Abstract
Computer-aided drug discovery started at Albany Molecular Research, Inc in 1997. Over nearly 20 years the role of cheminformatics and computational chemistry has grown throughout the pharmaceutical industry and at AMRI. This paper will describe the infrastructure and roles of CADD throughout drug discovery and some of the lessons learned regarding the success of several methods. Various contributions provided by computational chemistry and cheminformatics in chemical library design, hit triage, hit-to-lead and lead optimization are discussed. Some frequently used computational chemistry techniques are described. The ways in which they may contribute to discovery projects are presented based on a few examples from recent publications.
Similar content being viewed by others
Abbreviations
- CADD:
-
Computer-aided drug discovery
- ADMET:
-
Absorption, distribution, metabolism, excretion and toxicity
- DMPK:
-
Distribution, metabolism and pharmacokinetics
- HTS:
-
High-throughput screening
- SAR:
-
Structure-activity relationship
- SPR:
-
Structure-property relationship
- CRO:
-
Contract research organization
References
Molecular Operating Environment(MOE) Chemical Computing Group, Inc., Montreal, CA
Muchmore SW, Edmunds JJ, Stewart KD, Hajduk PJ (2010) Cheminformatic tools for medicinal chemists. J Med Chem 53:4830–4841. doi:10.1021/jm100164z
Metz JT, Huth JR, Hajduk PJ (2007) Enhancement of chemical rules for predicting compound reactivity towards protein thiol groups. J Comput Aided Mol Des 21:139–144. doi:10.1007/s10822-007-9109-z
Nicholls A (2008) What do we know and when do we know it? J Comput Aided Mol Des 22:239–255. doi:10.1007/s10822-008-9170-2
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 53:2719–2740. doi:10.1021/jm901137j
Bruns RF, Watson IA (2012) Rules for identifying potentially reactive or promiscuous compounds. J Med Chem 55:9763–9772. doi:10.1021/jm301008n
Lovering F, Bikker J, Humblet C (2009) Escape from flatland: increasing saturation as an approach to improving clinical success. J Med Chem 52:6752–6756. doi:10.1021/jm901241e
Sauer WHB, Schwarz MK (2003) Molecular shape diversity of combinatorial libraries: a prerequisite for broad bioactivity. J Chem Inf Comput Sci 43:987–1003. doi:10.1021/ci025599w
Kenny PW, Montanari CA (2013) Inflation of correlation in the pursuit of drug-likeness. J Comput Aided Mol Des 27:1–13. doi:10.1007/s10822-012-9631-5
Veber DF, Johnson SR, Cheng H et al (2002) Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem 45:2615–2623. doi:10.1021/jm020017n
Oprea TI, Chemistry M, Ab AH, Mölndal S- (2000) Property distribution of drug-related chemical databases. J Comput Aided Mol Des 14(3):251–264
Nilakantan R, Immermann F, Haraki K (2002) A novel approach to combinatorial library design. Comb Chem High Throughput Screen 5:105–110
Xu J (2002) A new approach to finding natural chemical structure classes. J Med Chem 45:5311–5320. doi:10.1021/jm010520k
Schuffenhauer A, Ertl P, Roggo S et al (2007) The scaffold tree—visualization of the scaffold universe by hierarchical scaffold classification. J Chem Inf Model 47:47–58. doi:10.1021/ci600338x
Bemis GW, Murcko MA (1996) The properties of known drugs. 1. Molecular frameworks. J Med Chem 39:2887–2893. doi:10.1021/jm9602928
Duffy BC, Liu S, Martin GS et al (2015) Discovery of a new chemical series of BRD4(1) inhibitors using protein-ligand docking and structure-guided design. Bioorg Med Chem Lett 25:2818–2823. doi:10.1016/j.bmcl.2015.04.107
Duffy BC, Zhu L, Decornez H, Kitchen DB (2012) Early phase drug discovery: cheminformatics and computational techniques in identifying lead series. Bioorg Med Chem 20:5324–5342
Kitchen DB, Decornez HY (2015) Computational techniques to support hit triage. Small Mol Med Chem Strateg Technol. doi:10.1002/9781118771723.ch7
