Добавил:
Sekretar
kiopkiopkiop18@yandex.ru
t.me/Prokururor I Вовсе не секретарь, но почту проверяю
Опубликованный материал нарушает ваши авторские права? Сообщите нам.
Вуз:
Предмет:
Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5649_Библиотеки_им_академика_М_И_Перельмана
.pdf
Drug Repurposing and Computational Drug Discovery: Strategies and Advances
2. Stone, J. E.; Phillips, J. C.; Freddolino, P. L.; Hardy D. J.; Trabuco, L. G.; Schulten, K.
Accelerating Molecular Modeling Applications With Graphics Processors. J. Comput.
Chem. 2007, 28 (16), 2618–2640.
3. Stone, J. E.; Hardy, D. J.; Ufimtsev, I. S.; Schulten, K. GPU Accelerated Molecular
Modeling Coming of Age. J. Mol. Graph. Model 2010, 29 (2), 116–125.
4. Waldman, S. A.; Terzic, A. Systems-Based Discovery Advances Drug Development.
Clin. Pharmacol. Ther. 2013, 93 (4), 285–287.
5. Barratt, M. J., Frail, D. E. Drug Repositioning: Bringing New Life to Shelved Assets and
Existing Drugs; John Wiley & Sons, 2012; pp 389–431.
6. Pushpakom, S.; Iorio, F.; Eyers, P. A.; Escott, K. J.; Hopper, S.; Wells, A.; Doig, A.;
Guilliams, T.; Latimer, J.; McNamee, C.; Norris, A. Drug Repurposing: Progress,
Challenges and Recommendations. Nat. Rev. Drug Discov. 2019, 18 (1), 41–58.
7. Polamreddy, P.; Gattu, N. The Drug Repurposing Landscape From 2012 to 2017:
Evolution, Challenges, and Possible Solutions. Drug Discov. Today 2019, 24 (3),
789–795.
8. Park, K. A Review of Computational Drug Repurposing. Transl. Clin. Pharmacol.
2019, 27 (2), 59–63.
9. Azencott, C. A.; Ksikes, A.; Swamidass, S. J.; Chen, J. H.; Ralaivola, L.; Baldi, P.
One-to Four-Dimensional Kernels for Virtual Screening and the Prediction of Physical,
Chemical and Biological Properties. J. Chem. Inf. Model 2007, 47 (3), 965–974.
10. Cheng, F., Sutariya, V. Applications of Artificial Neural Network Modeling in Drug
Discovery. Clin. Exp. Pharmacol. 2012, 2 (3), 1–2.
11. Ou-Yang, S. S.; Lu, J. Y.; Kong, X. Q.; Liang, Z. J.; Luo, C.; Jiang, H. Computational
Drug Discovery. Acta Pharmacol. Sin. 2012, 33 (9), 1131–1140.
12. Hung, C. L.; Chen, C. C. Computational Approaches for Drug Discovery. Drug Dev.
Res. 2014, 75 (6), 412–418.
13. Teo, C. Y.; Rahman, M. B. A.; Chor, A. L. T.; Salleh, A. B.; Ballester, P. J.; Tejo, B. A.
Ligand-Based Virtual Screening for the Discovery of Inhibitors for Protein Arginine
Deiminase Type 4 (PAD4). Metabolomics 2013, 3 (1),118—122.
14. Batool, M.; Ahmad, B.; Choi, S. A Structure-Based Drug Discovery
Paradigm. Int. J. Mol. Sci. 2019, 20 (11), 2783.
15. Acharya, C.; Coop, A. E.; Polli, J. MacKerell, D. A. Recent Advances in Ligand-Based
Drug Design: Relevance and Utility of the Conformationally Sampled Pharmacophore
Approach. Curr. Comput.-Aided Drug Des. 2011, 7 (1), 10–22.
16. Kaushik, A. C.; Kumar, A.; Bharadwaj, S.; Chaudhary, R.; Sahi, S. Ligand-Based
Approach for In-silico Drug Designing. In Bioinformatics Techniques for Drug
Discovery; Springer: Cham, 2018; pp 11–-19.
17. Gajipara, J.; Georrge, J. J. In Tools for Ligand Based Drug Discovery, Proceedings of
the National Science Symposium on Recent Trends in Science and Technology-2018,
Christ College, Rajkot & Gujarat Council on Science and Technology (GUJCOST),
Govt. of Gujarat, February 11, 2018.
18. Neves, B. J.; Braga, R. C.; Melo-Filho, C. C.; Moreira-Filho, J. T.; Muratov, E. N.;
Andrade, C. H. QSAR-Based Virtual Screening: Advances and Applications in Drug
Discovery. Front. Pharmacol. 2018, 9, 1275.
19. Peter, S. C.; Dhanjal, J. K.;Malik, V .; Radhakrishnan, N.; Jayakanthan, M.;
Sundar, D. Jayakanthan, M. Encyclopedia of Bioinformatics and Computational
Biology; Ranganathan, S., Grib-skov, M., Nakai, K., Schönbach, C., Eds.; 2018; pp
Данная книга находится в списке для перевода на русский язык сайта https://meduniver.com/
661–676.
20. Danishuddin; Khan, A. U. Descriptors and Their Selection Methods in QSAR Analysis:
Paradigm for Drug Design. Drug Discov. Today 2016, 21 (8), 1291–1302.
