Advertisement

In Silico Molecular Modelling: Key Technologies in the Drug Discovery Process to Combat Multidrug Resistance

  • Garima Saxena
  • Mala Sharma
  • Faria Fatima
  • Preeti BajpaiEmail author
  • Salman AkhtarEmail author
Chapter

Abstract

Drug discovery using advanced computational biology approaches is an emerging field in medical science and holds the promise towards identification of new drugs. The multidrug resistance in bacterial strains is a matter of serious concern specifically related to the pathogens associated with public health. Numerous strategies have been developed in the recent past to combat the MDR concerns. However, still due to upcoming new evolution mechanisms of bacterial strains, the issue has been addressed only to a limited extent. Pertaining to the limitations of molecular techniques, multiple in silico approaches are in trend with great advancements. This chapter is focused toward the description on several in silico techniques for drug discovery with an idea of target identification, namely, virtual screening, molecular docking, MD simulation, QSAR and pharmacophore modelling. In addition to multi-target identification, the structural genomics has also been illustrated which involves the three-dimensional structure predictions of proteins for better understanding to design drugs against MDR.

Keywords

Target identification Virtual screening Molecular docking MD simulation QSAR Pharmacophore modelling Multi-target identification Structural genomics 

References

  1. Abate, G., & Hoft, D. F. (2016). Immunotherapy for tuberculosis: Future prospects. Immuno Targets and therapy, 5, 37.Google Scholar
  2. Agarwal, S., Chadha, D., & Mehrotra, R. (2015). Molecular modeling and spectroscopic studies of semustine binding with DNA and its comparison with lomustine–DNA adduct formation. Journal of Biomolecular Structure and Dynamics, 33(8), 1653–1668.PubMedCrossRefPubMedCentralGoogle Scholar
  3. Ahmad, S., & Mokaddas, E. (2010). Recent advances in the diagnosis and treatment of multidrug-resistant tuberculosis. Respiratory Medicine CME, 3(2), 51–61.CrossRefGoogle Scholar
  4. Alder, B. J., & Wainwright, T. E. (1959). Studies in molecular dynamics. I. General method. The Journal of Chemical Physics, 31(2), 459–466.CrossRefGoogle Scholar
  5. Altschul, S. F., Madden, T. L., Schäffer, A. A., Zhang, J., Zhang, Z., Miller, W., & Lipman, D. J. (1997). Gapped BLAST and PSI-BLAST: A new generation of protein database search programs. Nucleic Acids Research, 25(17), 3389–3402.PubMedPubMedCentralCrossRefGoogle Scholar
  6. Arabnia, H. R., & Tran, Q. N. (2015). Emerging trends in computational biology, bioinformatics, and systems biology: Algorithms and software tools. Morgan Kaufmann.Google Scholar
  7. Bairoch, A., & Apweiler, R. (2000). The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000. Nucleic Acids Research, 28(1), 45–48.PubMedPubMedCentralCrossRefGoogle Scholar
  8. Bansal, A. K. (2005). Bioinformatics in microbial biotechnology–a mini review. Microbial Cell Factories, 4(1), 19.PubMedPubMedCentralCrossRefGoogle Scholar
  9. Bernal, P., Molina-Santiago, C., Daddaoua, A., & Llamas, M. A. (2013). Antibiotic adjuvants: Identification and clinical use. Microbial Biotechnology, 6(5), 445–449.PubMedPubMedCentralCrossRefGoogle Scholar
  10. Bush, K., & Jacoby, G. A. (2010). Updated functional classification of β-lactamases. Antimicrobial Agents and Chemotherapy, 54(3), 969–976.PubMedCrossRefPubMedCentralGoogle Scholar
  11. Carriço, J. A., Sabat, A. J., Friedrich, A. W., & Ramirez, M. (2013). Bioinformatics in bacterial molecular epidemiology and public health: Databases, tools and the next-generation sequencing revolution. Eurosurveillance, 18(4), 20382.PubMedCrossRefPubMedCentralGoogle Scholar