Hopkins AL, Keserü GM, Leeson PD et al (2014) The role of ligand efficiency metrics in drug discovery. Nat Rev Drug Discov 13:105–121. doi:10.1038/nrd4163
Murray CW, Erlanson DA, Hopkins AL et al (2014) Validity of ligand efficiency metrics. ACS Med Chem Lett 5(6):616–618
Kenny PW, Leitão A, Montanari CA (2014) Ligand efficiency metrics considered harmful. J Comput Aided Mol Des 28:699–710. doi:10.1007/s10822-014-9757-8
Shultz MD (2014) Improving the plausibility of success with inefficient metrics. ACS Med Chem Lett 5(1):2–5
Mannhold R, Poda GI, Ostermann C, Tetko IV (2009) Calculation of molecular lipophilicity: state-of-the-art and comparison of log P methods on more than 96,000 compounds. J Pharm Sci 98:861–893. doi:10.1002/jps.21494
Friesner RA, Banks JL, Murphy RB et al (2004) Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 47:1739–1749. doi:10.1021/jm0306430
Halgren TA, Murphy RB, Friesner RA et al (2004) Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J Med Chem 2:1750–1759
Friesner RA, Murphy RB, Repasky MP et al (2006) Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. ACS Med Chem 49(21):6177–6196
McGaughey GB, Sheridan RP, Bayly CI et al (2007) Comparison of topological, shape, and docking methods in virtual screening. J Chem Inf Model 47:1504–1519. doi:10.1021/ci700052x
Cross JB, Thompson DC, Rai BK et al (2009) Comparison of several molecular docking programs: pose prediction and virtual screening accuracy. J Chem Inf Model 49:1455–1474. doi:10.1021/ci900056c
Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The Protein Data Bank. Nucleic Acids Res 28:235–242
Wang L, Wu Y, Deng Y et al (2015) Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way of a modern free-energy calculation protocol and force field. J Am Chem Soc 137:2695–2703. doi:10.1021/ja512751q
Kovalenko A, Hirata F (1999) Self-consistent description of a metal—water interface by the Kohn—Sham density functional theory and the three-dimensional reference interaction site model. J Chem Physics 6(20):607–624. doi:10.1063/1.47803
Pechulis AD, Beck JP, Curry MA et al (2012) 4-Phenyl tetrahydroisoquinolines as dual norepinephrine and dopamine reuptake inhibitors. Bioorg Med Chem Lett 22:7219–7222. doi:10.1016/j.bmcl.2012.09.050
Cioffi CL, Racz B, Freeman EE et al (2015) Bicyclic [3.3.0]-octahydrocyclopenta[c]pyrrolo antagonists of retinol binding protein 4: potential treatment of atrophic age-related macular degeneration and stargardt disease. J Med Chem 58:5863–5888. doi:10.1021/acs.jmedchem.5b00423
Cioffi CL, Dobri N, Freeman EE et al (2014) Design, synthesis, and evaluation of nonretinoid retinol binding protein 4 antagonists for the potential treatment of atrophic age-related macular degeneration and stargardt disease. J Med Chem 57:7731–7757. doi:10.1021/jm5010013
Lewell XQ, Judd DB, Watson SP, Hann MM (1998) RECAP–retrosynthetic combinatorial analysis procedure: a powerful new technique for identifying privileged molecular fragments with useful applications in combinatorial chemistry. J Chem Inf Comput Sci 38:511–522. doi:10.1021/ci970429i
Pierce AC, Rao G, Bemis GW (2004) BREED: generating novel inhibitors through hybridization of known ligands. Application to CDK2, P38, and HIV protease. J Med Chem 47:2768–2775. doi:10.1021/jm030543u
Wagner BK (2015) The resurgence of phenotypic screening in drug discovery and development. Expert Opin Drug Discov 441:17460441.2016.1122589. doi: 10.1517/17460441.2016.1122589
Walter T, Shattuck DW, Baldock R et al (2010) Visualization of image data from cells to organisms. Nat Methods 7:S26–S41. doi:10.1038/nmeth.1431
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Kitchen, D.B. Computer-aided drug discovery research at a global contract research organization. J Comput Aided Mol Des 31, 309–318 (2017). https://doi.org/10.1007/s10822-016-9991-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10822-016-9991-3