21. Verma, J.; Khedkar, V. M. Coutinho, E. C. 3D-QSAR in Drug Design-A Review. Curr.
Top. Med. Chem. 2010, 10 (1), 95–115.
22. Melo-Filho, C. C.; Braga, R. C.; Andrade, C. H. 3D-QSAR Approaches in Drug Design:
Perspectives to Generate Reliable CoMFA Models. Curr. Comput. Aided Drug Des.
2014, 10 (2), 148–159.
23. Katsila, T.; Spyroulias, G. A.; Patrinos, G. P.; Matsoukas, M. T. Computational
Approaches in Target Identification and Drug Discovery. Comput. Struct. Biotechnol.
J. 2016, 14, 177–184.
24. Cumming, J. G.; Davis, A. M.; Muresan, S.; Haeberlein, M.; Chen, H. Chemical
Predictive Modelling to Improve Compound Quality. Nat. Rev. Drug Discov. 2013, 12
(12), 948–962.
25. Jing, Y.; Bian, Y.; Hu, Z.; Wang, L.; Xie, X. Q. S. Deep Learning for Drug Design: An
Artificial Intelligence Paradigm for Drug Discovery in the Big Data Era. AAPS J. 2018,
20 (3), 1–10.
26. Koscielny, G.; An, P.; Carvalho-Silva, D.; Cham, J. A.; Fumis, L.; Gasparyan, R.;
Dunham, I. Open Targets: A Platform for Therapeutic Target Identification and
Validation. Nucleic Acids Res. 2017, 45 (D1), D985–D994.
27. Gaulton, A.; Bellis, L. J.; Bento, A. P.; Chambers, J.; Davies, M.; Hersey, A.; Overington,
J. P. ChEMBL: A Large-Scale Bioactivity Database for Drug Discovery. Nucleic Acids
Res. 2012, 40 (D1), D1100–D1107.
28. Vamathevan, J.; Clark, D.; Czodrowski, P.; Dunham, I.; Ferran, E.; Lee, G.; Li, B.;
Madabhushi, A.; Shah, P.; Spitzer, M.; Zhao, S. Applications of Machine Learning in
Drug Discovery and Development. Nat. Rev. Drug Discov. 2019, 18, 463–477.
29. Brown, J. B.; Okuno, Y. Systems Biology and Systems Chemistry: New Directions for
Drug Discovery. Chem. Biol. 2012, 19 (1), 23–28.
30. Reymond, J. L.; Ruddigkeit, L.; Blum, L.; van Deursen, R. The Enumeration of Chemical
Space. Wiley Interdiscip. Rev. Comput. Mol. Sci. 2012, 2 (5), 717–733.
31. Wang, Y.; Xiao, J.; Suzek, T. O.; Zhang, J.; Wang, J.; Bryant, S. H. PubChem: A Public
Information System for Analyzing Bioactivities of Small Molecules. Nucleic Acids
Res. 2009, 37 (suppl_2), W623–W633.
32. Irwin, J. J.; Shoichet, B. K. ZINC - A Free Database of Commercially Available
Compounds for Virtual Screening. J. Chem. Inf. Model 2005, 45, 177−182.
33. Williams, A. J. Public Chemical Compound Databases. Curr . Opin. Drug Discov.
Dev. 2008, 11 (3), 393.
34. Chen, J.; Swamidass, S. J.; Dou, Y., Bruand, J.; Baldi, P. ChemDB: A Public Database
of Small Molecules and Related Chemoinformatics Resources. Bioinformatics 2005, 21
(22), 4133–4139.
35. Liu, T.; Lin, Y.; Wen, X.; Jorissen, R. N.; Gilson, M. K. BindingDB: A Web-Accessible
Database of Experimentally Determined Protein–Ligand Binding Affinities. Nucleic
Acids Res. 2007, 35 (suppl_1), D198–D201.
36. Voigt, J. H.; Bienfait, B.; Wang, S.; Nicklaus, M. C. Comparison of the NCI Open
Database With Seven Large Chemical Structural Databases. J. Chem. Inf. Comput. Sci.
2001, 41 (3), 702–712.
