Journal of Computer-Aided Molecular Design

, Volume 31, Issue 3, pp 275–285 | Cite as

Computer-aided drug design at Boehringer Ingelheim

  • Ingo Muegge
  • Andreas Bergner
  • Jan M. Kriegl


Computer-Aided Drug Design (CADD) is an integral part of the drug discovery endeavor at Boehringer Ingelheim (BI). CADD contributes to the evaluation of new therapeutic concepts, identifies small molecule starting points for drug discovery, and develops strategies for optimizing hit and lead compounds. The CADD scientists at BI benefit from the global use and development of both software platforms and computational services. A number of computational techniques developed in-house have significantly changed the way early drug discovery is carried out at BI. In particular, virtual screening in vast chemical spaces, which can be accessed by combinatorial chemistry, has added a new option for the identification of hits in many projects. Recently, a new framework has been implemented allowing fast, interactive predictions of relevant on and off target endpoints and other optimization parameters. In addition to the introduction of this new framework at BI, CADD has been focusing on the enablement of medicinal chemists to independently perform an increasing amount of molecular modeling and design work. This is made possible through the deployment of MOE as a global modeling platform, allowing computational and medicinal chemists to freely share ideas and modeling results. Furthermore, a central communication layer called the computational chemistry framework provides broad access to predictive models and other computational services.


Computational chemistry Molecular modeling Predictive modeling Chemoinformatics Virtual screening 



We would especially like to thank our Computational Chemistry and CADD teams in Biberach, Ridgefield and Vienna for their dedicated contribution, without whom this work would not have been possible. The support from our colleagues in IT, in particular for the implementation of the CCFW, is gratefully acknowledged. We thank Ulrike Küfner-Mühl, Darryl McConnell, Dirk Kessler, and Robert Hughes for fruitful discussions and for their continued support. We also thank Karen J. Bergner for critically reading the manuscript.


