Skip to main content
SpringerLink
Log in

Virtual Screening for the Discovery of Active Principles from Natural Products

  • Chapter
  • First Online:
Natural Products as Source of Molecules with Therapeutic Potential

Abstract

Computational methods are a powerful knowledge-based a approach that helps to select plant material or natural products (NP) with an increased likelihood for biological activity. These methods enable the rationalization of biological activities of NP and contribute to putative protein-ligand binding characteristics of these molecules. In this way, focusing on information about highly ranked virtual hits from properly validated in silico models is a rationale to streamline experimental efforts. In silico approaches can focus on well-known constituents of herbal remedies as well as on any natural compound with relevant biological effects directly retrieved from the literature. They might further be helpful for the selection of promising starting material for an experimental work-up. This chapter provides a general overview to students and researchers, who will step in this emerging and exciting field of science. It gives a brief introduction into the field of cheminformatics and presents different virtual screening strategies implemented in pharmacognostic workflows to point out opportunities and challenges in NP-based drug discovery.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 169.99
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

  • Acharya C, Coop A, Polli JE et al (2011) Recent advances in ligand-based drug design: relevance and utility of the conformationally sampled pharmacophore approach. Curr Comput Aided Drug Des 7:10–22

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Adcock SA, Mccammon JA (2006) Molecular dynamics: survey of methods for simulating the activity of proteins. Chem Rev 106:1589–1615

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Adhami HR, Linder T, Kaehlig H et al (2012) Catechol alkenyls from Semecarpus anacardium: acetylcholinesterase inhibition and binding mode predictions. J Ethnopharmacol 139(1):142–8

    Article  CAS  PubMed  Google Scholar 

  • Akella LB, Decaprio D (2010) Cheminformatics approaches to analyze diversity in compound screening libraries. Curr Opin Chem Biol 14:325–330

    Article  CAS  PubMed  Google Scholar 

  • Anderson AC (2003) The process of structure-based drug design. Chem Biol 10:787–797

    Article  CAS  PubMed  Google Scholar 

  • Atanasov AG, Blunder M, Fakhrudin N et al (2013) Polyacetylenes from Notopterygium incisum – new selective partial agonists of peroxisome proliferator-activated receptor-gamma. PLoS One 8:e61755

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Baell JB (2016) Feeling nature’s PAINS: natural products, natural product drugs, and pan assay interference compounds (PAINS). J Nat Prod 79:616–628

    Article  CAS  PubMed  Google Scholar 

  • Bajorath J (2001) Selected concepts and investigations in compound classification, molecular descriptor analysis, and virtual screening. J Chem Inf Comput Sci 41:233–245

    Article  CAS  PubMed  Google Scholar 

  • Bajorath J (2017) Molecular similarity concepts for informatics applications. Methods Mol Biol 1526:231–245

    Article  CAS  PubMed  Google Scholar 

  • Bajusz D, Rácz A, Héberger K (2015) Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations? J Cheminform 7:20

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Balaban AT (1997) Neural networks in QSAR and drug design. In: J Devillers (ed) vol. 2 in the series: principles of QSAR and drug design. J Chem Inf Comput Sci 37:628–629

    Google Scholar 

  • Ban F, Dalal K, Li H et al (2017) Best practices of computer-aided drug discovery: lessons learned from the development of a preclinical candidate for prostate cancer with a new mechanism of action. J Chem Inf Model 57:1018–1028

    Article  CAS  PubMed  Google Scholar 

  • Bento AP, Gaulton A, Hersey A et al (2014) The ChEMBL bioactivity database: an update. Nucleic Acids Res 42:D1083–D1090

    Article  CAS  PubMed  Google Scholar 

  • Berman HM, Westbrook J, Feng Z et al (2000) The protein data bank. Nucleic Acids Res 28:235–242

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Berman H, Henrick K, Nakamura H (2003) Announcing the worldwide protein data bank. Nat Struct Mol Biol 10:980

    Article  CAS  Google Scholar 

  • Bock A, Bermudez M, Krebs F et al (2016) Ligand binding ensembles determine graded agonist efficacies at a G protein-coupled receptor. J Biol Chem 291:16375–16389

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Bowman GR, Bolin ER, Hart KM et al (2015) Discovery of multiple hidden allosteric sites by combining Markov state models and experiments. Proc Natl Acad Sci U S A 112:2734–2739

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Brandt BW, Heringa J, Leunissen JA (2008) SEQATOMS: a web tool for identifying missing regions in PDB in sequence context. Nucleic Acids Res 36:W255–W259

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Brown FK (1998) Chapter 35 – Chemoinformatics: what is it and how does it impact drug discovery. Annu Rep Med Chem 33:375–384