52
37. Davis, A. P.; King, B. L.; Mockus, S.; Murphy, C. G.; Saraceni-Richards, C.; Rosenstein,
38. Wishart, D. S.; Knox, C.; Guo, A. C.; Eisner, R.; Young, N.; Gautam, B.; Hau, D. D.;
39. Frolkis, A.; Knox, C.; Lim, E.; Jewison, T.; Law, V.; Hau, D. D.; Liu, P.; Gautam, B.;
40. Knox, C.; Law, V.; Jewison, T.; Liu, P.; Ly, S.; Frolkis, A.; Pon, A.; Banco, K.; Mak,
41. Wishart, D. S. Introduction to Cheminformatics. Curr. Protoc. Bioinform. 2007, 18 (1),
42. Wasley, J. W. Chemoinformatics, Concepts, Methods, and Tools for Drug Discovery;
43. Bone, R. G.; Firth, M. A.; Sykes, R. A. SMILES Extensions for Pattern Matching and
44. Begam, B. F.; Kumar, J. S. A Study on Cheminformatics and its Applications on Modern
45. Qing, X.; Lee, X. Y.; De Raeymaecker, J.; Tame, J. R.; Zhang, K. Y.; De Maeyer, M.;
46. Schuster, D.; Spetea, M.; Music, M.; Rief, S.; Fink, M.; Kirchmair, J.; Rollinger, J.
47. Langer, T. Pharmacophores in Drug Research. Mol Inform. 2010, 29 (6–7), 470–475.
48. Arcon, J. P.; Modenutti, C. P.; Avendaño, D.; Lopez, E. D.; Defelipe, L. A.; Ambrosio,
49. Schaller, D.; Pach, S.; Wolber, G. PyRod: Tracing Water Molecules in Molecular
50. Yu, W.; Lakkaraju, S. K.; Raman, E. P.; Fang, L.; MacKerell Jr, A. D. Pharmacophore
51. Sato, T.; Honma, T.; Yokoyama, S. Combining Machine Learning and Pharmacophore-
Drug Repurposing and Computational Drug Discovery: Strategies and Advances
M.; Mattingly, C. J. The Comparative Toxicogenomics Database: Update 2011. Nucleic
Acids Res. 2010, 39 (suppl_1), D1067–D1072.
Psychogios, N.; Dong, E.; Bouatra, S.; Mandal, R.; Sinelnikov, I.; Xia, J.; Jia, L.; Cruz,
J. A.; Lim, E.; Sobsey, C. A.; Shrivastava, S.; Huang, P.; Liu, P.; Fang, L.; Peng, J.;
Fradette, R.; Cheng, D.; Tzur, D.; Clements, M.; Lewis, A.; De Souza, A.; Zuniga,
A.; Dawe, M.; Xiong, Y.; Clive, D.; Greiner, R.; Nazyrova, A.; Shaykhutdinov, R.; Li,
L.; Vogel, H. J.; Forsythe, I. HMDB: A Knowledgebase for the Human Metabolome.
Nucleic Acids Res. 2009, 37, D603−D610.
Ly, S.; Guo, A. C.; Xia, J.; Liang, Y.; Shrivastava, S.; Wishart, D. S. SMPDB: The Small
Molecule Pathway Database. Nucleic Acids Res. 2010, 38, D480−D487.
C.; Neveu, V.; Djoumbou, Y.; Eisner, R.; Guo, A. C.; Wishart, D. S. DrugBank 3.0: A
Comprehensive Resource for ‘Omics’ Research on Drugs. Nucleic Acids Res. 2011, 39,
D1035−D1041.
14.1.
Jurgen, B., Ed.; Humana Press Inc., Totowa, NJ, 2004; vol. xiii+, p 524.
Molecular Transformations: Applications in Chemoinformatics. J. Chem. Inf. Comput.
Sci. 1999, 39 (5), 846–860.
Drug Discovery. Procedia Eng. 2012, 38, 1264–1275.
Voet, A. Pharmacophore Modeling: Advances, Limitations, and Current Utility in Drug
Discovery. J. Recept. Ligand Channel Res. 2014, 7, 81–92.
M. Morphinans and Isoquinolines: Acetylcholinesterase Inhibition, Pharmacophore
Modeling, and Interaction With Opioid Receptors. Bioorg. Med. Chem. 2010, 18 (14),
5071–5080.
F. A.; Marti, M. A. AutoDock Bias: Improving Binding Mode Prediction and Virtual
Screening Using Known Protein–Ligand Interactions. Bioinformatics 2019, 35 (19),
3836–3838.
Dynamics Simulations. J. Chem. Inf. Model 2019, 59 (6), 2818–2829.
Modeling Using Site-Identification by Ligand Competitive Saturation (SILCS) With
Multiple Probe Molecules. J. Chem. Inf. Model 2015, 55 (2), 407–420.
Based Interaction Fingerprint for in Silico Screening. J .Chem. Inf. Model 2010, 50
(1),170–185.
52. Wang, X.; Shen, Y.; Wang, S.; Li, S.; Zhang, W.; Liu, X.; Li, H. PharmMapper 2017
Данная книга находится в списке для перевода на русский язык сайта https://meduniver.com/
Update: A Web Server for Potential Drug Target Identification With a Comprehensive
Target Pharmacophore Database. Nucleic Acids Res. 2017, 45 (W1), W356–W360.
53. Tian, S.; Wang, J.; Li, Y.; Li, D.; Xu, L.; Hou, T. The Application of in Silico Drug-
Likeness Predictions in Pharmaceutical Research. Adv. Drug Deliv. Rev. 2015, 86, 2–10.
54. Bickerton, G. R.; Paolini, G. V.; Besnard, J.; Muresan, S.; Hopkins, A. L. Quantifying
the Chemical Beauty of Drugs. Nat. Chem. 2012, 4 (2), 90–98.
55. Yusof, I.; Segall, M. D. Considering the Impact Drug-Like Properties Have on the
Chance of Success. Drug Discov . Today 2013, 18 (13–14), 659–666.
56. Singh, N.; Sun, H.; Chaudhury, S.; AbdulHameed, M. D. M.; Wallqvist, A.; T awa, G. A
Physicochemical Descriptor-Based Scoring Scheme for Effective and Rapid Filtering of
Kinase-Like Chemical Space. J. Cheminform. 2012, 4 (1), 1–12.