  1. 1.
    Schiele F, van Ryn J, Litzenburger T, Ritter M, Seeliger D, Nar H (2015) Structure-guided residence time optimization of a dabigatran reversal agent. MAbs 7:871–880CrossRefGoogle Scholar
  2. 2.
    Seeliger D, Schulz P, Litzenburger T, Spitz J, Hoerer S, Blech M, Enenkel B, Studts JM, Garidel P, Karow AR (2015) Boosting antibody developability through rational sequence optimization. MAbs 7:505–515CrossRefGoogle Scholar
  3. 3.
    Beck B, Seeliger D, Kriegl JM (2015) The impact of data integrity on decision making in early lead discovery. J Comput Aided Mol Des 29:911–921CrossRefGoogle Scholar
  4. 4.
  5. 5.
    Knime: Accessed 23 Feb 2016
  6. 6.
    Beck B (2012) BioProfile—Extract knowledge from corporate databases to assess cross-reactivities of compounds. Bioorganic Med Chem 20:5428–5435CrossRefGoogle Scholar
  7. 7.
    Loughney D, Claus BL, Johnson SR (2011) To measure is to know: an approach to CADD performance metrics. Drug Discov Today 16:548–554CrossRefGoogle Scholar
  8. 8.
    Baldwin ET (2012) Metrics and the effective computational scientist: process, quality and communication. Drug Discov Today 17:935–941CrossRefGoogle Scholar
  9. 9.
    Kuhn B, Guba W, Hert J, Banner D, Bissantz C, Ceccarelli S, Haap W, Korner M, Kuglstatter A, Lerner C, Mattei P, Neidhart W, Pinard E, Rudolph MG, Schulz-Gasch T, Woltering T, Stahl M (2016) A real-world perspective on molecular design. J Med Chem 59:4087–4102CrossRefGoogle Scholar
  10. 10.
    Christ CD, Fox T (2014) Accuracy assessment and automation of free energy calculations for drug design. J Chem Inf Model 54:108–120CrossRefGoogle Scholar
  11. 11.
    Hucke O, Coulombe R, Bonneau P, Bertrand-Laperle M, Brochu C, Gillard J, Joly MA, Landry S, Lepage O, Llinas-Brunet M, Pesant M, Poirier M, Poirier M, McKercher G, Marquis M, Kukolj G, Beaulieu PL, Stammers TA (2014) Molecular dynamics simulations and structure-based rational design lead to allosteric HCV NS5B polymerase thumb pocket 2 inhibitor with picomolar cellular replicon potency. J Med Chem 57:1932–1943CrossRefGoogle Scholar
  12. 12.
    Wassermann AM, Haebel P, Weskamp N, Bajorath J (2012) SAR matrices: automated extraction of information-rich SAR tables from large compound data sets. J Chem Inf Model 52:1769–1776CrossRefGoogle Scholar
  13. 13.
    Demir-Kavuk O, Bentzien J, Muegge I, Knapp EW (2011) DemQSAR: predicting human volume of distribution and clearance of drugs. J Comput Aided Mol Des 25:1121–1133CrossRefGoogle Scholar
  14. 14.
    Kramer C, Beck B, Kriegl JM, Clark T (2008) A composite model for HERG blockade. Chem Med Chem 3:254–265CrossRefGoogle Scholar
  15. 15.
    Nocker M, Handschuh S, Tautermann C, Liedl KR (2009) Theoretical prediction of hydrogen bond strength for use in molecular modeling. J Chem Inf Model 49:2067–2076CrossRefGoogle Scholar
  16. 16.
    Huber RG, Margreiter MA, Fuchs JE, Von GS, Tautermann CS, Liedl KR, Fox T (2014) Heteroaromatic pi-stacking energy landscapes. J Chem Inf Model 54:1371–1379CrossRefGoogle Scholar
  17. 17.
    Kneissl B, Leonhardt B, Hildebrandt A, Tautermann CS (2009) Revisiting automated G-protein coupled receptor modeling: the benefit of additional template structures for a neurokinin-1 receptor model. J Med Chem 52:3166–3173CrossRefGoogle Scholar
  18. 18.
    Li H, Kasam V, Tautermann CS, Seeliger D, Vaidehi N (2014) Computational method to identify druggable binding sites that target protein-protein interactions. J Chem Inf Model 54:1391–1400CrossRefGoogle Scholar
  19. 19.
    Hao MH, Haq O, Muegge I (2007) Torsion angle preference and energetics of small-molecule ligands bound to proteins. J Chem Inf Model 47:2242–2252CrossRefGoogle Scholar
  20. 20.
    Phipps MJ, Fox T, Tautermann CS, Skylaris CK (2016) Energy decomposition analysis based on absolutely localized molecular orbitals for large-scale density functional theory calculations in drug design. J Chem Theory Comput 12:3135–3148CrossRefGoogle Scholar
