Abstract
Computer-aided drug discovery has become an important part of the drug discovery process due to the reduced cost of computational methods and the increased availability of three-dimensional structural information. In this chapter, we compare structure-based and ligand-based modeling, and focus primarily on molecular docking and molecular dynamics simulations. In addition, we provide a broad overview of the application of computational methods in drug discovery and highlight some considerations in the application of molecular docking and molecular dynamics simulations. These approaches are particularly relevant in precision medicine because they have the potential to provide a detailed understanding of the molecular features that are essential for drug specificity and selectivity, thereby facilitating a comparison among population differences.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Abel R, Wang L, Mobley DL, Friesner RA (2017) A critical review of validation, blind testing, and real- world use of alchemical protein-ligand binding free energy calculations. Curr Top Med Chem 17:2577–2585. https://doi.org/10.2174/1568026617666170414142131
Abrams C, Bussi G (2014) Enhanced sampling in molecular dynamics using metadynamics, replica-exchange, and temperature-acceleration. Entropy-Switz 16:163–199. https://doi.org/10.3390/e16010163
Amaro RE, Baron R, McCammon JA (2008) An improved relaxed complex scheme for receptor flexibility in computer-aided drug design. J Comput Aided Mol Des 22:693–705. https://doi.org/10.1007/s10822-007-9159-2
Barakat K, Tuszynski J (2011) Relaxed complex scheme suggests novel inhibitors for the lyase activity of DNA polymerase beta. J Mol Graph Model 29:702–716. https://doi.org/10.1016/j.jmgm.2010.12.003
Berman HM, Bhat TN, Bourne PE, Feng Z, Gilliland G, Weissig H, Westbrook J (2000) The Protein Data Bank and the challenge of structural genomics. Nat Struct Biol (7 Suppl):957–959. https://doi.org/10.1038/80734
Best RB, Zhu X, Shim J, Lopes PE, Mittal J, Feig M, Mackerell AD Jr (2012) Optimization of the additive CHARMM all-atom protein force field targeting improved sampling of the backbone phi, psi and side-chain chi(1) and chi(2) dihedral angles. J Chem Theory Comput 8:3257–3273. https://doi.org/10.1021/ct300400x
Brooks BR et al (2009) CHARMM: the biomolecular simulation program. J Comput Chem 30:1545–1614. https://doi.org/10.1002/jcc.21287
Buch I, Giorgino T, De Fabritiis G (2011) Complete reconstruction of an enzyme-inhibitor binding process by molecular dynamics simulations. Proc Natl Acad Sci U S A 108:10184–10189. https://doi.org/10.1073/pnas.1103547108
Campbell AJ, Lamb ML, Joseph-McCarthy D (2014) Ensemble-based docking using biased molecular dynamics. J Chem Inf Model 54:2127–2138. https://doi.org/10.1021/ci400729j
Carbo A, Gandour RD, Hontecillas R, Philipson N, Uren A, Bassaganya-Riera J (2016) An N,N-Bis(benzimidazolylpicolinoyl)piperazine (BT-11): a novel lanthionine synthetase C-like 2-based therapeutic for inflammatory bowel disease. J Med Chem 59:10113–10126. https://doi.org/10.1021/acs.jmedchem.6b00412
Case DA et al (2005) The Amber biomolecular simulation programs. J Comput Chem 26:1668–1688. https://doi.org/10.1002/jcc.20290
Case DA et al (2017) AMBER 2017. University of California, San Francisco
Cherkasov A et al (2014) QSAR modeling: where have you been? Where are you going to? J Med Chem 57:4977–5010. https://doi.org/10.1021/jm4004285
