Skip to main content

Advances in Pharmacophore Modeling and Its Role in Drug Designing

  • Chapter
  • First Online:
Computer-Aided Drug Design

Abstract

Pharmacophore modeling is a central method in the ligand-based drug designing module. Its basis lies in developing a scaffold or an empirical molecule based on a group of known inhibitors to a target. The empirical molecule will contain features that are common to the known inhibitors and specified as donors, acceptors, rings, positively charged, or negatively charged. These five features or a combination of some of these features at specific distances make a pharmacophore. This pharmacophore facilitates the identification of other novel compounds that are specific and sensitive as well as effective inhibitors to a receptor. This method is particularly effective when the structural annotations are unavailable for the target. Thus pharmacophore modeling is a tool in drug discovery where screening of the pharmacophore built leads to the discovery of novel compounds against the target. Using these techniques as well as variations of these techniques, millions of compounds can be screened in a matter of hours to shortlist actives. Variations might be based on building a pharmacophore by the energy contribution of features in a single molecule against a specific target. Otherwise, based on only the geometric features of the active site in a target, a pharmacophore can be designed. Thus a designed pharmacophore can be used to screen novel agonists and antagonists that are specific to targets, to screen toxicants, to identify unknown targets, and to screen out best molecular docking results.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 159.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 159.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Alig L, Alsenz J, Andjelkovic M, Bendels S, Benardeau A, Bleicher K, Bourson A, David-Pierson P, Guba W, Hildbrand S, Kube D, Lubbers T, Mayweg AV, Narquizian R, Neidhart W, Nettekoven M, Plancher JM, Rocha C, Rogers-Evans M, Rover S, Schneider G, Taylor S, Waldmeier P (2008) Benzodioxoles: novel cannabinoid-1 receptor inverse agonists for the treatment of obesity. J Med Chem 51:2115–2127

    Article  CAS  PubMed  Google Scholar 

  • Böhm HJ (1993) A novel computational tool for automated structure-based drug design. J Mol Recognit 6:131–137

    Article  PubMed  Google Scholar 

  • Choudhari PB, Bhatia MS, Jadhav SD (2012) Pharmacophore identification and QSAR studies on substituted benzoxazinone as antiplatelet agents: KNN-MFA approach. Sci Pharm 80:283–294

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Choudhury C, Priyakumar UD, Sastry GN (2015) Dynamics based pharmacophore models for screening potential inhibitors of mycobacterial cyclopropane synthase. J Chem Inf Model 55:848–860

    Article  CAS  PubMed  Google Scholar 

  • Cohen NC (2007) Structure-based drug design and the discovery of aliskiren (tekturna): perseverance and creativity to overcome a R&D pipeline challenge. Chem Biol Drug Des 70:557–565

    Article  CAS  PubMed  Google Scholar 

  • Dalkas GA, Vlachakis D, Tsagkrasoulis D, Kastania A, Kossida S (2013) State-of-the-art technology in modern computer-aided drug design. Brief Bioinform 14:745–752

    Article  PubMed  Google Scholar 

  • Dixon SL, Smondyrev AM, Knoll EH, Rao SN, Shaw DE, Friesner RA (2006) PHASE: a new engine for pharmacophore perception, 3D QSAR model development, and 3D database screening: 1. Methodology and preliminary results. J Comput Aided Mol Des 20(10–11):647–671

    Article  CAS  PubMed  Google Scholar 

  • Ekins S, Freundlich JS, Coffee M (2014) A common feature pharmacophore for FDA-approved drugs inhibiting the Ebola virus. F1000Res 3:277

    Article  PubMed  PubMed Central  Google Scholar 

  • Fan F, Warshaviak D, Hamadeh HK, Dunn RT II (2019) The integration of pharmacophore-based 3D QSAR modeling and virtual screening in safety profiling: a case study to identify antagonistic activities against adenosine receptor, A2A, using 1,897 known drugs. PLoS One 14(1):e0204378

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Fei J, Zhou L, Liu T, Tang XY (2013) Pharmacophore modeling, virtual screening, and molecular docking studies for discovery of novel akt2 inhibitors. Int J Med Sci 10:265–275

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Guner OF, Bowen JP (2013) Pharmacophore modeling for ADME. Curr Top Med Chem 13:1327–1342

    Article  CAS  PubMed  Google Scholar 

  • Hartenfeller M, Schneider G (2011) De novo drug design. Methods Mol Biol 672:299–323

    Article  CAS  PubMed  Google Scholar 

  • Horvath D (2011) Pharmacophore-based virtual screening. Methods Mol Biol 672:261–298

    Article  CAS  PubMed  Google Scholar 

  • Juan Alvarez BS (2005) Virtual screening in drug discovery. CRC Press, Boca Raton