  21. 21.
    Python version 2.7 available at Accessed 23 Feb 2016
  22. 22.
    RDKit: Accessed 23 Feb 2016
  23. 23.
    Muegge I, Zhang Q (2015) 3D virtual screening of large combinatorial spaces. Methods 71:14–20CrossRefGoogle Scholar
  24. 24.
    Teodoro M, Muegge I (2011) BIBuilder: exhaustive Searching for De Novo Ligands. Mol Inform 30:63–75CrossRefGoogle Scholar
  25. 25.
    Bentzien J, Muegge I, Hamner B, Thompson DC (2013) Crowd computing: using competitive dynamics to develop and refine highly predictive models. Drug Discov Today 18:472–478CrossRefGoogle Scholar
  26. 26.
    Bentzien J, Bharadwaj R, Thompson DC (2015) Crowdsourcing in pharma: a strategic framework. Drug Discov Today 20:874–883CrossRefGoogle Scholar
  27. 27.
    Molecular Operating Environment (MOE) (2015) 2014.09; Chemical Computing Group Inc., 1010 Sherbooke St. West, Suite #910, Montreal, QC, Canada, H3A 2R7Google Scholar
  28. 28.
    Bentzien J, Hickey ER, Kemper RA, Brewer ML, Dyekjaer JD, East SP, Whittaker M (2010) An in silico method for predicting Ames activities of primary aromatic amines by calculating the stabilities of nitrenium ions. J Chem Inf Model 50:274–297CrossRefGoogle Scholar
  29. 29.
    Bentzien J, Muegge I (2014) In silico predictions of genotoxicity for aromatic amines. Front Biosci (Landmark Ed) 19:649–661CrossRefGoogle Scholar
  30. 30.
    Jones G, Willett P, Glen RC, Leach AR, Taylor R (1997) Development and validation of a genetic algorithm for flexible docking. J Mol Biol 267:727–748CrossRefGoogle Scholar
  31. 31.
    Sadowski J, Gasteiger J, Klebe G (1994) Comparison of automatic three-dimensional model builders using 639 X-ray structures. J Chem Inf Comput Sci 34:1000–1008CrossRefGoogle Scholar
  32. 32.
    Kriegl JM, Arnhold T, Beck B, Fox T (2005) A support vector machine approach to classify human cytochrome P450 3A4 inhibitors. J Comput Aided Mol Des 19:189–201CrossRefGoogle Scholar
  33. 33.
    Muegge I, Bentzien J, Mukherjee P, Hughes RO (2016) Automatically updating predictive modeling workflows support decision making in drug design. Future Med Chem 8:1779–1796CrossRefGoogle Scholar
  34. 34.
    Page KM (2016) Validation of early human dose prediction: a key metric for compound progression in Drug Discovery. Mol Pharm 13:609–620CrossRefGoogle Scholar
  35. 35.
    Marvin 6.0.2, 2014, ChemAxon (
  36. 36.
    Hillisch A, Heinrich N, Wild H (2015) Computational chemistry in the pharmaceutical industry: from childhood to adolescence. ChemMedChem 10:1958–1962CrossRefGoogle Scholar
  37. 37.
    Bieler M, Reutlinger M, Rodrigues T, Schneider P, Kriegl JM, Schneider G (2016) Designing multi-target compound libraries with Gaussian process models. Mol Inform 35:192–198CrossRefGoogle Scholar
  38. 38.
    Obach RS, Lombardo F, Waters NJ (2008) Trend analysis of a database of intravenous pharmacokinetic parameters in humans for 670 drug compounds. Drug Metab Dispos 36:1385–1405CrossRefGoogle Scholar
  39. 39.
    Ploemen JP, Kelder J, Hafmans T, van de Sandt H, van Burgsteden JA, Saleminki PJ, Van EE (2004) Use of physicochemical calculation of pKa and CLogP to predict phospholipidosis-inducing potential: a case study with structurally related piperazines. Exp Toxicol Pathol 55:347–355Google Scholar
  40. 40.
    Przybylak KR, Alzahrani AR, Cronin MT (2014) How does the quality of phospholipidosis data influence the predictivity of structural alerts? J Chem Inf Model 54:2224–2232CrossRefGoogle Scholar
  41. 41.
    Molecular Discovery Ltd. Moka version 1.1. Accessed 23 Feb 2016)
  42. 42.
    Hussain J, Rea C (2010) Computationally efficient algorithm to identify matched molecular pairs (MMPs) in large data sets. J Chem Inf Model 50:339–348CrossRefGoogle Scholar
  43. 43.
    Griffen E, Leach AG, Robb GR, Warner DJ (2011) Matched molecular pairs as a medicinal chemistry tool. J Med Chem 54:7739–7750CrossRefGoogle Scholar
