual screening. J Med Chem 2011;54:1211–22. https://doi.org/10.1021/jm101382t.Suche in Google Scholar PubMed
46. Lavecchia, A, Giovanni, C, Pesapane, A, Montuori, N, Ragno, P, Martucci, NM, et al.. Discovery of new inhibitors of Cdc25B dual specificity phosphatases by structure-based virtual screening. J Med Chem 2012;55:4142–58. https://doi.org/10.1021/jm201624h.Suche in Google Scholar PubMed
47. Caporuscio, F, Rastelli, G, Imbriano, C, Del, RA. Structure-based design of potent aromatase inhibitors by high-throughput docking. J Med Chem 2011;54:4006–17. https://doi.org/10.1021/jm2000689.Suche in Google Scholar PubMed
48. Birgit, W, Katja, W, Julia, B, Markt, P, Noha, SM, Wolber, G, et al.. Pharmacophore modeling and virtual screening for novel acidic inhibitors of microsomal prostaglandin E2 synthase-1 (mPGES-1). J Med Chem 2011;54:3163–74. https://doi.org/10.1021/jm101309g.Suche in Google Scholar PubMed PubMed Central
49. Stephane, DC, Sebastien, DE, Eric, T, Levan, D, Cueto, M, Schmidt, R, et al.. Virtual screening and computational optimization for the discovery of covalent prolyl oligopeptidase inhibitors with activity in human cells. J Med Chem 2012;55:6306–15. https://doi.org/10.1021/jm3002839.Suche in Google Scholar PubMed
50. Sager, G, Orvoll, EO, Lysaa, RA, Kufareva, I, Abagyan, R, Ravna, AW. Novel cGMP efflux inhibitors identified by virtual ligand screening (VLS) and confirmed by experimental studies. J Med Chem 2012;55:3049–57. https://doi.org/10.1021/jm2014666.Suche in Google Scholar PubMed PubMed Central
51. Lavecchia, A, Cerchia, C. In silico methods to address polypharmacology: current status, applications and future perspectives. Drug Discov Today 2016;21:288–98. https://doi.org/10.1016/j.drudis.2015.12.007.Suche in Google Scholar PubMed
52. Suh, ME, Park, SY, Lee, HJ. Comparison of QSAR methods (CoMFA, CoMSIA, HQSAR) of anticancer 1-N-substituted imidazoquinoline-4,9-dione derivatives. Bull Kor Chem Soc 2002;23:417–22. https://doi.org/10.5012/bkcs.2002.23.3.417.Suche in Google Scholar
53. Kurogi, Y, Guner, OF. Pharmacophore modeling and three-dimensional database searching for drug design using catalyst. Curr Med Chem 2001;8:1035–55. https://doi.org/10.2174/0929867013372481.Suche in Google Scholar PubMed
54. Wade, RC, Salo-Ahen, O. Molecular modeling in drug design. Molecules 2019;24:321–7. https://doi.org/10.3390/molecules24020321.Suche in Google Scholar
55. Xiao-Qiang, D, Hui-Yuan, W, Ying-Lan, Z, Xiang, ML, Jiang, PD, Cao, ZX, et al.. Pharmacophore modelling and virtual screening for identification of new aurora-A kinase inhibitors. Chem Biol Drug Des 2008;71:533–9. https://doi.org/10.1111/j.1747-0285.2008.00663.x.Suche in Google Scholar
56. Xie, HZ, Lin, LL, Xia, J, Zou, J, Yang, L, Wei, YQ, et al.. Pharmacophore modeling study based on known Spleen tyrosine kinase inhibitors together with virtual screening for identifying novel inhibitors. Bioorg Med Chem Lett 2009;19:1944–9. https://doi.org/10.1016/j.bmcl.2009.02.049.Suche in Google Scholar
57. Ji-Xia, R, Lin, LL, Zou, LY, Jin-Liang, Y, Sheng-Yong, Y. Pharmacophore modeling and virtual screening for the discovery of new transforming growth factor-β type I receptor (ALK5) inhibitors. Eur J Med Chem 2009;44:4259–65.10.1016/j.ejmech.2009.07.008Suche in Google Scholar
58. Li, R, Fan, W, Tian, G, Zhu, H, He, L, Cai, J, et al.. The sequence and de novo assembly of the giant panda genome. Nature 2010;463:311–7. https://doi.org/10.1038/nature08696.Suche in Google Scholar
59. Nessling, M, Solinas-Toldo, S, Lichter, P, Reifenberger, G, Wolter, M, Moller, P, et al.. Genomic imbalances are rare in hairy cell leukemia. Genes Chromosomes Cancer 1999;26:182–3. https://doi.org/10.1002/(sici)1098-2264(199910)26:2<182::aid-gcc13>3.0.co;2-z.10.1002/(SICI)1098-2264(199910)26:2<182::AID-GCC13>3.0.CO;2-ZSuche in Google Scholar