    Book  Google Scholar 

  • Kalva S, Vinod D, Saleena LM (2014) Combined structure- and ligand-based pharmacophore modeling and molecular dynamics simulation studies to identify selective inhibitors of MMP-8. J Mol Model 20:2191

    Article  PubMed  CAS  Google Scholar 

  • Kalva S, Agrawal N, Skelton A, Saleena LM (2016) Identification of novel selective MMP-9 inhibitors as potential anti-metastatic lead using structure-based hierarchical virtual screening and molecular dynamics simulation. Mol BioSyst 12:2519–2531

    Article  CAS  PubMed  Google Scholar 

  • Kaserer T, Beck KR, Akram M, Odermatt A, Schuster D (2015) Pharmacophore models and pharmacophore-based virtual screening: concepts and applications exemplified on hydroxysteroid dehydrogenases. Molecules 20:22799–22832

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Koes DR, Camacho CJ (2011) Pharmer: efficient and exact pharmacophore search. J Chem Inf Model 51:1307–1314

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Langer T, Wolber G (2004) Pharmacophore definition and 3D searches. Drug Discov Today Technol 1(3):203–207

    Article  CAS  PubMed  Google Scholar 

  • Levit A, Yarnitzky T, Wiener A, Meidan R, Niv MY (2011) Modeling of human prokineticin receptors: interactions with novel small-molecule binders and potential off-target drugs. PLoS One 6(11):e27990

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Li R-J, Wang Y-L, Wang QH, Wang J, Cheng MS (2015) In silico design of human IMPDH inhibitors using pharmacophore mapping and molecular docking approaches. Comput Math Methods Med 2015:1–11

    Google Scholar 

  • Lin SK (2000) Pharmacophore perception, development and use in drug design. Edited by Osman F Guner. Molecules 5(7):987–989

    Article  Google Scholar 

  • Liu X, Zhu F, Ma XH, Shi Z, Yang SY, Wei YQ, ChenY Z (2013) Predicting targeted polypharmacology for drug repositioning and multi-target drug discovery. Curr Med Chem 20:1646–1661

    Article  CAS  PubMed  Google Scholar 

  • Loving K, Salam NK, Sherman W (2009) Energetic analysis of fragment docking and application to structure-based pharmacophore hypothesis generation. J Comput Aided Mol Des 23(8):541–554

    Article  CAS  PubMed  Google Scholar 

  • Machaba KE, Mhlongo NN, Dokurugu YM, Soliman ME (2017) Tailored-pharmacophore model to enhance virtual screening and drug discovery: a case study on the identification of potential inhibitors against drug-resistant Mycobacterium tuberculosis (3r)-hydroxyacyl-ACP dehydratases. Future Med Chem 9:1055–1071

    Article  CAS  PubMed  Google Scholar 

  • Mcgregor MJ, Muskal SM (1999) Pharmacophore fingerprinting. 1. Application to QSAR and focused library design. J Chem Inf Comput Sci 39:569–574

    Article  CAS  PubMed  Google Scholar 

  • Mcgregor MJ, Muskal SM (2000) Pharmacophore fingerprinting. 2. Application to primary library design. J Chem Inf Comput Sci 40:117–125

    Article  CAS  PubMed  Google Scholar 

  • Merz K Jr, Ringe D, Reynolds C (2010) Drug design: structure- and ligand-based approaches. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Mysinger MM, Carchia M, Irwin JJ, Shoichet BK (2012) Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking. J Med Chem 55:6582–6594

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Pirhadi S, Shiri FJ, Ghasemi B (2013) Methods and applications of structure based pharmacophores in drug discovery. Curr Top Med Chem 13(9):1036–1047

    Article  CAS  PubMed  Google Scholar 

  • Qing X, Yin Lee X, De Raeymaecker J, Tame J, Zhang K, De Maeyer M, Voet A (2014) Pharmacophore modeling: advances, limitations, and current utility in drug discovery. J Recept Lig Channel Res 7:81–92

    CAS  Google Scholar 

  • Sahin K, Saripinar E (2020) A novel hybrid method named electron conformational genetic algorithm as a 4D QSAR investigation to calculate the biological activity of the tetrahydrodibenzazosines. J Comput Chem 41:1091–1104

    Article  CAS  PubMed  Google Scholar 

  • Salim AA, Kinghorn AD (2008) Drug discovery from plants. In: Ramawat KG, Mérillon JM (eds) Bioactive molecules and medicinal plants. Springer, Heidelberg, pp 1–24

    Google Scholar 

  • Sanders MP, Barbosa AJ, Zarzycka B, Nicolaes GA, Klomp JP, De Vlieg J, Del Rio A (2012) Comparative analysis of pharmacophore screening tools. J Chem Inf Model 52:1607–1620