Clark AJ et al (2016) Prediction of protein-ligand binding poses via a combination of induced fit docking and metadynamics simulations. J Chem Theory Comput 12:2990–2998. https://doi.org/10.1021/acs.jctc.6b00201
Cumming JG, Davis AM, Muresan S, Haeberlein M, Chen H (2013) Chemical predictive modelling to improve compound quality. Nat Rev Drug Discov 12:948–962. https://doi.org/10.1038/nrd4128
de Ruiter A, Oostenbrink C (2011) Free energy calculations of protein-ligand interactions. Curr Opin Chem Biol 15:547–552. https://doi.org/10.1016/j.cbpa.2011.05.021
De Vivo M, Masetti M, Bottegoni G, Cavalli A (2016) Role of molecular dynamics and related methods in drug discovery. J Med Chem 59:4035–4061. https://doi.org/10.1021/acs.jmedchem.5b01684
Decherchi S, Berteotti A, Bottegoni G, Rocchia W, Cavalli A (2015) The ligand binding mechanism to purine nucleoside phosphorylase elucidated via molecular dynamics and machine learning. Nat Commun 6:6155. https://doi.org/10.1038/ncomms7155
Dias R, de Azevedo WF Jr (2008) Molecular docking algorithms. Curr Drug Targets 9:1040–1047
DiMasi JA, Grabowski HG, Hansen RW (2016) Innovation in the pharmaceutical industry: new estimates of R&D costs. J Health Econ 47:20–33. https://doi.org/10.1016/j.jhealeco.2016.01.012
Dobson CM (2004) Chemical space and biology. Nature 432:824–828. https://doi.org/10.1038/nature03192
Dror RO et al (2011) Pathway and mechanism of drug binding to G-protein-coupled receptors. Proc Natl Acad Sci U S A 108:13118–13123. https://doi.org/10.1073/pnas.1104614108
Drwal MN, Griffith R (2013) Combination of ligand- and structure-based methods in virtual screening. Drug Discov Today Technol 10:e395–e401. https://doi.org/10.1016/j.ddtec.2013.02.002
Durrant JD, McCammon JA (2011) Molecular dynamics simulations and drug discovery. BMC Biol 9:71. https://doi.org/10.1186/1741-7007-9-71
Fischer E (1894) Einfluss der configuration auf die wirkung der enzyme. Ber Dtsch Chem Ges 27:2985–2993
Foda ZH, Shan Y, Kim ET, Shaw DE, Seeliger MA (2015) A dynamically coupled allosteric network underlies binding cooperativity in Src kinase. Nat Commun 6:5939. https://doi.org/10.1038/ncomms6939
Ganesan A, Coote ML, Barakat K (2017) Molecular dynamics-driven drug discovery: leaping forward with confidence. Drug Discov Today 22:249–269. https://doi.org/10.1016/j.drudis.2016.11.001
Grant BJ, Lukman S, Hocker HJ, Sayyah J, Brown JH, McCammon JA, Gorfe AA (2011) Novel allosteric sites on Ras for lead generation. PLoS One 6:e25711. https://doi.org/10.1371/journal.pone.0025711
Halperin I, Ma B, Wolfson H, Nussinov R (2002) Principles of docking: an overview of search algorithms and a guide to scoring functions. Proteins 47:409–443. https://doi.org/10.1002/prot.10115
Harriman G et al (2016) Acetyl-CoA carboxylase inhibition by ND-630 reduces hepatic steatosis, improves insulin sensitivity, and modulates dyslipidemia in rats. Proc Natl Acad Sci U S A 113:E1796–E1805. https://doi.org/10.1073/pnas.1520686113
Hess B, Kutzner C, van der Spoel D, Lindahl E (2008) GROMACS 4: algorithms for highly efficient, load-balanced, and scalable molecular simulation. J Chem Theory Comput 4:435–447
Hillisch A, Heinrich N, Wild H (2015) Computational chemistry in the pharmaceutical industry: from childhood to adolescence. ChemMedChem 10:1958–1962. https://doi.org/10.1002/cmdc.201500346