    CAS  Google Scholar 

  • Campbell AJ, Lamb ML, Joseph-Mccarthy D (2014) Ensemble-based docking using biased molecular dynamics. J Chem Inf Model 54:2127–2138

    Article  CAS  PubMed  Google Scholar 

  • Cavasotto CN, Orry AJ (2007) Ligand docking and structure-based virtual screening in drug discovery. Curr Top Med Chem 7:1006–1014

    Article  CAS  PubMed  Google Scholar 

  • Chavan S, Nicholls IA, Karlsson BC et al (2014) Towards global QSAR model building for acute toxicity: Munro database case study. Int J Mol Sci 15:18162–18174

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Chen YC (2015) Beware of docking! Trends Pharmacol Sci 36:78–95

    Article  PubMed  CAS  Google Scholar 

  • Chen X, Ung CY, Chen Y (2003) Can an in silico drug-target search method be used to probe potential mechanisms of medicinal plant ingredients? Nat Prod Rep 20:432–444

    Article  CAS  PubMed  Google Scholar 

  • Chen Y, De Bruyn Kops C, Kirchmair J (2017) Data resources for the computer-guided discovery of bioactive natural products. J Chem Inf Model 57:2099–2111

    Article  CAS  PubMed  Google Scholar 

  • Claude Cohen N (2007) Medicine pipeline: 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  Google Scholar 

  • Cordier C, Morton D, Murrison S et al (2008) Natural products as an inspiration in the diversity-oriented synthesis of bioactive compound libraries. Nat Prod Rep 25:719–737

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Corwin HA, Leo DH, Hoekman D (1995) Exploring QSAR: fundamentals and applications in chemistry and biology. Hydrophobic, electronic, and steric constants. American Chemical Society, Washington, DC

    Google Scholar 

  • Dalby A, Nourse JG, Hounshell WD et al (1992) Description of several chemical structure file formats used by computer programs developed at molecular design limited. J Chem Inf Comput Sci 32:244–255

    Article  CAS  Google Scholar 

  • Danishuddin KAU (2016) Descriptors and their selection methods in QSAR analysis: paradigm for drug design. Drug Discov Today 21:1291–1302

    Article  CAS  PubMed  Google Scholar 

  • De vivo M, Masetti M, Bottegoni G et al (2016) Role of molecular dynamics and related methods in drug discovery. J Med Chem 59:4035–4061

    Article  PubMed  CAS  Google Scholar 

  • Doman TN, Mcgovern SL, Witherbee BJ et al (2002) Molecular docking and high-throughput screening for novel inhibitors of protein tyrosine phosphatase-1B. J Med Chem 45:2213–2221

    Article  CAS  PubMed  Google Scholar 

  • Ebejer JP, Morris GM, Deane CM (2012) Freely available conformer generation methods: how good are they? J Chem Inf Model 52:1146–1158

    Article  CAS  PubMed  Google Scholar 

  • El-Houri RB, Mortier J, Murgueitio MS et al (2015) Identification of PPARγ agonists from natural sources using different in silico approaches. Planta Med 81:488–494

    CAS  PubMed  Google Scholar 

  • Evers A, Hessler G, Matter H et al (2005) Virtual screening of biogenic amine-binding G-protein coupled receptors: comparative evaluation of protein- and ligand-based virtual screening protocols. J Med Chem 48:5448–5465

    Article  CAS  PubMed  Google Scholar 

  • Feig M, Sugita Y (2013) Reaching new levels of realism in modeling biological macromolecules in cellular environments. J Mol Graph Model 45:144–156

    Article  CAS  PubMed  Google Scholar 

  • Fernandez-Leiro R, Scheres SHW (2016) Unravelling the structures of biological macromolecules by cryo-EM. Nature 537:339–346

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Free SM, Wilson JW (1964) A mathematical contribution to structure-activity studies. J Med Chem 7:395–399

    Article  CAS  PubMed  Google Scholar 

  • Friedrich NO, De Bruyn Kops C, Flachsenberg F et al (2017) Benchmarking commercial conformer ensemble generators. J Chem Inf Model 57:2719–2728

    Article  CAS  PubMed  Google Scholar 

  • 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. J Med Chem 49:6177–6196

    Article  CAS  PubMed  Google Scholar 

  • Fu W, Chen L, Wang Z et al (2016) Determination of the binding mode for anti-inflammatory natural product xanthohumol with myeloid differentiation protein 2. Drug Des Devel Ther 10:455–463

    CAS  PubMed  PubMed Central  Google Scholar 

  • Gasteiger J, Engel T (2003) Chemoinformatics: a textbook. Wiley-VCH, Weinheim

    Book  Google Scholar 

  • Gasteiger J (2016) Chemoinformatics: achievements and challenges, a personal view. Molecules 21:151

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Gaulton A, Bellis LJ, Bento AP et al (2012) ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res 40:D1100–D1107

