Chemical Genomics and Proteomics pp 25-31

Part of the Methods in Molecular Biology book series (MIMB, volume 800) | Cite as

In Silico Design of Small Molecules

Protocol

Abstract

Computational methods now play an integral role in modern drug discovery, and include the design and management of small molecule libraries, initial hit identification through virtual screening, optimization of the affinity and selectivity of hits, and improving the physicochemical properties of the lead compounds. In this chapter, we survey the most important data sources for the discovery of new molecular entities, and discuss the key considerations and guidelines for virtual chemical library design.

Key words

Bioinformatics Computational biology Virtual chemical library Virtual combinatorial library Computer-aided drug design 

References

  1. 1.
    Klebe G (2006) Virtual ligand screening: strategies, perspectives and limitations. Drug Discov Today 11:580–594PubMedCrossRefGoogle Scholar
  2. 2.
    Olah M, Mracec M, Ostopovici L et al (2004) WOMBAT: world of molecular bioactivity. In: Oprea TI (ed) Chemoinformatics in drug discovery, Wiley-VCH, New YorkGoogle Scholar
  3. 3.
    Goto S, Okuno Y, Hattori M et al (2002) LIGAND: database of chemical compounds and reactions in biological pathways. Nucleic Acids Res 30:402–404PubMedCrossRefGoogle Scholar
  4. 4.
    Irwin JJ, Shoichet BK (2004) ZINC–a free database of commercially available compounds for virtual screening. J Chem Inf Model 45:177–182CrossRefGoogle Scholar
  5. 5.
    Wishart DS, Knox C, Guo AC et al (2006) DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res 34:D668–672PubMedCrossRefGoogle Scholar
  6. 6.
    Chen J, Swamidass SJ, Dou Y et al (2005) ChemDB: a public database of small molecules and related chemoinformatics resources. Bioinformatics 21:4133–4139PubMedCrossRefGoogle Scholar
  7. 7.
    Jónsdóttir SO, Jørgensen FS, Brunak S (2005) Prediction methods and databases within chemoinformatics: emphasis on drugs and drug candidates. Bioinformatics 21:2145–2160PubMedCrossRefGoogle Scholar
  8. 8.
    Agrafiotis DK, Lobanov VS, Salemme FR (2002) Combinatorial informatics in the post-genomics era. Nat Rev Drug Discov 1:337–346PubMedCrossRefGoogle Scholar
  9. 9.
    Leland BA, Christie BD, Nourse JG et al (1997) Managing the combinatorial expansion. J Chem Inf Comput Sci 37:62–70CrossRefGoogle Scholar
  10. 10.
    Leach AR, Bradshaw J, Green DVS et al (1999) Implementation of a system for reagent selection and library enumeration, profiling and design. J Chem Inf Comput Sci 39:1161–1172PubMedCrossRefGoogle Scholar
  11. 11.
    Lobanov VS, Agrafiotis DK (2002) Scalable methods for the construction and analysis of virtual combinatorial libraries. Combin Chem High-Throughput Screen 5:167–178Google Scholar
  12. 12.
    Livingston DJ (2000) The characterization of molecular structures using molecular properties. A survey. J Chem Inf Comput Sci 40:195–209CrossRefGoogle Scholar
  13. 13.
    Brown RD, Hassan M, Waldman M (2000) Combinatorial library design for diversity, cost efficiency, and drug-like character. J Mol Graph Model 18:427–437PubMedCrossRefGoogle Scholar
  14. 14.
    O’Donovan C, Apweiler R, Bairoch A (2001) The human proteomics initiative (HPI). Trends Biotechnol 19:178–181PubMedCrossRefGoogle Scholar
  15. 15.
    Bohacek RS, McMartin C, Guida WC (1996) The art and practice of structure-based drug design: a molecular modelling perspective. Med Res Rev 16:3–50PubMedCrossRefGoogle Scholar
  16. 16.
    Lipinski CA (2000) Drug-like properties and the causes of poor solubility and poor permeability. J Pharmacol Toxicol 44:235–249CrossRefGoogle Scholar
  17. 17.
    Hou T, Wang J, Zhang W et al (2006) Recent advances in computational prediction of drug absorption and permeability in drug discovery. Curr Med Chem 13:2653–2667PubMedCrossRefGoogle Scholar
  18. 18.
    Ghose AK, Viswanadhan VN, Wendoloski JJ (1999) A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of known drug databases. J Comb Chem 1:55–68PubMedCrossRefGoogle Scholar
  19. 19.
    Oprea T (2000) Property distribution of drug-related chemical databases. J Comput Aided Mol Des 14:251–264PubMedCrossRefGoogle Scholar
  20. 20.
    Wenlock MC, Austin RP, Barton P et al (2003) A comparison of physicochemical property profiles of development and marketed oral drugs. J Med Chem 46:1250–1256PubMedCrossRefGoogle Scholar
  21. 21.
    Congreve M, Carr R, Murray C et al (2003) A “rule of three” for fragment-based lead discovery? Drug Discov Today 8:876–877PubMedCrossRefGoogle Scholar
  22. 22.
    Song CM, Tong JC (2009) Recent advances in computer-aided drug design. Brief Bioinform 10:579–591PubMedCrossRefGoogle Scholar
  23. 23.
    Gasteiger J, Marsili M (1978) A new model for calculating atomic charges in molecules. Tetrahedron Lett 19:3181–3184CrossRefGoogle Scholar
  24. 24.
    Cornell WD, Cieplak P et al (1995). A Second Generation Force Field for the Simulation of Proteins, Nucleic Acids, and Organic Molecules. J. Am Chem Soc 117: 5179–5197CrossRefGoogle Scholar
  25. 25.
    Halgren TA (1996) Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94. J Comp Chem 17:490–519CrossRefGoogle Scholar
  26. 26.
    Jakalian A, Jack DB, Bayly CI (2002) Fast, efficient generation of high-quality atomic charges. AM1-BCC model: II. Parameterization and validation. J Comput Chem 23:1623–1641PubMedCrossRefGoogle Scholar
  27. 27.
    Baell JB, Holloway GA (2010) New Substructure Filters for Removal of Pan Assay Interference Compounds (PAINS) from Screening Libraries and for Their Exclusion in Bioassays. J Med Chem 53:2719–2740PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Institute of Chemical and Engineering SciencesAgency for Science Technology and Research (A*STAR)SingaporeSingapore
  2. 2.Department of Biochemistry, Yong Loo Lin School of MedicineNational University of SingaporeSingaporeSingapore

Personalised recommendations