Hou T, Wang J, Li Y, Wang W (2011a) Assessing the performance of the MM/PBSA and MM/GBSA methods. 1. The accuracy of binding free energy calculations based on molecular dynamics simulations. J Chem Inf Model 51:69–82. https://doi.org/10.1021/ci100275a
Hou T, Wang J, Li Y, Wang W (2011b) Assessing the performance of the molecular mechanics/Poisson Boltzmann surface area and molecular mechanics/generalized Born surface area methods. II. The accuracy of ranking poses generated from docking. J Comput Chem 32:866–877. https://doi.org/10.1002/jcc.21666
Irwin JJ, Shoichet BK (2005) ZINC—a free database of commercially available compounds for virtual screening. J Chem Inf Model 45:177–182. https://doi.org/10.1021/ci049714+
Ivetac A, McCammon JA (2010) Mapping the druggable allosteric space of G-protein coupled receptors: a fragment-based molecular dynamics approach. Chem Biol Drug Des 76:201–217. https://doi.org/10.1111/j.1747-0285.2010.01012.x
Jo S, Kim T, Iyer VG, Im W (2008) CHARMM-GUI: a web-based graphical user interface for CHARMM. J Comput Chem 29:1859–1865. https://doi.org/10.1002/jcc.20945
Kaminski GA, Friesner RA, Tirado-Rives J, Jorgensen WL (2001) Evaluation and reparametrization of the OPLS-AA force field for proteins via comparison with accurate quantum chemical calculations on peptides. J Phys Chem B 105:6474–6487. https://doi.org/10.1021/jp003919d
Kappel K, Wereszczynski J, Clubb RT, McCammon JA (2012) The binding mechanism, multiple binding modes, and allosteric regulation of Staphylococcus aureus Sortase A probed by molecular dynamics simulations. Protein Sci 21:1858–1871. https://doi.org/10.1002/pro.2168
Kim S et al (2016) PubChem substance and compound databases. Nucleic Acids Res 44:D1202–D1213. https://doi.org/10.1093/nar/gkv951
Kuntz ID, Blaney JM, Oatley SJ, Langridge R, Ferrin TE (1982) A geometric approach to macromolecule-ligand interactions. J Mol Biol 161:269–288
Lavecchia A, Di Giovanni C (2013) Virtual screening strategies in drug discovery: a critical review. Curr Med Chem 20:2839–2860
Lee CH, Huang HC, Juan HF (2011) Reviewing ligand-based rational drug design: the search for an ATP synthase inhibitor. Int J Mol Sci 12:5304–5318. https://doi.org/10.3390/ijms12085304
Leelananda SP, Lindert S (2016) Computational methods in drug discovery. Beilstein J Org Chem 12:2894–2718. https://doi.org/10.3762/bjoc.12.267
Lengauer T, Rarey M (1996) Computational methods for biomolecular docking. Curr Opin Struct Biol 6:402–406
Lewis SN, Bassaganya-Riera J, Bevan DR (2010) Virtual screening as a technique for PPAR modulator discovery. PPAR Res 2010:861238. https://doi.org/10.1155/2010/861238
Lill M (2013) Virtual screening in drug design. Methods Mol Biol 993:1–12. https://doi.org/10.1007/978-1-62703-342-8_1
Lin JH, Perryman AL, Schames JR, McCammon JA (2002) Computational drug design accommodating receptor flexibility: the relaxed complex scheme. J Am Chem Soc 124:5632–5633
Lin JH, Perryman AL, Schames JR, McCammon JA (2003) The relaxed complex method: accommodating receptor flexibility for drug design with an improved scoring scheme. Biopolymers 68:47–62. https://doi.org/10.1002/bip.10218
Lindorff-Larsen K, Piana S, Palmo K, Maragakis P, Klepeis JL, Dror RO, Shaw DE (2010) Improved side-chain torsion potentials for the Amber ff99SB protein force field. Proteins 78:1950–1958. https://doi.org/10.1002/prot.22711
Lu P, Bevan DR, Lewis SN, Hontecillas R, Bassaganya-Riera J (2011) Molecular modeling of lanthionine synthetase component C-like protein 2: a potential target for the discovery of novel type 2 diabetes prophylactics and therapeutics. J Mol Model 17:543–553. https://doi.org/10.1007/s00894-010-0748-y