    Article  CAS  PubMed  Google Scholar 

  • Gawehn E, Hiss JA, Schneider G (2016) Deep learning in drug discovery. Mol Inform 35:3–14

    Article  CAS  PubMed  Google Scholar 

  • Ghose AK, Viswanadhan VN, Wendoloski JJ (1999) A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of known drug databases. J Comb Chem 1:55–68

    Article  CAS  PubMed  Google Scholar 

  • Goldmann D, Pakfeifer P, Hering S et al (2015) Novel scaffolds for modulation of TRPV1 identified with pharmacophore modeling and virtual screening. Future Med Chem 7:243–256

    Article  CAS  PubMed  Google Scholar 

  • Gong J, Sun P, Jiang N et al (2014) New steroids with a rearranged skeleton as (h)P300 inhibitors from the sponge theonella swinhoei. Org Lett 16:2224–2227

    Article  CAS  PubMed  Google Scholar 

  • Grienke U, Mihaly-Bison J, Schuster D et al (2011) Pharmacophore-based discovery of FXR-agonists. Part II: identification of bioactive triterpenes from Ganoderma lucidum. Bioorg Med Chem 19(22):6779–6791

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Grienke U, Braun H, Seidel N et al (2014) Computer-guided approach to access the anti-influenza activity of licorice constituents. J Nat Prod 77:563–570

    Article  CAS  PubMed  Google Scholar 

  • Grienke U, Kaserer T, Pfluger F et al (2015) Accessing biological actions of Ganoderma secondary metabolites by in silico profiling. Phytochemistry 114:114–124

    Article  CAS  PubMed  Google Scholar 

  • Grosdidier A, Zoete V, Michielin O (2011) SwissDock, a protein-small molecule docking web service based on EADock DSS. Nucleic Acids Res 39:W270–W277

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Gu J, Gui Y, Chen L et al (2013) Use of natural products as chemical library for drug discovery and network pharmacology. PLoS One 8:e62839

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Gumbart JC, Roux B, Chipot C (2013) Standard binding free energies from computer simulations: what is the best strategy? J Chem Theory Comput 9:794–802

    Article  CAS  PubMed  Google Scholar 

  • Ha H, Debnath B, Odde S (2015) Discovery of novel CXCR2 inhibitors using ligand-based pharmacophore models. J Chem Inf Model 55:1720–1738

    Article  CAS  PubMed  Google Scholar 

  • Hansch C, Maloney PP, Fujita T et al (1962) Correlation of biological activity of phenoxyacetic acids with hammett substituent constants and partition coefficients. Nature 194:178–180

    Article  CAS  Google Scholar 

  • Harvey AL, Edrada-Ebel R, Quinn RJ (2015) The re-emergence of natural products for drug discovery in the genomics era. Nat Rev Drug Discov 14:111–129

    Article  CAS  PubMed  Google Scholar 

  • Hauser AS, Windshügel B (2016) LEADS-PEP: a benchmark data set for assessment of peptide docking performance. J Chem Inf Model 56:188–200

    Article  CAS  PubMed  Google Scholar 

  • Hauser AS, Attwood MM, Rask-Andersen M et al (2017) Trends in GPCR drug discovery: new agents, targets and indications. Nat Rev Drug Discov 16:829

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Hawkins PC, Skillman AG, Nicholls A (2007) Comparison of shape-matching and docking as virtual screening tools. J Med Chem 50:74–82

    Article  CAS  PubMed  Google Scholar 

  • Henrick K, Feng Z, Bluhm WF et al (2008) Remediation of the protein data bank archive. Nucleic Acids Res 36:D426–D433

    Article  CAS  PubMed  Google Scholar 

  • Hessler G, Baringhaus KH (2010) The scaffold hopping potential of pharmacophores. Drug Discov Today Technol 7:e263–e269

    Article  CAS  Google Scholar 

  • Hochleitner J, Akram M, Ueberall M et al (2017) A combinatorial approach for the discovery of cytochrome P450 2D6 inhibitors from nature. Sci Rep 7:8071

    Article  PubMed  PubMed Central  Google Scholar 

  • Hu Y, Stumpfe D, Bajorath J (2013) Advancing the activity cliff concept. F1000Research 2:199

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Huang SY, Grinter SZ, Zou X (2010) Scoring functions and their evaluation methods for protein-ligand docking: recent advances and future directions. Phys Chem Chem Phys 12:12899–12908

    Article  CAS  PubMed  Google Scholar 

  • Jain AN, Nicholls A (2008) Recommendations for evaluation of computational methods. J Comput Aided Mol Des 22:133–139

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Jayaseelan KV, Moreno P, Truszkowski A et al (2012) Natural product-likeness score revisited: an open-source, open-data implementation. BMC Bioinformatics 13:106

