Journal of Computer-Aided Molecular Design

, Volume 26, Issue 11, pp 1207–1215 | Cite as

IADE: a system for intelligent automatic design of bioisosteric analogs

  • Peter ErtlEmail author
  • Richard Lewis


IADE, a software system supporting molecular modellers through the automatic design of non-classical bioisosteric analogs, scaffold hopping and fragment growing, is presented. The program combines sophisticated cheminformatics functionalities for constructing novel analogs and filtering them based on their drug-likeness and synthetic accessibility using automatic structure-based design capabilities: the best candidates are selected according to their similarity to the template ligand and to their interactions with the protein binding site. IADE works in an iterative manner, improving the fitness of designed molecules in every generation until structures with optimal properties are identified. The program frees molecular modellers from routine, repetitive tasks, allowing them to focus on analysis and evaluation of the automatically designed analogs, considerably enhancing their work efficiency as well as the area of chemical space that can be covered. The performance of IADE is illustrated through a case study of the design of a nonclassical bioisosteric analog of a farnesyltransferase inhibitor—an analog that has won a recent “Design a Molecule” competition.


IADE Bioisosteric design Scaffold hopping Chemical space Automated iterative drug design 



The authors would like to acknowledge help of colleagues Peter Gedeck, Nikolaus Stiefl and Marc Litherland who helped with the implementation of IADE. Several colleagues at Novartis acted as test users and early adopters, particularly Simona Cotesta, Paulette Greenidge, Hanneke Jansen, Anna Vulpetti, Bernard Pirard and Finton Sirockin. We would like to thank also Cresset Group for help with the use of command line FieldAlign and for organizing the “Design a Molecule” competition.


