Abstract
Pharmacophore-based techniques currently are an integral part of many computer-aided drug design workflows and have been successfully and extensively applied for tasks such as virtual screening, de novo design, and lead optimization. Pharmacophore models can be derived both in a receptor-based and in a ligand-based manner, and provide an abstract description of essential non-bonded interactions that typically occur between small-molecule ligands and macromolecular targets. Due to their simplistic and abstract nature, pharmacophores are both perfectly suited for efficient computer processing and easy to comprehend by life and physical scientists. As a consequence, they have also proven to be a valuable tool for communicating between computational and medicinal chemists.
This chapter aims to provide a short overview of the pharmacophore concept and its applications in modern computer-aided drug design. The chapter is divided into three distinct parts. The first section contains a brief introduction to the pharmacophore concept. The second section provides a description of the most common nonbonded interaction types and their representation as pharmacophoric features. Furthermore, it gives an overview of the various methods for pharmacophore generation and important pharmacophore-based techniques in drug design. This part concludes with examples for recent pharmacophore concept-related research and development. The last section is dedicated to a review of research in the field of natural product chemistry as carried out by employing pharmacophore-based drug design methods.
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References
Wermuth CG, Ganellin CR, Lindberg P, Mitscher LA (1998) Glossary of terms used in medicinal chemistry (IUPAC Recommendations 1998). J Macromol Sci A Pure Appl Chem 70:1129
Wermuth CG (2006) Pharmacophores: historical perspective and viewpoint from a medicinal chemist. In: Langer T, Hoffmann RD (eds) Methods and principles in medicinal chemistry, vol 32. Wiley-VCH, Weinheim, p 1
Langley JN (1878) On the physiology of the salivary secretion. J Physiol 1:339
Langley JN (1905) On the reaction of cells and of nerve-endings to certain poisons, chiefly as regards the reaction of striated muscle to nicotine and to curari. J Physiol 33:374
Ehrlich P, Morgenroth J (1900) Über Hämolysine. Dritte Mitheilung Berl Klin Wschr 37:453
Maehle A-H, Prüll C-R, Halliwell RF (2002) The emergence of the drug receptor theory. Nat Rev Drug Discov 1:637
Albert A (1985) Selective toxicity: the physico-chemical basis of therapy. Springer, Netherlands
Fischer E (1894) Einfluss der Configuration auf die Wirkung der Enzyme. Ber Dtsch Chem Ges 27:2985
Woods DD (1940) The relation of p-aminobenzoic acid to the mechanism of the action of sulphanilamide. Br J Exp Pathol 21:74
Woods DD, Fildes P (1940) The anti-sulphanilamide activity (in vitro) of p-aminobenzoic acid and related compounds. Chem Ind 59:133
Dodds EC, Lawson W (1938) Molecular structure in relation to oestrogenic activity. Compounds without a phenanthrene nucleus. Proc R Soc Lond B Biol Sci 125:222
Easson LH, Stedman E (1933) Studies on the relationship between chemical constitution and physiological action: molecular dissymmetry and physiological activity. Biochem J 27:1257
Gund P (2000) Evolution of the pharmacophore concept in pharmaceutical research. In: Güner OF (ed) Pharmacophore perception, development, and use in drug design. International University Line, La Jolla, CA, p 5
Matthews DA, Alden RA, Bolin JT, Freer ST, Hamlin R, Hol WGJ, Kisliuk RL, Pastore EJ, Plante LT, Xuong N Kraut J (1978) Dihydrofolate reductase from Lactobacillus casei. X-Ray structure of the enzyme methotrexate complex. J Biol Chem 253:6946
Wolfenden R (1976) Transition state analog inhibitors and enzyme catalysis. Annu Rev Biophys Bioeng 5:271
Gund P (1979) Pharmacophoric pattern searching and receptor mapping. In: Hess H-J (ed) Annual reports in medicinal chemistry, vol 14. Academic, New York, p 299
Humblet C, Marshall GR (1980) Pharmacophore identification and receptor mapping. In: Hess H-J (ed) Annual reports in medicinal chemistry, vol 15. Academic, New York, p 267
Marshall GR, Barry CD, Bosshard HE, Dammkoehler RA, Dunn DA (1979) In: Olson EC, Christoffersen RE (eds) The conformational parameter in drug design: The active analog approach, vol 112. American Chemical Society Books, Washington, DC, p 205
Greene J, Kahn S, Savoj H et al (1994) Chemical function queries for 3D database search. J Chem Inf Comput Sci 34:1297
Discovery Studio Predictive Science Application | Dassault Systèmes BIOVIA. https://www.3dsbiovia.com/products/collaborative-science/biovia-discovery-studio/. Accessed 7 Feb 2019
Phase | Schrödinger. https://www.schrodinger.com/phase. Accessed 7 Feb 2019
Molecular operating environment (MOE) | CCG Inc. https://www.chemcomp.com/MOE-Molecular_Operating_Environment.htm. Accessed 7 Feb 2019
LigandScout | InteLigand GmbH. http://www.inteligand.com/ligandscout/. Accessed 7 Feb 2019