  12. Case, D. A. (2002). Molecular dynamics and NMR spin relaxation in proteins. Accounts of Chemical Research, 35(6), 325–331.PubMedCrossRefPubMedCentralGoogle Scholar
  13. Case, D. A., Cheatham, T. E., Darden, T., Gohlke, H., Luo, R., Merz, K. M., et al. (2005). The Amber biomolecular simulation programs. Journal of Computational Chemistry, 26(16), 1668–1688.PubMedPubMedCentralCrossRefGoogle Scholar
  14. Chakraborty, A. K. (2016). Multi-drug resistant genes in bacteria and 21st century problems associated with antibiotic therapy. Biotechnology Indian Journal, 12(12), 113.Google Scholar
  15. Chen, H., Yu, R.-G., Yin, N.-N., & Zhou, J.-X. (2014). Combination of extracorporeal membrane oxygenation and continuous renal replacement therapy in critically ill patients: A systematic review. Critical Care, 18(6), 675.PubMedPubMedCentralCrossRefGoogle Scholar
  16. Chen, M.-Y., Lo, Y.-C., Chen, W.-C., Wang, K.-F., & Chan, P.-C. (2017). Recurrence after successful treatment of multidrug-resistant tuberculosis in Taiwan. PLoS One, 12(1), e0170980.PubMedPubMedCentralCrossRefGoogle Scholar
  17. Cheng, T., Li, Q., Zhou, Z., Wang, Y., & Bryant, S. H. (2012). Structure-based virtual screening for drug discovery: A problem-centric review. The AAPS Journal, 14(1), 133–141.PubMedPubMedCentralCrossRefGoogle Scholar
  18. Clewell, D. B. (2014). Antibiotic resistance plasmids in bacteria. eLS.Google Scholar
  19. Daggett, V. (2006). Protein folding− simulation. Chemical Reviews, 106(5), 1898–1916.PubMedCrossRefPubMedCentralGoogle Scholar
  20. Deeb, O., & Goodarzi, M. (2012). In silico quantitative structure toxicity relationship of chemical compounds: Some case studies. Current Drug Safety, 7(4), 289–297.PubMedCrossRefPubMedCentralGoogle Scholar
  21. Divakar, S., & Hariharan, S. (2015). 3D-QSAR studies on plasmodium falciparam proteins: A mini-review. Combinatorial Chemistry & High Throughput Screening, 18(2), 188–198.CrossRefGoogle Scholar
  22. Drawz, S. M., & Bonomo, R. A. (2010). Three decades of beta-lactamase inhibitors. Clin Microbiol Rev, 23, 160–201.PubMedPubMedCentralCrossRefGoogle Scholar
  23. Dror, R. O., Dirks, R. M., Grossman, J. P., Xu, H., & Shaw, D. E. (2012). Biomolecular simulation: a computational microscope for molecular biology. Annual Review of Biophysics, 41, 429–452.PubMedCrossRefPubMedCentralGoogle Scholar
  24. Duan, Y., Wu, C., Chowdhury, S., Lee, M. C., Xiong, G., Zhang, W., et al. (2003). A point-charge force field for molecular mechanics simulations of proteins based on condensed-phase quantum mechanical calculations. Journal of Computational Chemistry, 24(16), 1999–2012.PubMedCrossRefPubMedCentralGoogle Scholar
  25. Fang, C., & Xiao, Z. (2016). Receptor-based 3D-QSAR in drug design: Methods and applications in kinase studies. Current Topics in Medicinal Chemistry, 16(13), 1463–1477.PubMedCrossRefPubMedCentralGoogle Scholar
  26. Foloppe, N., & MacKerell, A. D., Jr. (2000). All-atom empirical force field for nucleic acids: I. parameter optimization based on small molecule and condensed phase macromolecular target data. Journal of Computational Chemistry, 21(2), 86–104.CrossRefGoogle Scholar
  27. Forsberg, K. J., Reyes, A., Wang, B., Selleck, E. M., Sommer, M. O. A., & Dantas, G. (2012). The shared antibiotic resistome of soil bacteria and human pathogens. Science, 337(6098), 1107–1111.PubMedPubMedCentralCrossRefGoogle Scholar
  28. Gibson, G., & Muse, S. V. (2002). A primer of genome science (Vol. 1). Sinauer Sunderland.Google Scholar
  29. Gschwend, D. A., Good, A. C., & Kuntz, I. D. (1996). Molecular docking towards drug discovery. Journal of Molecular Recognition: An Interdisciplinary Journal, 9(2), 175–186.CrossRefGoogle Scholar