57. Li, D.; Chen, L.; Li, Y.;Tian, S.; Sun, H.; Hou, T. ADMET Evaluation in Drug Discovery.
13. Development of in Silico Prediction Models for P-glycoprotein Substrates. Mol.
Pharm. 2014, 11 (3), 716–726.
58. Wang, S.; Sun, H.; Liu, H.; Li, D.; Li, Y.; Hou, T. ADMET Evaluation in Drug Discovery.
Predicting hERG Blockers by Combining Multiple Pharmacophores and Machine
Learning Approaches. Mol. Pharm. 2016, 13 (8), 2855–2866.
59. Pathak, R. K.; Gupta, A.; Shukla, R.; Baunthiyal, M. Identification of New Drug-Like
Compounds From Millets as Xanthine Oxidoreductase Inhibitors for Treatment of
Hyperuricemia: A Molecular Docking and Simulation Study. Comput. Biol. Chem.
2018, 76, 32–41.
60. Salum, L.; Polikarpov, I.; Andricopulo, A. D. Structure-Based Approach for the
Study of Estrogen Receptor Binding Affinity and Subtype Selectivity. J. Chem. Inf.
Model 2008, 48, 2243–2253.
61. Kalyaanamoorthy, S.; Chen, Y. P. Structure-Based Drug Design to Augment Hit
Discovery. Drug Discov. Today 2011, 16, 831–839.
62. Blaney, J. A Very Short History of Structure-Based Design: How Did we Get Here and
Where do we Need to go? J. Comput. Aided Mol. Des. 2012, 26, 13–14.
63. Kumar, A.; Chordia, N. Role of Bioinformatics in Biotechnology. Res. Rev. Biosci,
2017, 12 (1), 116.
64. Pathak, R. K.; Taj, G.; Pandey, D.; Kasana, V. K.; Baunthiyal, M.; Kumar, A.
Molecular Modeling and Docking Studies of Phytoalexin (s) With Pathogenic Protein
(s) as Molecular Targets for Designing the Derivatives With Anti-Fungal Action on
'Alternaria'spp. of'Brassica'. Plant Omics. 2016, 9 (3), 172–183.
65. Bagaria, A.; Jaravine, V.; Huang, Y. J.; Montelione, G. T.; Güntert, P. Protein
Structure Validation by Generalized Linear Model Root-Mean-Square Deviation
Prediction. Protein Sci. 2012, 21 (2), 229–238.
66. Singh, D. B.; Tripathi, T. Frontiers in Protein Structure, Function, and Dynamics;
Springer, 2020.
67. Kryshtafovych, A.; Schwede, T.; Topf, M.; Fidelis, K.; Moult, J. Critical Assessment of
Methods of Protein Structure Prediction (CASP)—Round XIII. Proteins: Struct. Funct.
Bioinf. 2019, 87 (12), 1011–1020.
68. Mamgain, S.; Dhiman, S.; Pathak, R. K.; Baunthiyal, M. In Silico Identification of
Agriculturally Important Molecule (s) for Defense Induction Against Bacterial Blight
Disease in Soybean (Glycine max). Plant Omics. 2018, 11 (2), 98–105.
Drug Repurposing and Computational Drug Discovery: Strategies and Advances
69. Adrian-Scotto, M.; Vasilescu, D. Quantum Molecular Modeling of Glycyl-adenylate.
J. Biomol. Struct. Dyn. 2008, 25 (6), 697–708.
70. Yuriev, E.; Ramsland, P. A. Latest Developments in Molecular Docking: 2010–2011 in
Review. J. Mol. Recognit. 2013, 26 (5), 215–239.
71. Guedes, I. A.; de Magalhães, C. S.; Dardenne, L. E. Receptor–Ligand Molecular
Docking. Biophys. Rev. 2014, 6 (1), 75–87.
72. Feinstein, W. P.; Brylinski, M. Calculating an Optimal Box Size for Ligand Docking
and Virtual Screening Against Experimental and Predicted Binding Pockets. J.
Cheminform. 2015, 7 (1), 1–10.
73. Durham, E.; Dorr, B.; Woetzel, N.; Staritzbichler, R.; Meiler. J. Solvent Accessible
Surface Area Approximations for Rapid and Accurate Protein Structure Prediction. J.
Chem. Inf. Model 2009, 15 (9), 1093–1108.
74. Neudert, G.; Klebe, G. DSX: A Knowledge-Based Scoring Function for the Assessment
of Protein–Ligand Complexes. J. Chem. Inf. Model 2011, 51 (10), 2731–2745.
75. Singh, D. B.; Dwivedi, S. Structural Insight Into Binding Mode of Inhibitor With SAHH
of Plasmodium and Human: Interaction of Curcumin With Anti-Malarial Drug Targets.
J. Chem. Biol. 2016, 9 (4), 107–120.
76. Wallace, A. C.; Laskowski, R. A.; Thornton, J. M. LIGPLOT: A Program to Generate
Schematic Diagrams of Protein-Ligand Interactions. Protein Eng. Des. Sel. 1995, 8 (2),
127–134.
77. Laskowski, R. A.; Swindells, M. B. LigPlot+: Multiple Ligand–Protein Interaction
Diagrams for Drug Discovery. J. Chem. Inf. Model 2011, 51 (10), 2778–2786.
78. DeLano, W. L. Pymol: An Open-Source Molecular Graphics Tool. CCP4 Newsletter on
Protein Crystallography, 2002, vol 40 (1), pp 82–92.