    Article  PubMed  PubMed Central  Google Scholar 

  • Jones G, Willett P, Glen RC et al (1997) Development and validation of a genetic algorithm for flexible docking. J Mol Biol 267:727–748

    Article  CAS  PubMed  Google Scholar 

  • Karaboga AS, Planesas JM, Petronin F et al (2013) Highly specific and sensitive pharmacophore model for identifying CXCR4 antagonists. Comparison with docking and shape-matching virtual screening performance. J Chem Inf Model 53:1043–1056

    Article  CAS  PubMed  Google Scholar 

  • Karelson M, Lobanov VS, Katritzky AR (1996) Quantum-chemical descriptors in QSAR/QSPR studies. Chem Rev 96:1027–1044

    Article  CAS  PubMed  Google Scholar 

  • Kaserer T, Schuster D, Rollinger JM (2018) Chapter 6.3. Chemoinformatics in natural product research. In: Applied chemoinformatics: achievements and future opportunities. Wiley-VCH, Weinheim

    Google Scholar 

  • Kim S, Thiessen PA, Bolton EE et al (2016) PubChem substance and compound databases. Nucleic Acids Res 44:D1202–D1213

    Article  CAS  PubMed  Google Scholar 

  • Kirchmair J, Markt P, Distinto S et al (2008) Evaluation of the performance of 3D virtual screening protocols: RMSD comparisons, enrichment assessments, and decoy selection – what can we learn from earlier mistakes? J Comput Aided Mol Des 22:213–228

    Article  CAS  PubMed  Google Scholar 

  • Kirchmair J, Distinto S, Markt P et al (2009) How to optimize shape-based virtual screening: choosing the right query and including chemical information. J Chem Inf Model 49:678–692

    Article  CAS  PubMed  Google Scholar 

  • Kirchmair J, Goller AH, Lang D et al (2015) Predicting drug metabolism: experiment and/or computation? Nat Rev Drug Discov 14:387–404

    Article  CAS  PubMed  Google Scholar 

  • Kirchweger B, Kratz JM, Ladurner A et al (2018) In silico workflow for the identification of natural products targeting GPBAR1. Front Chem 6:242

    Article  PubMed  PubMed Central  Google Scholar 

  • Kitchen DB, Decornez H, Furr JR (2004) Docking and scoring in virtual screening for drug discovery: methods and applications. Nat Rev Drug Discov 3:935–949

    Article  CAS  PubMed  Google Scholar 

  • Klabunde T, Evers A (2005) GPCR antitarget modeling: pharmacophore models for biogenic amine binding GPCRs to avoid GPCR-mediated side effects. Chembiochem 6:876–889

    Article  CAS  PubMed  Google Scholar 

  • Koeberle A, Werz O (2014) Multi-target approach for natural products in inflammation. Drug Discov Today 19:1871–1882

    Article  CAS  PubMed  Google Scholar 

  • Kortagere S, Krasowski MD, Ekins S (2009) The importance of discerning shape in molecular pharmacology. Trends Pharmacol Sci 30:138–147

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Kratz JM, Schuster D, Edtbauer M et al (2014) Experimentally validated HERG pharmacophore models as cardiotoxicity prediction tools. J Chem Inf Model 54:2887–2901

    Article  CAS  PubMed  Google Scholar 

  • Kratz JM, Mair CE, Oettl SK et al (2016) hERG channel blocking ipecac alkaloids identified by combined in silico – in vitro screening. Planta Med 82(11–12):1009–1015

    CAS  PubMed  Google Scholar 

  • Kratz JM, Grienke U, Scheel O et al (2017) Natural products modulating the hERG channel: heartaches and hope. Nat Prod Rep 34:957–980

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Lagorce D, Bouslama L, Becot J et al (2017) FAF-Drugs4: free ADME-tox filtering computations for chemical biology and early stages drug discovery. Bioinformatics 33:3658–3660

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  • Larsson J, Gottfries J, Muresan S et al (2007) ChemGPS-NP: tuned for navigation in biologically relevant chemical space. J Nat Prod 70:789–794

    Article  CAS  PubMed  Google Scholar 

  • Lavecchia A (2015) Machine-learning approaches in drug discovery: methods and applications. Drug Discov Today 20:318–331

    Article  PubMed  Google Scholar 

  • Lipinski CA, Lombardo F, Dominy BW et al (2001) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 46:3–26

    Article  CAS  PubMed  Google Scholar 

  • Liu K, Kokubo H (2017) Exploring the stability of ligand binding modes to proteins by molecular dynamics simulations: a cross-docking study. J Chem Inf Model 57:2514–2522

    Article  CAS  PubMed  Google Scholar 

  • Lo YC, Rensi SE, Torng W et al (2018) Machine learning in chemoinformatics and drug discovery. Drug Discov Today S1359-6446(17):30469–30465