  30. Guedes, I. A., de Magalhães, C. S., & Dardenne, L. E. (2014). Receptor–ligand molecular docking. Biophysical Reviews, 6(1), 75–87.PubMedCrossRefPubMedCentralGoogle Scholar
  31. Güner, O. F., & Bowen, J. P. (2014). Setting the record straight: The origin of the pharmacophore concept. Journal of Chemical Information and Modeling, 54(5), 1269–1283.PubMedCrossRefPubMedCentralGoogle Scholar
  32. Gupta, C. L., Akhtar, S., & Bajpai, P. (2014). IN SILICO protein modeling: Possibilities and limitations. EXCLI, 13, 513–515.Google Scholar
  33. Halgren, T. A., Murphy, R. B., Friesner, R. A., Beard, H. S., Frye, L. L., Pollard, W. T., & Banks, J. L. (2004). Glide: A new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. Journal of Medicinal Chemistry, 47(7), 1750–1759.PubMedCrossRefPubMedCentralGoogle Scholar
  34. Henry, B. D., Neill, D. R., Becker, K. A., Gore, S., Bricio-Moreno, L., Ziobro, R., et al. (2015). Engineered liposomes sequester bacterial exotoxins and protect from severe invasive infections in mice. Nature Biotechnology, 33(1), 81.PubMedCrossRefPubMedCentralGoogle Scholar
  35. Hogeweg, P. (2011). The roots of bioinformatics in theoretical biology. PLoS Computational Biology, 7(3), e1002021.PubMedPubMedCentralCrossRefGoogle Scholar
  36. Hopkins, A. L., & Groom, C. R. (2002). The druggable genome. Nature Reviews Drug Discovery, 1(9), 727.PubMedCrossRefPubMedCentralGoogle Scholar
  37. Jacoby, G. A. (2009). AmpC β-lactamases. Clinical Microbiology Reviews, 22(1), 161–182.PubMedPubMedCentralCrossRefGoogle Scholar
  38. Jiang, Z., & Zhou, Y. (2005). Using bioinformatics for drug target identification from the genome. American Journal of Pharmacogenomics, 5(6), 387–396.PubMedCrossRefPubMedCentralGoogle Scholar
  39. Keen, E. C., & Adhya, S. L. (2015). Phage therapy: Current research and applications. Oxford: Oxford University Press.Google Scholar
  40. Khedkar, S. A., Malde, A. K., Coutinho, E. C., & Srivastava, S. (2007). Pharmacophore modeling in drug discovery and development: An overview. Medicinal Chemistry, 3(2), 187–197.PubMedCrossRefPubMedCentralGoogle Scholar
  41. Kho, A. N., Dexter, P. R., Warvel, J. S., Belsito, A. W., Commiskey, M., Wilson, S. J., et al. (2008). An effective computerized reminder for contact isolation of patients colonized or infected with resistant organisms. International Journal of Medical Informatics, 77(3), 194–198.PubMedCrossRefPubMedCentralGoogle Scholar
  42. King, D. T., & Strynadka, N. C. J. (2013). Targeting metallo-β-lactamase enzymes in antibiotic resistance. Future Medicinal Chemistry, 5(11), 1243–1263.PubMedCrossRefPubMedCentralGoogle Scholar
  43. Kramer, B., Metz, G., Rarey, M., & Lengauer, T. (1999). Part 1–Docking and scoring: Methods development-LIGAND DOCKING AND SCREENING WITH FLEXX. Medicinal Chemistry Research, 9(7–8), 463–478.Google Scholar
  44. Kukol, A. (2008). Molecular modeling of proteins (Vol. 443). Springer.Google Scholar
  45. Kwang, L. S. (2005). In silico high-throughput screening for ADME/Tox properties: PreADMET program. In Abstracts Conference Combinational Chemistry Japan (Vol. 21, pp. 22–28).Google Scholar
  46. Lavecchia, A., & Di Giovanni, C. (2013). Virtual screening strategies in drug discovery: A critical review. Current Medicinal Chemistry, 20(23), 2839–2860.PubMedCrossRefPubMedCentralGoogle Scholar
  47. Leach, A. R., & Gillet, V. J. (2007). An introduction to chemoinformatics.. Springer Science & Business Media.CrossRefGoogle Scholar
  48. Lengauer, T., & Rarey, M. (1996). Computational methods for biomolecular docking. Current Opinion in Structural Biology, 6(3), 402–406.PubMedCrossRefPubMedCentralGoogle Scholar