  1. 1.
    Kubinyi H (2006) Success stories of computer-aided design. In: Ekins S, Wang B (eds) Computer applications in pharmaceutical research and development. Wiley, Hoboken, pp 377–424CrossRefGoogle Scholar
  2. 2.
    Lewis RA (2012) The development of molecular modelling programs: the use and limitations of physical models. In: Livingstone DJ, Davis AM (eds) Drug design strategies: quantitative approaches. RSC Publishing, Cambridge, pp 88–107Google Scholar
  3. 3.
    Pirard B (2011) The quest for novel chemical matter and the contribution of computer-aided de novo design. Expert Opin Drug Discov 6:225–231CrossRefGoogle Scholar
  4. 4.
    Venkatasubramanian V, Chan K, Caruthers JM (1995) Evolutionary design of molecules with desired properties using the genetic algorithm. J Chem Inf Comput Sci 35:188–195CrossRefGoogle Scholar
  5. 5.
    Sheridan RP, Kearsley SK (1995) Using a genetic algorithm to suggest combinatorial libraries. J Chem Inf Comput Sci 35:310–320CrossRefGoogle Scholar
  6. 6.
    Weber L, Almstetter M (1999) Diversity in very large libraries. In: Dean PM, Lewis RA (eds) Molecular diversity in drug design. Kluwer, pp 93-114Google Scholar
  7. 7.
    Skvortsova MI, Baskin II, Slovokhotova OL, Palyulin VA, Zefirov NS (1993) Inverse problem in QSAR/QSPR studies for the case of topological indices characterizing molecular shape (Kier indices). J Chem Inf Comput Sci 33:630–634CrossRefGoogle Scholar
  8. 8.
    Kier LB, Hall LH (1993) The generation of molecular structures from a graph-based QSAR equation. Quant Struct-Act Relat 12:383–388CrossRefGoogle Scholar
  9. 9.
    Hall LH, Fisk JB (1994) Computer generation of vertex degree sets for chemical graphs from a number of vertices and rings. J Chem Inf Comput Sci 34:1184–1189CrossRefGoogle Scholar
  10. 10.
    Kvasnicka V, Pospichal J (1996) Simulated annealing construction of molecular graphs with required properties. J Chem Inf Comput Sci 36:516–526CrossRefGoogle Scholar
  11. 11.
    Faulon J-L, Visco DP Jr, Pophale RS (2003) The signature molecular descriptor. 1. Using extended valence sequences in QSAR and QSPR studies. J Chem Inf Comput Sci 43:707–720CrossRefGoogle Scholar
  12. 12.
    Weininger D (1995) Method and apparatus for designing molecules with desired properties by evolving successive populations. U.S. Patent No. 5,434,796Google Scholar
  13. 13.
    Lewis RA (2005) A general method for exploiting QSAR models in lead optimization. J Med Chem 2005(48):1638–1648CrossRefGoogle Scholar
  14. 14.
    Ertl P (2003) Cheminformatics analysis of organic substituents: identification of the most common substituents, calculation of substituent properties, and automatic identification of drug-like bioisosteric groups. J Chem Inf Comput Sci 43:374–380CrossRefGoogle Scholar
  15. 15.
    Ertl P (1998) World Wide Web-based system for the calculation of substituent parameters and substituent similarity searches. J Mol Graph Mod 16:11–13CrossRefGoogle Scholar
  16. 16.
    Ertl P (2012) Database of bioactive ring systems with calculated properties and its use in bioisosteric design and scaffold hopping. Bioorg Med Chem 20:5436–5442CrossRefGoogle Scholar
  17. 17.
    Gaulton A, Bellis LJ, Bento AP, Chambers J, Davies M, Hersey A, Light Y, McGlinchey S, Michalovich D, Al-Lazikani B, Overington JP (2012) ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res 40:D1100–D1107CrossRefGoogle Scholar
  18. 18.
    Ertl P (1997) Simple quantum-chemical parameters as an alternative to the Hammett sigma constants in QSAR studies. Quant Struct-Act Relat 16:377–382CrossRefGoogle Scholar
  19. 19.
    Ertl P, Rohde B, Selzer P (2000) Fast calculation of molecular polar surface area as a sum of fragment-based contributions and its application to the prediction of drug transport properties. J Med Chem 43:3714–3717CrossRefGoogle Scholar
  20. 20.
    Walters WP, Murcko MA (2002) Prediction of ‘drug-likeness’. Adv Drug Del Rev 54:255–271CrossRefGoogle Scholar
  21. 21.
    Ertl P, Schuffenhauer A (2009) Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions. J Cheminf 1:8CrossRefGoogle Scholar
  22. 22.
    Hartenfeller M, Zettl H, Walter M, Rupp M, Reisen F, Proschak E, Weggen S, Stark H, Schneider G (2012) DOGS: reaction-driven de novo design of bioactive compounds. PLoS Comput Biol 8:e1002380CrossRefGoogle Scholar
  23. 23.
    CORINA version 3.20, Molecular Networks GmbH, Erlangen, GermanyGoogle Scholar
  24. 24.
    FieldAlign version 3.01, Cresset Group, Welwyn Garden City, UKGoogle Scholar
  25. 25.
    Glide, version 5.8, Schrödinger, Inc., New York, NY, USAGoogle Scholar
  26. 26.
    Langdon S, Ertl P, Brown N (2010) Bioisosteric replacement and scaffold hopping in lead generation and optimization. Mol Inform 29:366–385CrossRefGoogle Scholar
  27. 27.
  28. 28.
    Hast MA, Fletcher S, Cummings CG, Pusateri EE, Blaskovich MA, Rivas K, Gelb MH, Van Voorhis WC, Sebti SM, Hamilton AD, Beese LS (2009) Structural basis for binding and selectivity of antimalarial and anticancer ethylenediamine inhibitors to protein farnesyltransferase. Chem Biol 16:181–192CrossRefGoogle Scholar
  29. 29.
    Beno BR, Langley DR (2010) MORPH: a new tool for ligand design. J Chem Inf Model 50:1159–1164CrossRefGoogle Scholar
  30. 30.
    Raymond JW, Watson IA, Mahouni A (2009) Rationalizing lead optimization by associating quantitative relevance with molecular structure modification. J Chem Inf Model 49:1952–1962CrossRefGoogle Scholar
  31. 31.
    Lewell XQ, Jones AC, Bruce CL, Harper G, Jones MM, Mclay IM, Bradshaw J (2003) Drug rings database with web interface. A tool for identifying alternative chemical rings in lead discovery programs. J Med Chem 46:3257–3274CrossRefGoogle Scholar
  32. 32.
    Boehm M, Wu T-Y, Claussen H, Lemmen C (2008) Similarity searching and scaffold hopping in synthetically accessible combinatorial chemistry spaces. J Med Chem 51:2468–2480CrossRefGoogle Scholar
  33. 33.
    Lessel U, Wellenzohn B, Lilienthal M, Claussen H (2009) Searching fragment spaces with feature trees. J Chem Inf Model 49:270–279CrossRefGoogle Scholar
  34. 34.
    Jorgensen WL (2009) Efficient drug lead discovery and optimization. Acc Chem Res 42:724–733CrossRefGoogle Scholar
  35. 35.
    Jorgensen WL, Ruiz-Caro J, Tirado-Rives J, Basavapathruni A, Anderson KS, Hamilton AD (2006) Computer-aided design of non-nucleoside inhibitors of HIV-1 reverse transcriptase. Bioorg Med Chem Lett 16:663–667CrossRefGoogle Scholar
  36. 36.
    Sheridan RP, Kearsley SK (2002) Why do we need so many chemical similarity search methods? Drug Discov Today 7:903–911CrossRefGoogle Scholar
  37. 37.
    Ertl P (2007) Polar surface area. In: Mannhold R (ed) Molecular drug properties. Wiley, Hoboken, pp 111–126CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  1. 1.Novartis Institutes for BioMedical ResearchBaselSwitzerland

Personalised recommendations