Skip to main content

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

  • Chapter
  • First Online:
Antibacterial Drug Discovery to Combat MDR
  • 998 Accesses

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Abate, G., & Hoft, D. F. (2016). Immunotherapy for tuberculosis: Future prospects. Immuno Targets and therapy, 5, 37.

    Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • Ahmad, S., & Mokaddas, E. (2010). Recent advances in the diagnosis and treatment of multidrug-resistant tuberculosis. Respiratory Medicine CME, 3(2), 51–61.

    Article  Google Scholar 

  • Alder, B. J., & Wainwright, T. E. (1959). Studies in molecular dynamics. I. General method. The Journal of Chemical Physics, 31(2), 459–466.

    Article  CAS  Google Scholar 

  • 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Arabnia, H. R., & Tran, Q. N. (2015). Emerging trends in computational biology, bioinformatics, and systems biology: Algorithms and software tools. Morgan Kaufmann.

    Google Scholar 

  • Bairoch, A., & Apweiler, R. (2000). The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000. Nucleic Acids Research, 28(1), 45–48.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Bansal, A. K. (2005). Bioinformatics in microbial biotechnology–a mini review. Microbial Cell Factories, 4(1), 19.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Bernal, P., Molina-Santiago, C., Daddaoua, A., & Llamas, M. A. (2013). Antibiotic adjuvants: Identification and clinical use. Microbial Biotechnology, 6(5), 445–449.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Bush, K., & Jacoby, G. A. (2010). Updated functional classification of β-lactamases. Antimicrobial Agents and Chemotherapy, 54(3), 969–976.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • Case, D. A. (2002). Molecular dynamics and NMR spin relaxation in proteins. Accounts of Chemical Research, 35(6), 325–331.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • 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 

  • 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.

    Article  PubMed  PubMed Central  Google Scholar 

  • 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.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Clewell, D. B. (2014). Antibiotic resistance plasmids in bacteria. eLS.

    Google Scholar 

  • Daggett, V. (2006). Protein folding− simulation. Chemical Reviews, 106(5), 1898–1916.

    Article  CAS  PubMed  Google Scholar 

  • Deeb, O., & Goodarzi, M. (2012). In silico quantitative structure toxicity relationship of chemical compounds: Some case studies. Current Drug Safety, 7(4), 289–297.

    Article  CAS  PubMed  Google Scholar 

  • Divakar, S., & Hariharan, S. (2015). 3D-QSAR studies on plasmodium falciparam proteins: A mini-review. Combinatorial Chemistry & High Throughput Screening, 18(2), 188–198.

    Article  CAS  Google Scholar 

  • Drawz, S. M., & Bonomo, R. A. (2010). Three decades of beta-lactamase inhibitors. Clin Microbiol Rev, 23, 160–201.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Gibson, G., & Muse, S. V. (2002). A primer of genome science (Vol. 1). Sinauer Sunderland.

    Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • Guedes, I. A., de Magalhães, C. S., & Dardenne, L. E. (2014). Receptor–ligand molecular docking. Biophysical Reviews, 6(1), 75–87.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  PubMed  CAS  Google Scholar 

  • Gupta, C. L., Akhtar, S., & Bajpai, P. (2014). IN SILICO protein modeling: Possibilities and limitations. EXCLI, 13, 513–515.

    Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • Hogeweg, P. (2011). The roots of bioinformatics in theoretical biology. PLoS Computational Biology, 7(3), e1002021.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Hopkins, A. L., & Groom, C. R. (2002). The druggable genome. Nature Reviews Drug Discovery, 1(9), 727.

    Article  CAS  PubMed  Google Scholar 

  • Jacoby, G. A. (2009). AmpC β-lactamases. Clinical Microbiology Reviews, 22(1), 161–182.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Jiang, Z., & Zhou, Y. (2005). Using bioinformatics for drug target identification from the genome. American Journal of Pharmacogenomics, 5(6), 387–396.

    Article  CAS  PubMed  Google Scholar 

  • Keen, E. C., & Adhya, S. L. (2015). Phage therapy: Current research and applications. Oxford: Oxford University Press.

    Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • King, D. T., & Strynadka, N. C. J. (2013). Targeting metallo-β-lactamase enzymes in antibiotic resistance. Future Medicinal Chemistry, 5(11), 1243–1263.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    CAS  Google Scholar 

  • Kukol, A. (2008). Molecular modeling of proteins (Vol. 443). Springer.

    Google Scholar 

  • 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 

  • Lavecchia, A., & Di Giovanni, C. (2013). Virtual screening strategies in drug discovery: A critical review. Current Medicinal Chemistry, 20(23), 2839–2860.

