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Applications and Limitations of In Silico Models in Drug Discovery

  • Ahmet Sacan
  • Sean Ekins
  • Sandhya KortagereEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 910)

Abstract

Drug discovery in the late twentieth and early twenty-first century has witnessed a myriad of changes that were adopted to predict whether a compound is likely to be successful, or conversely enable identification of molecules with liabilities as early as possible. These changes include integration of in silico strategies for lead design and optimization that perform complementary roles to that of the traditional in vitro and in vivo approaches. The in silico models are facilitated by the availability of large datasets associated with high-throughput screening, bioinformatics algorithms to mine and annotate the data from a target perspective, and chemoinformatics methods to integrate chemistry methods into lead design process. This chapter highlights the applications of some of these methods and their limitations. We hope this serves as an introduction to in silico drug discovery.

Key words

Structural bioinformatics Chemoinformatics Structure prediction Virtual screening Hybrid structure-based method QSAR Drug discovery Troubleshooting computational methods 

Notes

Acknowledgment

We would like to thank Dr. Ronald Preez for generating figure 12.

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Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  1. 1.School of Biomedical EngineeringDrexel UniversityPhiladelphiaUSA
  2. 2.Collaborations in ChemistryJenkintownUSA
  3. 3.Department of Pharmaceutical SciencesUniversity of MarylandCollege ParkUSA
  4. 4.Department of PharmacologyUniversity of Medicine and Dentistry of New Jersey (UMDNJ)-Robert Wood Johnson Medical SchoolPiscatawayUSA
  5. 5.Department of Microbiology and ImmunologyDrexel University College of MedicinePhiladelphiaUSA

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