  49. Lill, M. A. (2007). Multi-dimensional QSAR in drug discovery. Drug Discovery Today, 12(23–24), 1013–1017.PubMedCrossRefPubMedCentralGoogle Scholar
  50. Lim, C., Takahashi, E., Hongsuwan, M., Wuthiekanun, V., Thamlikitkul, V., Hinjoy, S., et al. (2016). Epidemiology and burden of multidrug-resistant bacterial infection in a developing country. eLife, 5.Google Scholar
  51. Lynch, J. P., III, Clark, N. M., & Zhanel, G. G. (2013). Evolution of antimicrobial resistance among Enterobacteriaceae (focus on extended spectrum β-lactamases and carbapenemases). Expert Opinion on Pharmacotherapy, 14(2), 199–210.PubMedCrossRefPubMedCentralGoogle Scholar
  52. Lyne, P. D. (2002). Structure-based virtual screening: An overview. Drug Discovery Today, 7(20), 1047–1055.PubMedCrossRefPubMedCentralGoogle Scholar
  53. Mannhold, R., Kubinyi, H., & Folkers, G. (2006). High-throughput screening in drug discovery (Vol. 35). Wiley.Google Scholar
  54. Mason, J. S., Morize, I., Menard, P. R., Cheney, D. L., Hulme, C., & Labaudiniere, R. F. (1999). New 4-point pharmacophore method for molecular similarity and diversity applications: Overview of the method and applications, including a novel approach to the design of combinatorial libraries containing privileged substructures. Journal of Medicinal Chemistry, 42(17), 3251–3264.PubMedCrossRefPubMedCentralGoogle Scholar
  55. McArthur, A. G., Waglechner, N., Nizam, F., Yan, A., Azad, M. A., Baylay, A. J., et al. (2013). The comprehensive antibiotic resistance database. Antimicrobial Agents and Chemotherapy, 57(7), 3348–3357.PubMedPubMedCentralCrossRefGoogle Scholar
  56. Medzhitov, R., Schneider, D. S., & Soares, M. P. (2012). Disease tolerance as a defense strategy. Science, 335(6071), 936–941.PubMedPubMedCentralCrossRefGoogle Scholar
  57. Miller, M. B., & Bassler, B. L. (2001). Quorum sensing in bacteria. Annual Reviews in Microbiology, 55(1), 165–199.CrossRefGoogle Scholar
  58. Morris, G. M., Huey, R., Lindstrom, W., Sanner, M. F., Belew, R. K., Goodsell, D. S., & Olson, A. J. (2009). AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. Journal of Computational Chemistry, 30(16), 2785–2791.PubMedPubMedCentralCrossRefGoogle Scholar
  59. Nikaido, H. (2009). Multidrug resistance in bacteria. Annual Review of Biochemistry, 78, 119–146.PubMedPubMedCentralCrossRefGoogle Scholar
  60. Nobili, S., Landini, I., Mazzei, T., & Mini, E. (2012). Overcoming tumor multidrug resistance using drugs able to evade P-glycoprotein or to exploit its expression. Medicinal Research Reviews, 32(6), 1220–1262.PubMedCrossRefPubMedCentralGoogle Scholar
  61. Okeke, I. N., Laxminarayan, R., Bhutta, Z. A., Duse, A. G., Jenkins, P., O’Brien, T. F., et al. (2005). Antimicrobial resistance in developing countries. Part I: Recent trends and current status. The Lancet Infectious Diseases, 5(8), 481–493.PubMedCrossRefPubMedCentralGoogle Scholar
  62. Opal, S. M. (2016). Non-antibiotic treatments for bacterial diseases in an era of progressive antibiotic resistance. BioMed Central.Google Scholar
  63. Ordonez, A. A., Weinstein, E. A., Bambarger, L. E., Saini, V., Chang, Y. S., DeMarco, V. P., et al. (2017). A systematic approach for developing bacteria-specific imaging tracers. Journal of Nuclear Medicine, 58(1), 144.PubMedPubMedCentralCrossRefGoogle Scholar
  64. Palzkill, T. (2013). Metallo-β-lactamase structure and function. Annals of the New York Academy of Sciences, 1277(1), 91–104.PubMedCrossRefPubMedCentralGoogle Scholar
  65. Parrinello, M., & Rahman, A. (1981). Polymorphic transitions in single crystals: A new molecular dynamics method. Journal of Applied Physics, 52(12), 7182–7190.CrossRefGoogle Scholar