60. Pollack, JR, Perou, CM, Alizadeh, AA, Eisen, MB, Pergamenschikov, A, Williams, CF, et al.. Genome-wide analysis of DNA copy-number changes using cDNA microarrays. Nat Genet 1999;23:41–6. https://doi.org/10.1038/12640.Suche in Google Scholar
61. Macgregor, PF, Jeremy, A. Application of microarrays to the analysis of gene expression in cancer. Clin Chem 2002;48:1170–7. https://doi.org/10.1093/clinchem/48.8.1170.Suche in Google Scholar
62. Uthuppan, J, Soni, K. Conformational analysis: a review. Int J Pharmaceut Sci Res 2013;4:34–41.Suche in Google Scholar
63. Cheung, DL, Alessandro, T. Modelling charge transport in organic semiconductors: from quantum dynamics to soft matter. Phys Chem Chem Phys 2008;10:5941–52. https://doi.org/10.1039/b807750a.Suche in Google Scholar
64. Cheung, DL. Molecular simulation of nanoparticle diffusion at fluid interfaces. Chem Phys Lett 2010;495:55–9. https://doi.org/10.1016/j.cplett.2010.06.074.Suche in Google Scholar
65. Moller, W, Eckstein, W. Tridyn – a TRIM simulation code including dynamic composition changes. Nucl Instrum Methods Phys Res B 1984;2:814–8. https://doi.org/10.1016/0168-583x(84)90321-5.Suche in Google Scholar
66. Hemert, FJ, Amons, R, Wim, JMP, Hans, VO, Moller, W. The primary structure of elongation factor EF-lac from the brine shrimp Artemia. EMBO J 1984;3:1109–13. https://doi.org/10.1002/j.1460-2075.1984.tb01937.x.Suche in Google Scholar PubMed PubMed Central
67. Milik, M, Skolnick, J. Insertion of peptide chains into lipid membranes: an off-lattice Monte Carlo dynamics model. Proteins Struct Funct Genet 1993;15:10–25. https://doi.org/10.1002/prot.340150104.Suche in Google Scholar PubMed
68. Chaslot, GMJB, Winands, MHM, Szita, I, van den Herik, HJ. Cross entropy for Monte Carlo tree search. ICGA J (Int Comput Games Assoc) 2008;31:145–56. https://doi.org/10.3233/icg-2008-31303.Suche in Google Scholar
69. Hansson, T, Chris, O, Gunsteren, WF. Molecular dynamics simulations. Curr Opin Struct Biol 2002;12:190–6.https://doi.org/10.1016/s0959-440x(02)00308-1.Suche in Google Scholar PubMed
70. Friesner, RA, Banks, JL, Murphy, RB, Halgren, TA, Klicic, JJ, Mainz, DT, et al.. Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 2004;47:1739–49. https://doi.org/10.1021/jm0306430.Suche in Google Scholar PubMed
71. Methe, BA, Nelson, KE, Deming, JW, Momen, B, Melamud, E, Zhang, X, et al.. The psychrophilic lifestyle as revealed by the genome sequence of Colwellia psychrerythraea 34H through genomic and proteomic analyses. Proc Natl Acad Sci USA 2005;102:10913–8. https://doi.org/10.1073/pnas.0504766102.Suche in Google Scholar PubMed PubMed Central
72. Peng, Y, Li, Z, John, M. Loss of protein structure stability as a major causative factor in monogenic disease. J Mol Biol 2005;353:459–73.10.1016/j.jmb.2005.08.020Suche in Google Scholar PubMed
73. Jacques, MJ, Pierre, C, Jacob, F. Allosteric proteins and cellular control systems. J Mol Biol 1963;6:306–29.10.1016/S0022-2836(63)80091-1Suche in Google Scholar PubMed
74. Edmunds, NS, McGuffin, LJ. Computational methods for the elucidation of protein structure and interactions. Methods Mol Biol 2021;2305:23–52. https://doi.org/10.1007/978-1-0716-1406-8_2.Suche in Google Scholar PubMed
© 2021 Walter de Gruyter GmbH, Berlin/Boston
Artikel in diesem Heft
- Frontmatter
- Reviews
- Magnetic characterization of magnetoactive elastomers containing magnetic hard particles using first-order reversal curve analysis
- Microscopic understanding of particle-matrix interaction in magnetic hybrid materials by element-specific spectroscopy
- Biodeinking: an eco-friendly alternative for chemicals based recycled fiber processing
- Bio-based polyurethane aqueous dispersions
- Cellulose-based polymers
- Biodegradable shape-memory polymers and composites
- Natural substances in cancer—do they work?