    Google Scholar 

  • Ma DL, Chan DSH, Leung CH (2011) Molecular docking for virtual screening of natural product databases. Chem Sci 2:1656–1665

    Article  CAS  Google Scholar 

  • Macarron R (2006) Critical review of the role of HTS in drug discovery. Drug Discov Today 11:277–279

    Article  PubMed  Google Scholar 

  • Makeneni S, Thieker DF, Woods RJ (2018) Applying pose clustering and md simulations to eliminate false positives in molecular docking. J Chem Inf Model 58:605–614

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Malo M, Brive L, Luthman K et al (2010) Selective pharmacophore models of dopamine D(1) and D(2) full agonists based on extended pharmacophore features. ChemMedChem 5:232–246

    Article  CAS  PubMed  Google Scholar 

  • Matthias B, Clare H (2011) The Nagoya protocol on access to genetic resources and the fair and equitable sharing of benefits arising from their utilization to the convention on biological diversity. R.E.C.I.E.L. 20:47–61

    Google Scholar 

  • Medina-Franco JL, Maggiora GM, Giulianotti MA et al (2007) A similarity-based data-fusion approach to the visual characterization and comparison of compound databases. Chem Biol Drug Des 70:393–412

    Article  CAS  PubMed  Google Scholar 

  • Mollica L, Decherchi S, Zia SR et al (2015) Kinetics of protein-ligand unbinding via smoothed potential molecular dynamics simulations. Sci Rep 5:11539

    Article  PubMed  PubMed Central  Google Scholar 

  • Mortier J, Prévost JRC, Sydow D et al (2017) Arginase structure and inhibition: catalytic site plasticity reveals new modulation possibilities. Sci Rep 7:13616

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Mulholland K, Wu C (2016) Binding of telomestatin to a telomeric g-quadruplex dna probed by all-atom molecular dynamics simulations with explicit solvent. J Chem Inf Model 56:2093–2102

    Article  CAS  PubMed  Google Scholar 

  • Mysinger MM, Carchia M, Irwin JJ et al (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 

  • Nikolova N, Jaworska J (2003) Approaches to measure chemical similarity – a review. QSAR Comb Sci 22:1006–1026

    Article  CAS  Google Scholar 

  • O’Boyle NM (2012) Towards a Universal SMILES representation – a standard method to generate canonical SMILES based on the InChI. J Cheminform 4:22–22

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Oda A, Tsuchida K, Takakura T et al (2006) Comparison of consensus scoring strategies for evaluating computational models of protein-ligand complexes. J Chem Inf Model 46:380–391

    Article  CAS  PubMed  Google Scholar 

  • Osterberg F, Morris GM, Sanner MF et al (2002) Automated docking to multiple target structures: incorporation of protein mobility and structural water heterogeneity in AutoDock. Proteins 46:34–40

    Article  CAS  PubMed  Google Scholar 

  • Pagadala NS, Syed K, Tuszynski J (2017) Software for molecular docking: a review. Biophys Rev 9:91–102

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Pándy-Szekeres G, Munk C, Tsonkov TM et al (2018) GPCRdb in 2018: adding GPCR structure models and ligands. Nucleic Acids Res 46:D440–D446

    Article  PubMed  CAS  Google Scholar 

  • Pang YP, Kozikowski AP (1994) Prediction of the binding sites of huperzine A in acetylcholinesterase by docking studies. J Comput Aided Mol Des 8:669–681

    Article  CAS  PubMed  Google Scholar 

  • Pardridge WM (2005) The blood-brain barrier: bottleneck in brain drug development. NeuroRx 2:3–14

    Article  PubMed  PubMed Central  Google Scholar 

  • Payne DJ, Gwynn MN, Holmes DJ et al (2006) Drugs for bad bugs: confronting the challenges of antibacterial discovery. Nat Rev Drug Discov 6:29

    Article  PubMed  CAS  Google Scholar 

  • Pereira JC, Caffarena ER, Dos Santos CN (2016) boosting docking-based virtual screening with deep learning. J Chem Inf Model 56:2495–2506

    Article  CAS  PubMed  Google Scholar 

  • Pye CR, Bertin MJ, Lokey RS et al (2017) Retrospective analysis of natural products provides insights for future discovery trends. Proc Natl Acad Sci U S A 114:5601–5606

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Rastelli G, Degliesposti G, Del Rio A et al (2009) Binding estimation after refinement, a new automated procedure for the refinement and rescoring of docked ligands in virtual screening. Chem Biol Drug Des 73:283–286

    Article  CAS  PubMed  Google Scholar 

  • Reker D, Perna AM, Rodrigues T et al (2014) Revealing the macromolecular targets of complex natural products. Nat Chem 6:1072–1078