  66. Patodia, S., Bagaria, A., & Chopra, D. (2014). Molecular dynamics simulation of proteins: A brief overview. Journal of Physical Chemistry & Biophysics, 4(6), 1.CrossRefGoogle Scholar
  67. Paulsen, I. T. (2003). Multidrug efflux pumps and resistance: Regulation and evolution. Current Opinion in Microbiology, 6(5), 446–451.PubMedCrossRefPubMedCentralGoogle Scholar
  68. Perumal, D., Lim, C. S., & Sakharkar, M. K. (2008). Microbial drug target identification using different computational approaches: Specific application to Pseudomonas aeruginosa. In Innovations in Information Technology, 2008. IIT 2008. International Conference on (pp. 135–139). IEEE.Google Scholar
  69. Petrenko, R., & Meller, J. (2010). Molecular dynamics. eLS.Google Scholar
  70. Phillips, J. C., Braun, R., Wang, W., Gumbart, J., Tajkhorshid, E., Villa, E., et al. (2005). Scalable molecular dynamics with NAMD. Journal of Computational Chemistry, 26(16), 1781–1802.PubMedPubMedCentralCrossRefGoogle Scholar
  71. Pinner, U. K. (2007). Contributors to volume 4. Comprehensive medicinal chemistry II, 2.Google Scholar
  72. Pirhadi, S., Shiri, F., & Ghasemi, J. B. (2013). Methods and applications of structure based pharmacophores in drug discovery. Current Topics in Medicinal Chemistry, 13(9), 1036–1047.PubMedCrossRefPubMedCentralGoogle Scholar
  73. Pittet, D., Safran, E., Harbarth, S., Borst, F., Copin, P., Rohner, P., et al. (1996). Automatic alerts for methicillin-resistant Staphylococcus aureus surveillance and control: Role of a hospital information system. Infection Control & Hospital Epidemiology, 17(8), 496–502.CrossRefGoogle Scholar
  74. Pronk, S., Páll, S., Schulz, R., Larsson, P., Bjelkmar, P., Apostolov, R., et al. (2013). GROMACS 4.5: A high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics, 29(7), 845–854.PubMedPubMedCentralCrossRefGoogle Scholar
  75. Qing, X., Lee, X. Y., De Raeymaecker, J., Tame, J. R. H., Zhang, K. Y. J., De Maeyer, M., & Voet, A. (2014). Pharmacophore modeling: Advances, limitations, and current utility in drug discovery.Google Scholar
  76. Rasmussen, A. L., Okumura, A., Ferris, M. T., Green, R., Feldmann, F., Kelly, S. M., et al. (2014). Host genetic diversity enables Ebola hemorrhagic fever pathogenesis and resistance. Science, 346(6212), 987–991.PubMedPubMedCentralCrossRefGoogle Scholar
  77. Rohs, R., Bloch, I., Sklenar, H., & Shakked, Z. (2005). Molecular flexibility in ab initio drug docking to DNA: Binding-site and binding-mode transitions in all-atom Monte Carlo simulations. Nucleic Acids Research, 33(22), 7048–7057.PubMedPubMedCentralCrossRefGoogle Scholar
  78. Roy, K., Kar, S., & Das, R. N. (2015). Understanding the basics of QSAR for applications in pharmaceutical sciences and risk assessment. London: Academic press.Google Scholar
  79. Saeb, A. T. M., Abouelhoda, M., Selvaraju, M., Althawadi, S. I., Mutabagani, M., Adil, M., et al. (2017). The use of next-generation sequencing in the identification of a fastidious pathogen: A lesson from a clinical setup. Evolutionary Bioinformatics, 13, 1176934316686072.CrossRefGoogle Scholar
  80. Schiffelers, R. M., Storm, G., & Bakker-Woudenberg, I. A. J. M. (2001). Therapeutic efficacy of liposomal gentamicin in clinically relevant rat models. International Journal of Pharmaceutics, 214(1–2), 103–105.PubMedCrossRefPubMedCentralGoogle Scholar
  81. Schuler, L. D., Daura, X., & Van Gunsteren, W. F. (2001). An improved GROMOS96 force field for aliphatic hydrocarbons in the condensed phase. Journal of Computational Chemistry, 22(11), 1205–1218.CrossRefGoogle Scholar
  82. Seeliger, D., & de Groot, B. L. (2010). Ligand docking and binding site analysis with PyMOL and Autodock/Vina. Journal of Computer-Aided Molecular Design, 24(5), 417–422.PubMedPubMedCentralCrossRefGoogle Scholar