- Personalized and targeted therapies
- Identification of potential histone deacetylase inhibitory biflavonoids from Garcinia kola (Guttiferae) using in silico protein-ligand interaction
- Chemical computational approaches for optimization of effective surfactants in enhanced oil recovery
- Social media and learning in an era of coronavirus among chemistry students in tertiary institutions in Rivers State
- Techniques for the detection and quantification of emerging contaminants
- Occurrence, fate, and toxicity of emerging contaminants in a diverse ecosystem
- Updates on the versatile quinoline heterocycles as anticancer agents
- Trends in microbial degradation and bioremediation of emerging contaminants
- Power to the city: Assessing the rooftop solar photovoltaic potential in multiple cities of Ecuador
- Phytoremediation as an effective tool to handle emerging contaminants
- Recent advances and prospects for industrial waste management and product recovery for environmental appliances: a review
- Integrating multi-objective superstructure optimization and multi-criteria assessment: a novel methodology for sustainable process design
- A conversation on the quartic equation of the secular determinant of methylenecyclopropene
- Recent developments in the synthesis and anti-cancer activity of acridine and xanthine-based molecules
- An overview of in silico methods used in the design of VEGFR-2 inhibitors as anticancer agents
- Fragment based drug design
- Advances in heterocycles as DNA intercalating cancer drugs
- Systems biology–the transformative approach to integrate sciences across disciplines
- Pharmaceutical interest of in-silico approaches
- Membrane technologies for sports supplementation
- Fused pyrrolo-pyridines and pyrrolo-(iso)quinoline as anticancer agents
- Membrane applications in the food industry
- Membrane techniques in the production of beverages
- Statistical methods for in silico tools used for risk assessment and toxicology
- Dicarbonyl compounds in the synthesis of heterocycles under green conditions
- Green synthesis of triazolo-nucleoside conjugates via azide–alkyne C–N bond formation
- Anaerobic digestion fundamentals, challenges, and technological advances
- Survival is the driver for adaptation: safety engineering changed the future, security engineering prevented disasters and transition engineering navigates the pathway to the climate-safe future
Artikel in diesem Heft
- Frontmatter
- Reviews
- Magnetic characterization of magnetoactive elastomers containing magnetic hard particles using first-order reversal curve analysis
- Microscopic understanding of particle-matrix interaction in magnetic hybrid materials by element-specific spectroscopy
- Biodeinking: an eco-friendly alternative for chemicals based recycled fiber processing
- Bio-based polyurethane aqueous dispersions
- Cellulose-based polymers
- Biodegradable shape-memory polymers and composites
- Natural substances in cancer—do they work?
- Personalized and targeted therapies
- Identification of potential histone deacetylase inhibitory biflavonoids from Garcinia kola (Guttiferae) using in silico protein-ligand interaction
- Chemical computational approaches for optimization of effective surfactants in enhanced oil recovery
- Social media and learning in an era of coronavirus among chemistry students in tertiary institutions in Rivers State
- Techniques for the detection and quantification of emerging contaminants
- Occurrence, fate, and toxicity of emerging contaminants in a diverse ecosystem
- Updates on the versatile quinoline heterocycles as anticancer agents
- Trends in microbial degradation and bioremediation of emerging contaminants
- Power to the city: Assessing the rooftop solar photovoltaic potential in multiple cities of Ecuador
- Phytoremediation as an effective tool to handle emerging contaminants
- Recent advances and prospects for industrial waste management and product recovery for environmental appliances: a review
- Integrating multi-objective superstructure optimization and multi-criteria assessment: a novel methodology for sustainable process design
- A conversation on the quartic equation of the secular determinant of methylenecyclopropene
- Recent developments in the synthesis and anti-cancer activity of acridine and xanthine-based molecules
- An overview of in silico methods used in the design of VEGFR-2 inhibitors as anticancer agents
- Fragment based drug design
- Advances in heterocycles as DNA intercalating cancer drugs
- Systems biology–the transformative approach to integrate sciences across disciplines
- Pharmaceutical interest of in-silico approaches
- Membrane technologies for sports supplementation
- Fused pyrrolo-pyridines and pyrrolo-(iso)quinoline as anticancer agents
- Membrane applications in the food industry
- Membrane techniques in the production of beverages
- Statistical methods for in silico tools used for risk assessment and toxicology
- Dicarbonyl compounds in the synthesis of heterocycles under green conditions
- Green synthesis of triazolo-nucleoside conjugates via azide–alkyne C–N bond formation
- Anaerobic digestion fundamentals, challenges, and technological advances
- Survival is the driver for adaptation: safety engineering changed the future, security engineering prevented disasters and transition engineering navigates the pathway to the climate-safe future