79. Pettersen, E. F.; Goddard, T. D.; Huang, C. C.; Couch, G. S.; Greenblatt, D. M.; Meng,
E. C.; Ferrin, T. E. UCSF Chimera-a Visualization System for Exploratory Research and
Analysis. J. Comput. Chem . 2004, 25 (13), 1605–1612.
80. Lindahl, E. R. Molecular Dynamics Simulations. In Molecular modeling of
proteins; Kukol, A., Ed.; Humana Press, 2008; vol 443, pp 3–23.
81. Khan, F. I.; Wei, D. Q.; Gu, K. R.; Hassan, M. I.; Tabrez, S. Current Updates on Computer
Aided Protein Modeling and Designing. Int. J. Biol. Macromol. 2016, 85, 48–62.
82. Pronk, S.; Pall, S.; Schulz, R.; Larsson, P.; Bjelkmar, P.; Apostolov, R.; Shirts, M. R.;
Smith, J. C.; Kasson, P. M.; van der Spoel, D; Hess, B.; Lindahl, E. GROMACS 4.5:
A High-Throughput and Highly Parallel Open Source Molecular Simulation Toolkit.
Bioinformatics 2013, 29 (7), 845–854.
83. Salomon-Ferrer, R.; Gotz, A. W.; Poole, D.; Le Grand, S.; Walker, R. C. Routine Micro
Second Molecular Dynamics Simulations With AMBER on GPUs. 2. Explicit Solvent
Particle MeshEwald. J. Chem. Theory Comput. 2013, 9 (9), 3878–3888.
84. Brooks, B. R.; Brooks III, C. L.; Mackerell Jr, A. D.; Nilsson, L.; Petrella, R. J.; Roux,
B.; Karplus, M. CHARMM: The Biomolecular Simulation Program. J. Comput. Chem .
2009, 30 (10), 1545–1614.
85. Phillips, J. C.; Hardy, D. J.; Maia, J. D.; Stone, J. E.; Ribeiro, J. V.; Bernardi, R. C.;
Buch, R.; Fiorin, G.; Hénin, J.; Jiang, W.; McGreevy, R. Scalable Molecular Dynamics
on CPU and GPU Architectures With NAMD. J. Chem. Phys. 2020, 153 (4), 044130.
86. Humbert, M. T.; Zhang, Y.; Maginn, E. J. PyLAT: Python LAMMPS Analysis Tools. J.
Chem. Inf. Model 2019, 59 (4), 1301–1305.
87. Harrold, J. M.; Ramanathan, M.; Mager, D. E. Network-Based Approaches in Drug
Данная книга находится в списке для перевода на русский язык сайта https://meduniver.com/
Discovery and Early Development. Clin. Pharmacol. Ther. 2013, 94 (6), 651–658.
88. Pujol, A.; Mosca, R.; Farres, J.; Aloy, P. Unveiling the Role of Network and Systems
Biology in Drug Discovery. Trends Pharmacol. Sci. 2010, 31 (3), 115–123.
89. Hopkins, A. L. Network Pharmacology. Nat. Biotechnol. 2007, 25 (10), 1110–1111.
90. Jia-hu, P. New Paradigm for Drug Discovery Based on Network Pharmacology. Chinese
J. New Drugs Clin. Remed. 2009, 10, 002.
91. Zhang, G. B.; Li, Q. Y.; Chen, Q. L.; Su, S. B. Network Pharmacology: A New Approach
for Chinese Herbal Medicine Research. Evid. Based Complement. Alter. Med. 2013,
2013.
92. Kibble, M.; Saarinen, N.; Tang, J.; Wennerberg, K.; Mäkelä, S.; Aittokallio, T. Network
Pharmacology Applications to Map the Unexplored Target Space and Therapeutic
Potential of Natural Products. Nat. Prod. Rep. 2015, 32 (8),1249–1266.
93. Muhammad, J.; Khan, A.; Ali, A.; Fang, L.; Yanjing, W.; Xu, Q.; Wei, D. Q. Network
Pharmacology: Exploring the Resources and Methodologies. Curr. Top. Med. Chem.
2018, 18 (12), 949–964.
94. Lapinsh, M.; Prusis, P.; Uhlén, S.; W ikberg, J. E. Improved Approach for
Proteochemometrics Modeling: Application to Organic Compound-Amine G ProteinCoupled Receptor Interactions. Bioinformatics 2005, 21 (23), 4289–4296.
95. van Westen, G. J.; Hendriks, A.; Wegner, J. K.; IJzerman, A. P.; van Vlijmen, H. W.;
Bender, A. Significantly Improved HIV Inhibitor Efficacy Prediction Employing
Proteochemometric Models Generated From Antivirogram Data. PLoS Comput. Biol.
2013, 9 (2), e1002899.
96. Strömbergsson, H.; Lapins, M.; Kleywegt. G. J.; Wikberg, J. E. Towards Proteome–
Wide Interaction Models Using the Proteochemometrics Approach. Mol. Inform. 2010,
29 (6–7), 499–508.
97. Dakshanamurthy, S.; Issa, N. T .; Assefnia, S.; Seshasayee, A.; Peters, O. J.;
Madhavan, S.; Byers, S. W. Predicting New Indications for Approved Drugs Using a
Proteochemometric Method. J. Med. Chem. 2012, 55 (15), 6832–6848.