Lu P et al (2012) Computational modeling-based discovery of novel classes of anti-inflammatory drugs that target lanthionine synthetase C-like protein 2. PLoS One 7:e34643. https://doi.org/10.1371/journal.pone.0034643
Lu P, Hontecillas R, Philipson CW, Bassaganya-Riera J (2014) Lanthionine synthetase component C-like protein 2: a new drug target for inflammatory diseases and diabetes. Curr Drug Targets 15:565–572
Macalino SJ, Gosu V, Hong S, Choi S (2015) Role of computer-aided drug design in modern drug discovery. Arch Pharm Res 38:1686–1701. https://doi.org/10.1007/s12272-015-0640-5
Marti-Renom MA, Stuart AC, Fiser A, Sanchez R, Melo F, Sali A (2000) Comparative protein structure modeling of genes and genomes. Annu Rev Biophys Biomol Struct 29:291–325. doi:29/1/291 [pii]. https://doi.org/10.1146/annurev.biophys.29.1.291
Meng XY, Zhang HX, Mezei M, Cui M (2011) Molecular docking: a powerful approach for structure-based drug discovery. Curr Comput Aided Drug Des 7:146–157. doi:BSP/CCADD/E-Pub/000030 [pii].
Mortier J, Rakers C, Bermudez M, Murgueitio MS, Riniker S, Wolber G (2015) The impact of molecular dynamics on drug design: applications for the characterization of ligand-macromolecule complexes. Drug Discov Today 20:686–702. https://doi.org/10.1016/j.drudis.2015.01.003
Muegge I, Bergner A, Kriegl JM (2017) Computer-aided drug design at Boehringer Ingelheim. J Comput Aided Mol Des 31:275–285. https://doi.org/10.1007/s10822-016-9975-3
Ng HW et al (2014) Competitive molecular docking approach for predicting estrogen receptor subtype alpha agonists and antagonists. BMC Bioinformatics 15(Suppl 11):S4. https://doi.org/10.1186/1471-2105-15-S11-S4
Oostenbrink C, Villa A, Mark AE, van Gunsteren WF (2004) A biomolecular force field based on the free enthalpy of hydration and solvation: the GROMOS force-field parameter sets 53A5 and 53A6. J Comput Chem 25:1656–1676. https://doi.org/10.1002/jcc.20090
Phillips JC et al (2005) Scalable molecular dynamics with NAMD. J Comput Chem 26:1781–1802. https://doi.org/10.1002/jcc.20289
Pirhadi S, Shiri F, Ghasemi JB (2013) Methods and applications of structure based pharmacophores in drug discovery. Curr Top Med Chem 13:1036–1047
Pronk S et al (2013) GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics 29:845–854. https://doi.org/10.1093/bioinformatics/btt055
Ripphausen P, Nisius B, Peltason L, Bajorath J (2010) Quo vadis, virtual screening? A comprehensive survey of prospective applications. J Med Chem 53:8461–8467. https://doi.org/10.1021/jm101020z
Sawyer JS et al (2003) Synthesis and activity of new aryl- and heteroaryl-substituted pyrazole inhibitors of the transforming growth factor-beta type I receptor kinase domain. J Med Chem 46:3953–3956. https://doi.org/10.1021/jm0205705
Schames JR, Henchman RH, Siegel JS, Sotriffer CA, Ni H, McCammon JA (2004) Discovery of a novel binding trench in HIV integrase. J Med Chem 47:1879–1881. https://doi.org/10.1021/jm0341913
Schlick T, Collepardo-Guevara R, Halvorsen LA, Jung S, Xiao X (2011) Biomolecular modeling and simulation: a field coming of age. Q Rev Biophys 44:191–228. https://doi.org/10.1017/S0033583510000284
Schüttelkopf AW, van Aalten DMF (2004) PRODRG: a tool for high-throughput crystallography of protein-ligand complexes. Acta Crystallogr D Biol Crystallogr 60:1355–1363