    Article  CAS  PubMed  Google Scholar 

  • Sharma R, Dhingra N, Patil S (2016) COMFA, COMSIA, HQSAR and molecular docking analysis of ionone-based chalcone derivatives as antiprostate cancer activity. Indian J Pharm Sci 78:54–64

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Shiri F, Pirhadi S, Ghasemi B (2019) Dynamic structure based pharmacophore modeling of the Acetylcholinesterase reveals several potential inhibitors. J Biomol Struct Dyn 37(7):1800–1812

    Article  CAS  PubMed  Google Scholar 

  • Soliman MES (2013) A hybrid structure/pharmacophore-based virtual screening approach to design potential leads: a computer-aided design of South African HIV-1 subtype C protease inhibitors. Drug Dev Res 74:283–295

    Article  CAS  Google Scholar 

  • Summa V, Petrocchi A, Matassa VG, Gardelli C, Muraglia E, Rowley M, Paz OG, Laufer R, Monteagudo E, Pace P (2006) 4,5-dihydroxypyrimidine carboxamides and N-alkyl-5-hydroxypyrimidinone carboxamides are potent, selective HIV integrase inhibitors with good pharmacokinetic profiles in preclinical species. J Med Chem 49:6646–6649

    Article  CAS  PubMed  Google Scholar 

  • Swaminathan P, Kalva S, Saleena LM (2014) E-pharmacophore and molecular dynamics study of flavonols and dihydroflavonols as inhibitors against dihydroorotate dehydrogenase. Comb Chem High Throughput Screen 17:663–673

    Article  CAS  PubMed  Google Scholar 

  • 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

    Article  CAS  PubMed  Google Scholar 

  • Testa B (2012) To Monty kier, a friendly tribute. Curr Comput Aided Drug Des 8:85–86

    Article  CAS  PubMed  Google Scholar 

  • Thai KM, Ngo TD, Tran TD, Le MT (2013) Pharmacophore modeling for antitargets. Curr Top Med Chem 13:1002–1014

    Article  CAS  PubMed  Google Scholar 

  • Thangapandian S, John S, Lee Y, Kim S, Lee KW (2011) Dynamic structure-based pharmacophore model development: a new and effective addition in the histone deacetylase 8 (hdac8) inhibitor discovery. Int J Mol Sci 12:9440–9462

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Triballeau N, Acher F, Brabet I, Pin JP, Bertrand HO (2005) Virtual screening workflow development guided by the “receiver operating characteristic” curve approach. Application to high-throughput docking on metabotropic glutamate receptor subtype 4. J Med Chem 48:2534–2547

    Article  CAS  PubMed  Google Scholar 

  • Van Drie JH (2012) Generation of three-dimensional pharmacophore models. WIREs Comput Mol Sci 3:449–464

    Article  CAS  Google Scholar 

  • Verma J, Khedkar VM, Coutinho EC (2010) 3D-QSAR in drug design—a review. Curr Top Med Chem 10:95–115

    Article  CAS  PubMed  Google Scholar 

  • VLifeMDS (2010) Molecular design suite. VLife Sciences Technologies, Pune. www.vlifesciences.com

    Google Scholar 

  • Wadood A, Mehmood A, Khan H, Ilyas M, Ahmad A, Alarjah M, Abu-Izneid T (2017) Epitopes based drug design for dengue virus envelope protein: a computational approach. Comput Biol Chem 71:52–160

    Article  CAS  Google Scholar 

  • Wang F, Chen Y (2013) Pharmacophore models generation by catalyst and phase consensus-based virtual screening protocol against Pi3kα inhibitors. Mol Simul 39:529–544

    Article  CAS  Google Scholar 

  • Warszycki D, Mordalski S, Kristiansen K, Kafel R, Sylte I, Chilmonczyk Z, Bojarski AJ (2013) A linear combination of pharmacophore hypotheses as a new tool in search of new active compounds—an application for 5-HT1A receptor ligands. PLoS One 8:E84510

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Woods DD (1940) The anti-sulphanilamide activity (in vitro) of P-aminobenzoic acid and related compounds. Chem Ind 59:133–134

    Google Scholar 

  • Yang SY (2010) Pharmacophore modeling and applications in drug discovery: challenges and recent advances. Drug Discov Today 15:444–450

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgement

The author thanks the Editor, Dr. Dev Bukhsh Singh, for his critical comments and encouragement to write this chapter. The author also thanks SRMIST and the Department of Biotechnology for their support.

Competing Interest

The author declares that there are no competing interests.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Priya Swaminathan .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Swaminathan, P. (2020). Advances in Pharmacophore Modeling and Its Role in Drug Designing. In: Singh, D.B. (eds) Computer-Aided Drug Design. Springer, Singapore. https://doi.org/10.1007/978-981-15-6815-2_10

Download citation

Publish with us

Policies and ethics