  44. 44.
    Geppert T, Beck B (2014) Fuzzy matched pairs: a means to determine the pharmacophore impact on molecular interaction. J Chem Inf Model 54:1093–1102CrossRefGoogle Scholar
  45. 45.
    Fuchs JE, Wellenzohn B, Weskamp N, Liedl KR (2015) Matched peptides: tuning matched molecular pair analysis for biopharmaceutical applications. J Chem Inf Model 55:2315–2323CrossRefGoogle Scholar
  46. 46.
    Tibco Spotfire. version 6.3. Accessed 4 July 2016
  47. 47.
    Certara. D360: The Pharmaceutical Industry’s Data Analytics and Scientific Informatics Platform. Accessed 24 Feb 2016
  48. 48.
    Bergner A, Parel SP (2013) Hit expansion approaches using multiple similarity methods and virtualized query structures. J Chem Inf Model 53:1057–1066CrossRefGoogle Scholar
  49. 49.
    Muegge I, Oloff S (2006) Advances in virtual screening. Drug Discov Today Technol 3:405–411CrossRefGoogle Scholar
  50. 50.
    Muegge I (2008) Synergies of virtual screening approaches. Mini Rev Med Chem 8:927–933CrossRefGoogle Scholar
  51. 51.
    Muegge I, Oloff S (2010) Virtual screening. In: Abraham DJ, Rotella DP (eds) Burger’s medicinal chemistry drug discovery and development, vol 2, 7th edn. Wiley, Hoboken, pp 1–46Google Scholar
  52. 52.
    Muegge I, Mukherjee P (2016) An overview of molecular fingerprint similarity search in virtual screening. Expert Opin Drug Discov 11:137–148CrossRefGoogle Scholar
  53. 53.
    Lessel U, Wellenzohn B, Lilienthal M, Claussen H (2009) Searching fragment spaces with feature trees. J Chem Inf Model 49:270–279CrossRefGoogle Scholar
  54. 54.
    Rarey M, Dixon JS (1998) Feature trees: a new molecular similarity measure based on tree matching. J Comput Aided Mol Des 12:471–490CrossRefGoogle Scholar
  55. 55.
    Grant JA, Nicholls A, Stahl MT. ROCS OpenEye, 3600 Cerrillos Rd., Suite 1107, Santa Fe, NM 87507Google Scholar
  56. 56.
    Wellenzohn B, Lessel U, Beller A, Isambert T, Hoenke C, Nosse B (2012) Identification of new potent GPR119 agonists by combining virtual screening and combinatorial chemistry. J Med Chem 55:11031–11041CrossRefGoogle Scholar
  57. 57.
    Lessel U, Wellenzohn B, Fischer JR, Rarey M (2012) Design of combinatorial libraries for the exploration of virtual hits from fragment space searches with LoFT. J Chem Inf Model 52:373–379CrossRefGoogle Scholar
  58. 58.
    Muegge I, Collin D, Cook B, Hill-Drzewi M, Horan J, Kugler S, Labadia M, Li X, Smith L, Zhang Y (2015) Discovery of 1,3-dihydro-2,1,3-benzothiadiazole 2,2-dioxide analogs as new RORC modulators. Bioorg Med Chem Lett 25:1892–1895CrossRefGoogle Scholar
  59. 59.
    Hickey ER, Zindell R, Cirillo PF, Wu L, Ermann M, Berry AK, Thomson DS, Albrecht C, Gemkow MJ, Riether D (2015) Selective CB2 receptor agonists. Part 1: the identification of novel ligands through computer-aided drug design (CADD) approaches. Bioorg Med Chem Lett 25:575–580CrossRefGoogle Scholar
  60. 60.
    Bento AP, Gaulton A, Hersey A, Bellis LJ, Chambers J, Davies M, Kruger FA, Light Y, Mak L, McGlinchey S, Nowotka M, Papadatos G, Santos R, Overington JP (2014) The ChEMBL bioactivity database: an update. Nucleic Acids Res 42:D1083–D1090CrossRefGoogle Scholar
  61. 61.
    Williams AJ, Harland L, Groth P, Pettifer S, Chichester C, Willighagen EL, Evelo CT, Blomberg N, Ecker G, Goble C, Mons B (2012) Open PHACTS: semantic interoperability for drug discovery. Drug Discov Today 17:1188–1198CrossRefGoogle Scholar
  62. 62.
    Briggs KA (2016) Is preclinical data sharing the new norm? Drug Discov Today (in press)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Small Molecule Discovery ResearchBoehringer Ingelheim PharmaceuticalsRidgefieldUSA
  2. 2.Department of Medicinal ChemistryBoehringer Ingelheim RCV GmbH & Co KGViennaAustria
  3. 3.Department of Lead Identification and Optimization SupportBoehringer Ingelheim Pharma GmbH & Co. KGBiberachGermany

Personalised recommendations