98. Simeon, S.; Spjuth, O.; Lapins, M.; Nabu, S.; Anuwongcharoen, N.; Prachayasittikul,
V.; Wikberg, J. E.; Nantasenamat, C. Origin of Aromatase Inhibitory Activity Via
Proteochemometric Modeling. Peer J. 2016, 4, e1979.
99. Nazarshodeh, E.; Sheikhpour, R.; Gharaghani, S.; Sarram, M. A. A Novel
Proteochemometrics Model for Predicting the Inhibition of Nine Carbonic Anhydrase
Isoforms Based on Supervised Laplacian Score and k-Nearest Neighbour Regression.
SAR QSAR Environ. Res. 2018, 29 (6), 419–437.
100. Murrell, D. S.; Cortes-Ciriano, I.; van Westen, G. J. P.; Stott, I. P.; Bender, A.; Malliavin,
T. E.; Glen, R. C. Chemically Aware Model Builder (CAMB): An R Package for
Property and Bioactivity Modelling of Small Molecules. J. Cheminformatics. 2015, 7,
45.
101. Prusis, P.; Junaid, M.; Petrovska, R.; Yahorava, S.; Yahorau, A.; Katzenmeier, G.;
Wikberg, J. E. Design and Evaluation of Substrate-Based Octapeptide and Non SubstrateBased Tetrapeptide Inhibitors of Dengue Virus NS2b-NS3 Proteases. Biochem. Biophys.
Res. Commun. 2013, 434 (4), 767–772.
102. Jaeger, S.; Fulle, S.; Turk, S. Mol2vec: Unsupervised Machine Learning Approach With
Chemical Intuition. J. Chem. Inf. Model 2018, 58 (1), 27–35.
Drug Repurposing and Computational Drug Discovery: Strategies and Advances
103. Qiu, T.; Wu, D.; Qiu, J.; Cao, Z. Finding the Molecular Scaffold of Nuclear Receptor
Inhibitors Through Highthroughput Screening Based on Proteochemometric Modelling.
J. Cheminformatics 2018, 10, 21.
104. Subramanian, V.; Ain, Q. U.; Henno, H.; Pietil€a, L. O.; Fuchs, J. E.; Prusis, P.;
Wohlfahrt, G. 3D Proteochemometrics: Using Three-Dimensional Information of
Proteins and Ligands to Address Aspects of the Selectivity of Serine Proteases. Med.
Chem. Comm. 2017, 8 (5), 1037–1045.
105. Mitrea, C.; Taghavi, Z.; Bokanizad, B.; Hanoudi, S.; Tagett, R.; Donato, M.; Voichita,
C.; Draghici, S. Methods and Approaches in the Topology-Based Analysis of Biological
Pathways. Front. Physiol. 2013, 4, 278.
106. Ackermann, M.; Strimmer, K. A General Modular Framework for Gene Set Enrichment
Analysis. BMC Bioinform. 2009, 10, 47.
107. Gentleman, R. C.; Carey, V. J.; Bates, D. M.; Bolstad, B.; Dettling, M.; Dudoit, S.;
, Ellis, B.; Gautier, L.; Ge, Y.; Gentry, J.; Hornik, K. Bioconductor: Open Software
Development for Computational Biology and Bioinformatics. Genome Biol. 2004, 5
(10), 1–6.
108. Dabbish, L.; Stuart, C.; Tsay, J.; Herbsleb, J. In Social Coding in Github: Transparency
and Collaboration in an Open Software Repository, Proceedings of the ACM Conference
on Computer Supported Cooperative Work (New York), 2012; pp 1277–1286.
109. Rath, B.; Qais, F. A.; Patro, R.; Mohapatra, S.; Sharma, T. Design, Synthesis and
Molecular Modeling Studies of Novel Mesalamine Linked Coumarin for Treatment of
Inflammatory Bowel Disease. Bioorganic Med. Chem. Lett. 2021, 41, 128029.
110. Klambauer, G.; Sepp, H.; Matthias, R. Machine Learning in Drug Discovery. J. Chem.
Inf. Model 2019, 59 (3), 945–946.
111. Patel, L.; Shukla, T.; Huang, X.; Ussery, D. W.; Wang, S. Machine Learning Methods in
Drug Discovery. Molecules 2020, 25 (22), 5277.
112. Zhang, L.; Tan, J; Han, D.; Zhu, H. From Machine Learning to Deep Learning: Progress
in Machine Intelligence for Rational Drug Discovery. Drug Discov. Today 2017, 22
(11), 1680–1685.
113. Hughes, J. P.; Rees, S.; Kalindjian, S. B.; Philpott, K. L. Principles of Early Drug
Discovery. Br. J. Pharmacol. 2011, 162 (6), 1239–1249.
114. Schenone, M.; Dančík, V.; Wagner, B. K.; Clemons, P. A. Target Identification and
Mechanism of Action in Chemical Biology and Drug Discovery. Nat. Chem. Biol. 2013,
9 (4), 232–240.
115. Croston, G. E. The Utility of Target-Based Discovery. Expert Opin. Drug Discov.
2017, 12 (5), 427–429.
116. Lionta, E.; Spyrou, G. K.; Vassilatis, D.; Cournia, Z. Structure-Based Virtual Screening
for Drug Discovery: Principles, Applications and Recent Advances. Curr. Top. Med.