    Article  CAS  PubMed  Google Scholar 

  • Leach, A. R., & Gillet, V. J. (2007). An introduction to chemoinformatics.. Springer Science & Business Media.

    Book  Google Scholar 

  • Lengauer, T., & Rarey, M. (1996). Computational methods for biomolecular docking. Current Opinion in Structural Biology, 6(3), 402–406.

    Article  CAS  PubMed  Google Scholar 

  • Lill, M. A. (2007). Multi-dimensional QSAR in drug discovery. Drug Discovery Today, 12(23–24), 1013–1017.

    Article  CAS  PubMed  Google Scholar 

  • 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 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • Lyne, P. D. (2002). Structure-based virtual screening: An overview. Drug Discovery Today, 7(20), 1047–1055.

    Article  CAS  PubMed  Google Scholar 

  • Mannhold, R., Kubinyi, H., & Folkers, G. (2006). High-throughput screening in drug discovery (Vol. 35). Wiley.

    Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Medzhitov, R., Schneider, D. S., & Soares, M. P. (2012). Disease tolerance as a defense strategy. Science, 335(6071), 936–941.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Miller, M. B., & Bassler, B. L. (2001). Quorum sensing in bacteria. Annual Reviews in Microbiology, 55(1), 165–199.

    Article  CAS  Google Scholar 

  • 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Nikaido, H. (2009). Multidrug resistance in bacteria. Annual Review of Biochemistry, 78, 119–146.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • Opal, S. M. (2016). Non-antibiotic treatments for bacterial diseases in an era of progressive antibiotic resistance. BioMed Central.

    Google Scholar 

  • 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Palzkill, T. (2013). Metallo-β-lactamase structure and function. Annals of the New York Academy of Sciences, 1277(1), 91–104.

    Article  CAS  PubMed  Google Scholar 

  • Parrinello, M., & Rahman, A. (1981). Polymorphic transitions in single crystals: A new molecular dynamics method. Journal of Applied Physics, 52(12), 7182–7190.

    Article  CAS  Google Scholar 

  • Patodia, S., Bagaria, A., & Chopra, D. (2014). Molecular dynamics simulation of proteins: A brief overview. Journal of Physical Chemistry & Biophysics, 4(6), 1.

    Article  CAS  Google Scholar 

  • Paulsen, I. T. (2003). Multidrug efflux pumps and resistance: Regulation and evolution. Current Opinion in Microbiology, 6(5), 446–451.

    Article  CAS  PubMed  Google Scholar 

  • 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 

  • Petrenko, R., & Meller, J. (2010). Molecular dynamics. eLS.

    Google Scholar 

  • 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Pinner, U. K. (2007). Contributors to volume 4. Comprehensive medicinal chemistry II, 2.

    Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • 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 

  • 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • 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 

  • 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.

    Article  CAS  PubMed Central  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • 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.

    Article  PubMed  PubMed Central  Google Scholar 

  • 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Solt, I., Tomin, A., & Niesz, K. (n.d.). New approaches to virtual screening wed, 12/18/2013–3: 10pm.

    Google Scholar 

  • Sulakvelidze, A., Alavidze, Z., & Morris, J. G. (2001). Bacteriophage therapy. Antimicrobial Agents and Chemotherapy, 45(3), 649–659.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • Todeschini, R., Consonni, V., & Gramatica, P. (n.d.). 4.05 Chemometrics in QSAR.

    Google Scholar 

  • Unger, S. H., & Hansch, C. (1975). Quantitative models of steric effects. Progress in Physical Organic Chemistry, 12, 91–118.

    Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • Worthington, R. J., & Melander, C. (2013). Combination approaches to combat multidrug-resistant bacteria. Trends in Biotechnology, 31(3), 177–184.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • 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 

  • Yu, W., & MacKerell, A. D. (2017). Computer-aided drug design methods. In Antibiotics (pp. 85–106). Springer.

    Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

PhD Thesis:

  • 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 

  • 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 

Internet Page

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Preeti Bajpai or Salman Akhtar .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Saxena, G., Sharma, M., Fatima, F., Bajpai, P., Akhtar, S. (2019). In Silico Molecular Modelling: Key Technologies in the Drug Discovery Process to Combat Multidrug Resistance. In: Ahmad, I., Ahmad, S., Rumbaugh, K. (eds) Antibacterial Drug Discovery to Combat MDR. Springer, Singapore. https://doi.org/10.1007/978-981-13-9871-1_10

Download citation

Publish with us

Policies and ethics