    Article  CAS  PubMed  Google Scholar 

  • Ren J, He Y, Chen W et al (2014) Thermodynamic and structural characterization of halogen bonding in protein-ligand interactions: a case study of PDE5 and its inhibitors. J Med Chem 57:3588–3593

    Article  CAS  PubMed  Google Scholar 

  • Rester U (2008) From virtuality to reality – virtual screening in lead discovery and lead optimization: a medicinal chemistry perspective. Curr Opin Drug Discov Devel 11:559–568

    CAS  PubMed  Google Scholar 

  • Reymond JL, Van Deursen R, Blum LC et al (2010) Chemical space as a source for new drugs. Med Chem Comm 1:30–38

    Article  CAS  Google Scholar 

  • Rodrigues T, Sieglitz F, Somovilla VJ et al (2016) Unveiling (−)-Englerin A as a modulator of l-type calcium channels. Angewandte Chemie (International Ed in English) 55:11077–11081

    Article  CAS  Google Scholar 

  • Rollinger JM (2009) Accessing target information by virtual parallel screening – the impact on natural product research. Phytochem Lett 2:53–58

    Article  CAS  Google Scholar 

  • Rollinger JM, Wolber G (2011) Computational approaches for the discovery of natural lead structures. In: Bioactive compounds from natural sources, Natural products as lead compounds in drug discovery, 2nd edn. CRC Press, Boca Raton, pp 167–186

    Google Scholar 

  • Rollinger JM, Haupt S, Stuppner H et al (2004) Combining ethnopharmacology and virtual screening for lead structure discovery: COX-inhibitors as application example. J Chem Inf Comput Sci 44:480–488

    Article  CAS  PubMed  Google Scholar 

  • Rollinger JM, Bodensieck A, Seger A et al (2005) Discovering COX-inhibiting constituents of Morus root bark: activity-guided versus computer-aided methods. Planta Med 71:399–405

    Article  CAS  PubMed  Google Scholar 

  • Rollinger JM, Langer T, Stuppner H (2006a) Integrated in silico tools for exploiting the natural products’ bioactivity. Planta Med 72:671–678

    Article  CAS  PubMed  Google Scholar 

  • Rollinger JM, Langer T, Stuppner H (2006b) Strategies for efficient lead structure discovery from natural products. Curr Med Chem 13:1491–1507

    Article  CAS  PubMed  Google Scholar 

  • Rollinger JM, Steindl TM, Schuster D et al (2008) Structure-based virtual screening for the discovery of natural inhibitors for human rhinovirus coat protein. J Med Chem 51:842–851

    Article  CAS  PubMed  Google Scholar 

  • Rollinger JM, Schuster D, Danzl B et al (2009) In silico target fishing for rationalized ligand discovery exemplified on constituents of Ruta graveolens. Planta Med 75:195–204

    Article  CAS  PubMed  Google Scholar 

  • Rush TS, Grant JA, Mosyak L et al (2005) A shape-based 3-D scaffold hopping method and its application to a bacterial protein−protein interaction. J Med Chem 48:1489–1495

    Article  CAS  PubMed  Google Scholar 

  • Sabbadin D, Ciancetta A, Moro S (2014) Bridging molecular docking to membrane molecular dynamics to investigate GPCR-Ligand recognition: the human A2A adenosine receptor as a key study. J Chem Inf Model 54:169–183

    Article  CAS  PubMed  Google Scholar 

  • Santos R, Ursu O, Gaulton A (2016) A comprehensive map of molecular drug targets. Nat Rev Drug Discov 16:19

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Sawada R, Kotera M, Yamanishi Y (2014) Benchmarking a wide range of chemical descriptors for drug-target interaction prediction using a chemogenomic approach. Mol Inform 33:719–731

    CAS  PubMed  Google Scholar 

  • Schneider G (2010) Virtual screening: an endless staircase? Nat Rev Drug Discov 9:273–276

    Article  CAS  PubMed  Google Scholar 

  • Schneider G (2017) Automating drug discovery. Nat Rev Drug Discov 17:97

    Article  PubMed  CAS  Google Scholar 

  • Schneider P, Schneider G (2017) A computational method for unveiling the target promiscuity of pharmacologically active compounds. Angew Chem Int Ed Engl 56:11520–11524

    Article  CAS  PubMed  Google Scholar 

  • Schuster D, Waltenberger B, Kirchmair J et al (2010) Predicting cyclooxygenase inhibition by three-dimensional pharmacophoric profiling. Part I: model generation, validation and applicability in ethnopharmacology. Mol Inform 29:75–86

    Article  CAS  PubMed  Google Scholar 

  • Schuster D, Markt P, Grienke U et al (2011) Pharmacophore-based discovery of FXR agonists. Part I: model development and experimental validation. Bioorganic Med Chem 19:7168–7180