Sgobba M, Caporuscio F, Anighoro A, Portioli C, Rastelli G (2012) Application of a post-docking procedure based on MM-PBSA and MM-GBSA on single and multiple protein conformations. Eur J Med Chem 58:431–440. https://doi.org/10.1016/j.ejmech.2012.10.024
Shan Y, Kim ET, Eastwood MP, Dror RO, Seeliger MA, Shaw DE (2011) How does a drug molecule find its target binding site? J Am Chem Soc 133:9181–9183. https://doi.org/10.1021/ja202726y
Singh J et al (2003) Successful shape-based virtual screening: the discovery of a potent inhibitor of the type I TGFbeta receptor kinase (TbetaRI). Bioorg Med Chem Lett 13:4355–4359
Sliwoski G, Kothiwale S, Meiler J, Lowe EW Jr (2014) Computational methods in drug discovery. Pharmacol Rev 66:334–395. https://doi.org/10.1124/pr.112.007336
Stahura FL, Bajorath J (2004) Virtual screening methods that complement HTS. Comb Chem High Throughput Screen 7:259–269
Sterling T, Irwin JJ (2015) ZINC 15—ligand discovery for everyone. J Chem Inf Model 55:2324–2337. https://doi.org/10.1021/acs.jcim.5b00559
Talele TT, Khedkar SA, Rigby AC (2010) Successful applications of computer aided drug discovery: moving drugs from concept to the clinic. Curr Top Med Chem 10:127–141
Tautermann CS, Seeliger D, Kriegl JM (2015) What can we learn from molecular dynamics simulations for GPCR drug design? Comput Struct Biotechnol J 13:111–121. https://doi.org/10.1016/j.csbj.2014.12.002
Tong M, Seeliger MA (2015) Targeting conformational plasticity of protein kinases. ACS Chem Biol 10:190–200. https://doi.org/10.1021/cb500870a
Tropsha A (2010) Best practices for QSAR model development, validation, and exploitation. Mol Inform 29:476–488. https://doi.org/10.1002/minf.201000061
Virtanen SI, Niinivehmas SP, Pentikainen OT (2015) Case-specific performance of MM-PBSA, MM-GBSA, and SIE in virtual screening. J Mol Graph Model 62:303–318. https://doi.org/10.1016/j.jmgm.2015.10.012
Wang H, Duffy RA, Boykow GC, Chackalamannil S, Madison VS (2008) Identification of novel cannabinoid CB1 receptor antagonists by using virtual screening with a pharmacophore model. J Med Chem 51:2439–2446. https://doi.org/10.1021/jm701519h
Wang L 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. https://doi.org/10.1021/ja512751q
Wei BQ, Weaver LH, Ferrari AM, Matthews BW, Shoichet BK (2004) Testing a flexible-receptor docking algorithm in a model binding site. J Mol Biol 337:1161–1182. https://doi.org/10.1016/j.jmb.2004.02.015
Wermuth CG, Ganellin CR, Lindberg P, Mitscher LA (1998) Glossary of terms used in medicinal chemistry (IUPAC Recommendations 1998). Pure Appl Chem 70:1129–1143
Yousefinejad S, Hemmateenejad B (2015) Chemometrics tools in QSAR/QSPR studies: a historical perspective. Chemom Intell Lab 149:177–204. https://doi.org/10.1016/j.chemolab.2015.06.016
Zhao H, Caflisch A (2015) Molecular dynamics in drug design. Eur J Med Chem 91:4–14. https://doi.org/10.1016/j.ejmech.2014.08.004
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this chapter
Cite this chapter
Lu, P., Bevan, D.R., Leber, A., Hontecillas, R., Tubau-Juni, N., Bassaganya-Riera, J. (2018). Computer-Aided Drug Discovery. In: Bassaganya-Riera, J. (eds) Accelerated Path to Cures. Springer, Cham. https://doi.org/10.1007/978-3-319-73238-1_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-73238-1_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-73237-4
Online ISBN: 978-3-319-73238-1
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)