Chem . 2014, 14 (16), 1923–1938.
117. Paul, D.; Sanap, G.; Shenoy, S.; Kalyane, D.; Kalia, K.; Tekade, R. K. Artificial
Intelligence in Drug Discovery and Development. Drug Discov. Today 2021, 26 (1), 80.
118. Ghose, A. K.; Herbertz, T.; Pippin, D. A; Salvino, J. M.; Mallamo, J. P. Knowledge
Based Prediction of Ligand Binding Modes and Rational Inhibitor Design for Kinase
Drug Discovery. J. Med. Chem. 2008, 51 (17), 5149–5171.
119. Ghose, A. K.; Herbertz, T.; Pippin, D. A; Salvino, J. M.; Mallamo, J. P. Knowledge-
Based Chemoinformatic Approaches to Drug Discovery. Drug Discov. Today 2006, 11
(23–24), 1107–1114.
120. Szymański, P.; Markowicz, M.; Mikiciuk-Olasik, E. Adaptation of High-Throughput
Данная книга находится в списке для перевода на русский язык сайта https://meduniver.com/
Screening in Drug Discovery—Toxicological Screening Tests. Int. J. Mol. Sci. 2012,
13 (1), 427–452.
121. Atanasov, A. G.; Zotchev, S. B.; Dirsch, V. M.; Supuran, C. T. Natural Products in Drug
Discovery: Advances and Opportunities. Nat. Rev. Drug Discov. 2021, 20 (3), 200–216.
122. Hu, L.; Chen, G.; Chau, R. M. A Neural Networks-Based Drug Discovery Approach and
its Application for Designing Aldose Reductase Inhibitors. J. Mol. Graph. Model 2006,
24 (4), 244–253.
123. Subramanian, I.; Verma, S.; Kumar, S.; Jere, A.; Anamika, K. Multi-omics Data
Integration, Interpretation, and Its Application. Bioinform. Biol. Insights 2020, 14,
1177932219899051.
124. Fotis, C.; Antoranz, A.; Hatziavramidis, D.; Sakellaropoulos, T.; Alexopoulos, L. G.
Network-Based Technologies for Early Drug Discovery. Drug Discov. Today 2018, 23
(3), 626–635.
125. Talevi, A. Multi-Target Pharmacology: Possibilities and Limitations of the “Skeleton
Key Approach” From a Medicinal Chemist Perspective. Front. Pharmacol. 2015, 6,
205.
126. Ramsay, R. R.; Popovic-Nikolic, M. R.; Nikolic, K.; Uliassi, E.; Bolognesi, M. L. A
Perspective on Multi-Target Drug Discovery and Design for Complex Diseases. Clin.
Transl. Med. 2018, 7 (1), 1–14.
127. Newman, D. J.; Cragg, G. M. Natural Products as Sources of New Drugs Over the
Nearly Four Decades From 01/1981 to 09/2019. J. Nat. Pr od. 2020, 83 (3), 770–803.
128. Sliwoski, G.; Kothiwale, S.; Meiler, J.; Lowe, E. W. Computational Methods in Drug
Discovery. Pharmacol. Rev . 2014, 66 (1), 334–395.
129. Langedijk, J.; Mantel-Teeuwisse, A. K.; Slijkerman, D. S.; Schutjens, M. H. Drug
Repositioning and Repurposing: Terminology and Definitions in Literature. Drug
Discov. Today 2015, 20 (8), 1027–1034.
130. Adasme, M. F.; Parisi, D.; Sveshnikova, A.; Schroeder, M. Structure-Based Drug
Repositioning: Potential and Limits. Semin. Cancer Biol. 2021, 68, 192–198.
131. Xue, H.; Li, J.; Xie, H.; Wang, Y. Review of Drug Repositioning Approaches and
Resources. Int. J. Biol. Sci . 2018, 14 (10), 1232.
132. Saberian, N.; Peyvandipour, A.; Donato, M.; Ansari, S.; Draghici, S. A New
Computational Drug Repurposing Method Using Established Disease–Drug Pair
Knowledge. Bioinformatics 2019, 35 (19), 3672–3678.
133. Bymaster, F. P.; Katner, J. S.; Nelson, D. L.; Hemrick-Luecke, S. K.; Threlkeld,
P. G.; Heiligenstein, J. H.; Morin, S. M.; Gehlert, D. R.; Perry, K. W. Atomoxetine
Increases Extracellular Levels of Norepinephrine and Dopamine in Prefrontal Cortex
of Rat: A Potential Mechanism for Efficacy in Attention Deficit/Hyperactivity
Disorder. Neuropsychopharmacology 2002, 27 (5), 699–711.
134. Ferry, L.; Johnston, J. A. Efficacy and Safety of Bupropion SR for Smoking Cessation:
Data From Clinical Trials and Five Years of Postmarketing Experience. Int. J. Clin.
Pract. 2003, 57 (3), 224–230.
135. Raskin, J.; Pritchett, Y. L.; Wang, F.; D'Souza, D. N.; Waninger, A. L.; Iyengar, S.;
Wernicke, J. F. A Double-Blind, Randomized Multicenter Trial Comparing Duloxetine
With Placebo in the Management of Diabetic Peripheral Neuropathic Pain. Pain
Med. 2005, 6 (5), 346–356.