    Article  CAS  Google Scholar 

  • Scior T, Bender A, Tresadern G et al (2012) Recognizing pitfalls in virtual screening: a critical review. J Chem Inf Model 52:867–881

    Article  CAS  PubMed  Google Scholar 

  • Seidel T, Ibis G, Bendix F et al (2010) Strategies for 3D pharmacophore-based virtual screening. Drug Discov Today Technol 7:e221–e228

    Article  CAS  Google Scholar 

  • Sheridan RP (2008) Alternative global goodness metrics and sensitivity analysis: heuristics to check the robustness of conclusions from studies comparing virtual screening methods. J Chem Inf Model 48:426–433

    Article  CAS  PubMed  Google Scholar 

  • Shin WH, Zhu X, Bures MG et al (2015) Three-dimensional compound comparison methods and their application in drug discovery. Molecules 20:12841–12862

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Shoichet BK, Mcgovern SL, Wei B et al (2002) Lead discovery using molecular docking. Curr Opin Chem Biol 6:439–446

    Article  CAS  PubMed  Google Scholar 

  • Sichao W, Youyon GL, Lei X et al (2013) Recent developments in computational prediction of hERG blockage. Curr Top Med Chem 13:1317–1326

    Article  CAS  Google Scholar 

  • Singh N, Guha R, Giulianotti MA et al (2009) Chemoinformatic analysis of combinatorial libraries, drugs, natural products, and molecular libraries small molecule repository. J Chem Inf Model 49:1010–1024

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Sliwoski G, Kothiwale S, Meiler J et al (2014) Computational methods in drug discovery. Pharmacol Rev 66:334–395

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Sliwoski G, Mendenhall J, Meiler J (2016) Autocorrelation descriptor improvements for QSAR: 2DA_Sign and 3DA_Sign. JCAMD 30:209–217

    CAS  Google Scholar 

  • Sohn YS, Park C, Lee Y et al (2013) Multi-conformation dynamic pharmacophore modeling of the peroxisome proliferator-activated receptor γ for the discovery of novel agonists. J Mol Graph Model 46:1–9

    Article  CAS  PubMed  Google Scholar 

  • Sperandio O, Mouawad L, Pinto E et al (2010) How to choose relevant multiple receptor conformations for virtual screening: a test case of Cdk2 and normal mode analysis. Eur Biophys J 39:1365–1372

    Article  CAS  PubMed  Google Scholar 

  • Spitzer GM, Heiss M, Mangold M et al (2010) One Concept, three implementations of 3D pharmacophore-based virtual screening: distinct coverage of chemical search space. J Chem Inf Model 50:1241–1247

    Article  CAS  PubMed  Google Scholar 

  • Spyrakis F, Benedetti P, Decherchi S et al (2015) A Pipeline to enhance ligand virtual screening: integrating molecular dynamics and fingerprints for ligand and proteins. J Chem Inf Model 55:2256–2274

    Article  CAS  PubMed  Google Scholar 

  • Steindl TM, Schuster D, Laggner C et al (2006a) Parallel screening: a novel concept in pharmacophore modeling and virtual screening. J Chem Inf Model 46:2146–2157

    Article  CAS  PubMed  Google Scholar 

  • Steindl TM, Schuster D, Wolber G et al (2006b) High-throughput structure-based pharmacophore modelling as a basis for successful parallel virtual screening. JCAMD 20:703–715

    CAS  Google Scholar 

  • Strohl WR (2000) The role of natural products in a modern drug discovery program. Drug Discov Today 5:39–41

    Article  CAS  PubMed  Google Scholar 

  • Stumpfe D, De La Vega De Leon A, Dimova D et al (2014) Advancing the activity cliff concept, part II. F1000Res 3:75

    Article  PubMed  PubMed Central  Google Scholar 

  • Su H, Yan J, Xu J et al (2015) Stepwise high-throughput virtual screening of Rho kinase inhibitors from natural product library and potential therapeutics for pulmonary hypertension. Pharm Biol 53:1201–1206

    Article  CAS  PubMed  Google Scholar 

  • Tarcsay A, Paragi G, Vass M et al (2013) The impact of molecular dynamics sampling on the performance of virtual screening against GPCRs. J Chem Inf Model 53:2990–2999

    Article  CAS  PubMed  Google Scholar 

  • Taylor RD, Jewsbury PJ, Essex JW (2002) A review of protein-small molecule docking methods. JCAMD 16:151–166

    CAS  Google Scholar 

  • Tetko IV (2003) The WWW as a tool to obtain molecular parameters. Mini Rev Med Chem 3:809–820

    Article  CAS  PubMed  Google Scholar 

  • Tian S, Sun H, Pan P et al (2014) Assessing an ensemble docking-based virtual screening strategy for kinase targets by considering protein flexibility. J Chem Inf Model 54:2664–2679