  83. Shenoi, S., Heysell, S., Moll, A., & Friedland, G. (2009). Multidrug-resistant and extensively drug-resistant tuberculosis: Consequences for the global HIV community. Current Opinion in Infectious Diseases, 22(1), 11.PubMedPubMedCentralCrossRefGoogle Scholar
  84. Shin, W.-H., Zhu, X., Bures, M. G., & Kihara, D. (2015). Three-dimensional compound comparison methods and their application in drug discovery. Molecules, 20(7), 12841–12862.PubMedPubMedCentralCrossRefGoogle Scholar
  85. Solt, I., Tomin, A., & Niesz, K. (n.d.). New approaches to virtual screening wed, 12/18/2013–3: 10pm.Google Scholar
  86. Sulakvelidze, A., Alavidze, Z., & Morris, J. G. (2001). Bacteriophage therapy. Antimicrobial Agents and Chemotherapy, 45(3), 649–659.PubMedPubMedCentralCrossRefGoogle Scholar
  87. Tan, W., Mei, H., Chao, L., Liu, T., Pan, X., Shu, M., & Yang, L. (2013). Combined QSAR and molecule docking studies on predicting P-glycoprotein inhibitors. Journal of Computer-Aided Molecular Design, 27(12), 1067–1073.PubMedCrossRefPubMedCentralGoogle Scholar
  88. Todeschini, R., Consonni, V., & Gramatica, P. (n.d.). 4.05 Chemometrics in QSAR.Google Scholar
  89. Unger, S. H., & Hansch, C. (1975). Quantitative models of steric effects. Progress in Physical Organic Chemistry, 12, 91–118.Google Scholar
  90. Verma, J., Khedkar, V. M., & Coutinho, E. C. (2010). 3D-QSAR in drug design-a review. Current Topics in Medicinal Chemistry, 10(1), 95–115.PubMedCrossRefPubMedCentralGoogle Scholar
  91. Wang, Y., Chiu, J.-F., & He, Q.-Y. (2009). Genomics and proteomics in drug design and discovery. In Pharmacology (pp. 561–573). Elsevier.Google Scholar
  92. Wang, T., Wu, M.-B., Lin, J.-P., & Yang, L.-R. (2015). Quantitative structure–activity relationship: Promising advances in drug discovery platforms. Expert Opinion on Drug Discovery, 10(12), 1283–1300.PubMedCrossRefPubMedCentralGoogle Scholar
  93. Worthington, R. J., & Melander, C. (2013). Combination approaches to combat multidrug-resistant bacteria. Trends in Biotechnology, 31(3), 177–184.PubMedPubMedCentralCrossRefGoogle Scholar
  94. Wu, J.-H., Chen, Y.-C., Hsieh, S., Lin, H.-C., Chen, Y.-Y., Cheng, P.-H., et al. (2009). Real-time automated MDRO surveillance system. In BIOCOMP (pp. 764–769).Google Scholar
  95. Yu, W., & MacKerell, A. D. (2017). Computer-aided drug design methods. In Antibiotics (pp. 85–106). Springer.Google Scholar
  96. Zewdie, O., Mihret, A., Abebe, T., Kebede, A., Desta, K., Worku, A., & Ameni, G. (2018). Genotyping and molecular detection of multidrug-resistant Mycobacterium tuberculosis among tuberculosis lymphadenitis cases in Addis Ababa, Ethiopia. New Microbes and New Infections, 21, 36–41.PubMedCrossRefPubMedCentralGoogle Scholar
  97. Zimmermann, G. R., Lehar, J., & Keith, C. T. (2007). Multi-target therapeutics: When the whole is greater than the sum of the parts. Drug Discovery Today, 12(1–2), 34–42.PubMedCrossRefPubMedCentralGoogle Scholar

PhD Thesis:

  1. Balaramnavar Vishalsinh Mohansinh (2015) Design and Synthesis of BMP Receptor agonist as anti osteoporotic and anti cancer agents and synthesis of some bioactive molecules. PhD thesis, Integral University, Lucknow, India.Google Scholar
  2. Jani, Mitesh H (2015) Synthesis and evaluation of some novel heterocyclic compounds of biological interest by rational approach. PhD Thesis, Gujrat University, India.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of BiosciencesIntegral UniversityLucknowIndia
  2. 2.Department of BioengineeringIntegral UniversityLucknowIndia
  3. 3.Integral Institute of Agricultural Science & TechnologyIntegral UniversityLucknowIndia

Personalised recommendations