Drug Repurposing and Computational Drug Discovery: Strategies and Advances
136. McMahon, C. G.; Althof, S. E.; Kaufman, J. M.; Buvat, J.; Levine, S. B.; Aquilina, J. W.;
Tesfaye, F.; Rothman, M.; Rivas, D. A.; Porst, H. Efficacy and Safety of Dapoxetine for
the Treatment of Premature Ejaculation: Integrated Analysis of Results From Five Phase
3 Trials. J. Sex Med. 2011, 8 (2), 524–539.
137. Yao, J. C.; Shah, M. H.; Ito, T.; Bohas, C. L; Wolin, E. M.; Van Cutsem, E.; Hobday, T.
J.; Okusaka, T.; Capdevila, J.; De Vries, E. G.; Tomassetti, P. Everolimus for Advanced
Pancreatic Neuroendocrine Tumors. N. Engl. J. Med. 2011, 364 (6), 514–523.
138. Cohen, L. S.; Miner, C.; Brown, E.; Freeman, E. W.; Halbreich, U.; Sundell, K., McCray,
S. Premenstrual Daily Fluoxetine for Premenstrual Dysphoric Disorder: A Placebo-
Controlled, Clinical Trial Using Computerized Diaries. Obstet. Gynecol. 2002, 100 (3),
435–444.
139. Heinrich, M. C.; Corless, C. L.; Demetri, G. D.; Blanke, C.; Von Mehren. M.; Joensuu,
H.; McGreevey, L. S.; Chen, C. J.; Van den Abbeele, A. D.; Druker, B. J.; Kiese, B.
Kinase Mutations and Imatinib Response in Patients With Metastatic Gastrointestinal
Stromal Tumor. Clin. Oncol. 2003, 21 (23), 4342–4349.
140. Fleseriu, M.; Biller, B. M.; Findling, J. W.; Molitch, M. E.; Schteingart, D. E.; Gross,
C. SEISMIC Study Investigators, SEISMIC Study Investigators Include. Mifepristone,
A glucocorticoid Receptor Antagonist, Produces Clinical and Metabolic Benefits in
Patients With Cushing's Syndrome. J. Clin. Endocrinol. 2012, 97 (6), 2039–2049.
141. Vitton, O.; Gendreau, M.; Gendreau, J.; Kranzler, J.; Rao, S. G. A Double-Blind Placebo-
Controlled Trial of Milnacipran in the Treatment of Fibromyalgia. Hum. Psychopharmacol.
2004, 19 (S1), S27–S35.
142. Tepe, G.; Zeller, T.; Albrecht, T.; Heller, S.; Schwarzwälder, U.; Beregi, J. P.; Claussen,
M.C.; Oldenburg, A.; Scheller, B.; Speck, U. Local Delivery of Paclitaxel to Inhibit
Restenosis During Angioplasty of the Leg. N. Engl. J. Med. 2008, 358 (7), 689–699.
143. Diener, H. C.; Tfelt-Hansen, P.; Dahlöf, C.; Láinez, M. J.; Sandrini, G.; Wang, S. J.;
Neto, W.; Vijapurkar, U.; Doyle, A.; Jacobs, D. Topiramate in Migraine Prophylaxis. J.
Neurol. 2004, 251 (8), 943–950.
144. Tek, C. Naltrexone HCI/Bupropion HCI for Chronic Weight Management in Obese
Adults: Patient Selection and Perspectives. Patient Prefer. Adherence 2016, 10, 751.
145. Jarada, T. N.; Rokne, J. G.; Alhajj, R. A Review of Computational Drug Repositioning:
Strategies, Approaches, Opportunities, Challenges, and Directions. J. Cheminformatics
2020, 12 (1), 1–23.
146. Schaduangrat, N.; Lampa, S.; Simeon, S.; Gleeson, M. P.; Spjuth, O. Nantasenamat,
C. Towards Reproducible Computational Drug Discovery. J. Cheminformatics 2020,
12 (1), 1–30.
Drug Repurposing and Computational Drug Discovery: Strategies and Advances.
Mithun Rudrapal, PhD (Ed)
© 2024 Apple Academic Press, Inc. Co-published with CRC Press (Taylor & Francis)
Данная книга находится в списке для перевода на русский язык сайта https://meduniver.com/
Drug Repurposing and Computational
Drug Discovery for Viral Infections and
Coronavirus Disease-2019 (COVID-19)
SIDDHARTHA MAJI1, VISHNU NAYAK BADAVATH2, and
SWASTIKA GANGULY
1
2
3
3
ABSTRACT
Since the pandemic created by the novel coronavirus (SARS-CoV-2), no
exact therapeutic options came to light except some vaccines and one or two
drugs which are still under clinical trials. Computational techniques including
the computer-aided drug design model (CADD) which has been proven to be
a best starting point in the development of drugs for different diseases with
a fast and fruitful outcome is also the best possible way to design a potential
drug for SARS-CoV-2. Such approaches have been previously used in many
viral infectious diseases like Hepatitis C virus (HCV), Ebola virus, influenza, and HIV. Recent clinical studies have revealed that the use of several
repurposed drugs as well as multi-targeted drugs can be a successful and
promising approach to inhibit virus such as lopinavir, remdesivir, ribavirin,
Соседние файлы в папке Библиотека им академика М.И. Перельмана