    Article  CAS  PubMed  Google Scholar 

  • Todeschini R, Consonni V (2008) Handbook of molecular descriptors. Wiley-VCH, Weinheim

    Google Scholar 

  • Truchon JF, Bayly CI (2007) Evaluating virtual screening methods: good and bad metrics for the “early recognition” problem. J Chem Inf Model 47:488–508

    Article  CAS  PubMed  Google Scholar 

  • Van Drie JH (2010) History of 3D pharmacophore searching: commercial, academic and open-source tools. Drug Discov Today Technol 7:e255–e262

    Article  CAS  Google Scholar 

  • Varnek A, Baskin I (2012) Machine learning methods for property prediction in chemoinformatics: quo vadis? J Chem Inf Model 52:1413–1437

    Article  CAS  PubMed  Google Scholar 

  • Veber DF, Johnson SR, Cheng HY et al (2002) Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem 45:2615–2623

    Article  CAS  PubMed  Google Scholar 

  • Vuorinen A, Engeli R, Meyer A et al (2014) Ligand-based pharmacophore modeling and virtual screening for the discovery of novel 17β-Hydroxysteroid dehydrogenase 2 inhibitors. J Med Chem 57:5995–6007

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Waltenberger B, Atanasov AG, Heiss EH et al (2016) Drugs from nature targeting inflammation (DNTI): a successful Austrian interdisciplinary network project. Monatsh Chem 47:479–491

    Article  CAS  Google Scholar 

  • Wang G, Zhu W (2016) Molecular docking for drug discovery and development: a widely used approach but far from perfect. Future Med Chem 8:1707–1710

    Article  CAS  PubMed  Google Scholar 

  • Wang JC, Chu PY, Chen CM et al (2012) idTarget: a web server for identifying protein targets of small chemical molecules with robust scoring functions and a divide-and-conquer docking approach. Nucleic Acids Res 40:W393–W399

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Wang Z, Sun H, Yao X et al (2016) Comprehensive evaluation of ten docking programs on a diverse set of protein-ligand complexes: the prediction accuracy of sampling power and scoring power. Phys Chem Chem Phys 18:12964–12975

    Article  CAS  PubMed  Google Scholar 

  • Warren GL, Andrews CW, Capelli AM et al (2006) A critical assessment of docking programs and scoring functions. J Med Chem 49:5912–5931

    Article  CAS  PubMed  Google Scholar 

  • Weininger D (1988) SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J Chem Inf Model 28:31–36

    Article  CAS  Google Scholar 

  • Wermuth CG, Ganellin CR, Lindberg P et al (1998) Glossary of terms used in medicinal chemistry (IUPAC recommendations 1998). Pure Appl Chem 70:1129

    Article  CAS  Google Scholar 

  • Wetzel S, Schuffenhauer A, Roggo S et al (2007) Cheminformatic analysis of natural products and their chemical space. Chimia 61:355–360

    Article  CAS  Google Scholar 

  • Wieder M, Garon A, Perricone U et al (2017) Common Hits approach: combining pharmacophore modeling and molecular dynamics simulations. J Chem Inf Model 57:365–385

    Article  CAS  PubMed  Google Scholar 

  • Wójcikowski M, Ballester PJ, Siedlecki P (2017) Performance of machine-learning scoring functions in structure-based virtual screening. Sci Rep 7:46710

    Article  PubMed  PubMed Central  Google Scholar 

  • Yan SF, King FJ, He Y et al (2006) Learning from the data: mining of large high-throughput screening databases. J Chem Inf Model 46:2381–2395

    Article  CAS  PubMed  Google Scholar 

  • Yang Y, Xu Z, Zhang Z et al (2015) Like-charge guanidinium pairing between ligand and receptor: an unusual interaction for drug discovery and design? J Phys Chem B 119:11988–11997

    Article  CAS  PubMed  Google Scholar 

  • Zhu T, Cao S, Su PC et al (2013) Hit identification and optimization in virtual screening: practical recommendations based on a critical literature analysis. J Med Chem 56:6560–6572

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Pharmacognosy, University of Vienna, Vienna, Austria

    Benjamin Kirchweger & Judith M. Rollinger

Authors
  1. Benjamin Kirchweger
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Judith M. Rollinger
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Judith M. Rollinger .

Editor information

Editors and Affiliations

  1. University of Vale do Itajaí – UNIVALI, Itajaí, Santa Catarina, Brazil

    Valdir Cechinel Filho

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Kirchweger, B., Rollinger, J.M. (2018). Virtual Screening for the Discovery of Active Principles from Natural Products. In: Cechinel Filho, V. (eds) Natural Products as Source of Molecules with Therapeutic Potential. Springer, Cham. https://doi.org/10.1007/978-3-030-00545-0_9

Download citation

Publish with us

Policies and ethics

Search

Navigation