Wolber G, Kosara R (2006) Pharmacophores from macromolecular complexes with LigandScout. In: Langer T, Hoffmann RD (eds) Methods and principles in medicinal chemistry, vol 32. Wiley-VCH, Weinheim, p 13
Wolber G, Langer T (2005) LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters. J Chem Inf Model 45:160
Wolber G, Seidel T, Bendix F, Langer T (2008) Molecule-pharmacophore superpositioning and pattern matching in computational drug design. Drug Discov Today 13:23
Krovat EM, Langer T (2003) Non-peptide angiotensin II receptor antagonists: chemical feature based pharmacophore identification. J Med Chem 46:716
Williams MA, Ladbury JE (2008) Hydrogen bonds in protein-ligand complexes. Protein Science Encyclopedia: online 137
Böhm H-J, Klebe G, Kubinyi H (1996) Protein-ligand-Wechselwirkungen. In: Böhm HJ, Klebe G, Kubinyi H (eds) Wirkstoffdesign. Spektrum Akademischer, Heidelberg, p 95
Ma JC, Dougherty DA (1997) The cation−π interaction. Chem Rev 97:1303
Waters ML (2002) Aromatic interactions in model systems. Curr Opin Chem Biol 6:736
Böhm H-J, Klebe G, Kubinyi H (1996) Metalloprotease-Hemmer. In: Böhm HJ, Klebe G, Kubinyi H (eds) Wirkstoffdesign. Spektrum. Akademischer Verlag, Heidelberg, p 505
Englert L, Silber K, Steuber H, Brass S, Over B, Gerber HD, Heine A, Diederich WE, Klebe G (2010) Fragment-based lead discovery: screening and optimizing fragments for thermolysin inhibition. ChemMedChem 5:930
Leach AR, Gillet VJ, Lewis RA, Taylor R (2010) Three-dimensional pharmacophore methods in drug discovery. J Med Chem 53:539
Goodford PJ (1985) A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. J Med Chem 28:849
Wade RC, Goodford PJ (1993) Further development of hydrogen bond functions for use in determining energetically favorable binding sites on molecules of known structure. 2. Ligand probe groups with the ability to form more than two hydrogen bonds. J Med Chem 36:148
Schuetz DA, Seidel T, Garon A, Martini R, Körbel M, Ecker GF, Langer T (2018) GRAIL: GRids of phArmacophore Interaction fieLds. J Chem Theory Comput 14:4958
Böhm H-J (1992) The computer program LUDI: A new method for the de novo design of enzyme inhibitors. J Comput Aided Mol Des 6:61
Verdonk ML, Cole JC, Taylor R (1999) SuperStar: a knowledge-based approach for identifying interaction sites in proteins. J Mol Biol 289:1093
Kirchhoff PD, Brown R, Kahn S, Waldman M, Venkatachalam CM (2001) Application of structure-based focusing to the estrogen receptor. J Comput Chem 22:993
Venkatachalam CM, Kirchhoff P, Waldman M (2000) Receptor-based pharmacophore perception and modeling. In: Güner OF (ed) Pharmacophore perception, development, and use in drug design. International University Line, La Jolla, CA, p 341
Dixon SL (2010) Pharmacophore methods. In: Merz KM Jr, Ringe D, Reynolds CH (eds) Drug design: structure- and ligand-based approaches. Cambridge University Press, Cambridge, p 137
Poptodorov K, Luu T, Hoffmann RD (2006) Pharmacophore model generation software tools. In: Langer T, Hoffmann RD (eds) Methods and principles in medicinal chemistry, vol 32. Wiley-VCH, Weinheim, p 17
Triballeau N, Bertrand H-O, Achner F (2006) Are you sure you have a good model? In: Langer T, Hoffmann RD (eds) Methods and principles in medicinal chemistry, vol 32. Wiley-VCH, Weinheim, p 325
Martin YC (1992) 3D database searching in drug design. J Med Chem 35:2145
Manallack DT (1996) Getting that hit: 3D database searching in drug discovery. Drug Discov Today 1:231
Clark DE, Westhead DR, Sykes RA, Murray CW (1996) Active-site-directed 3D database searching: pharmacophore extraction and validation of hits. J Comput Aided Mol Des 10:397
Good AC, Mason JS (1996) Three-dimensional structure database searches. Rev Comput Chem 67
Hurst T (1994) Flexible 3D searching: The directed tweak technique. J Chem Inf Comput Sci 34:190
Wolber G, Dornhofer AA, Langer T (2006) Efficient overlay of small organic molecules using 3D pharmacophores. J Comput Aided Mol Des 20:773
Laggner C, Wolber G, Kirchmair J, Schuster D, Langer T (2008) Pharmacophore-based virtual screening in drug discovery. In: Chemoinformatics approaches to virtual screening. The Royal Society of Chemistry, London, p 76
Sheridan RP, Kearsley SK (2002) Why do we need so many chemical similarity search methods? Drug Discov Today 7:903
Johnson MA, Maggiora GM (1990) Concepts and applications of molecular similarity. Wiley, New York
Leach AR (2001) Molecular modelling: principles and applications. Pearson Education, London
Zhu F, Agrafiotis DK (2007) Recursive distance partitioning algorithm for common pharmacophore identification. J Chem Inf Model 47:1619
Dixon SL, Smondyrev AM, Knoll EH, Rao SN, Shaw DE, Freisner 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 20:647
Wolber G, Seidel T, Bendix F (2010) 3D pharmacophore alignments: does improved geometric accuracy affect virtual screening performance? J Cheminform 2:O10
Evers A, Hessler G, Matter H, Klabunde T (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
Brint AT, Willett P (1987) Algorithms for the identification of three-dimensional maximal common substructures. J Chem Inf Comput Sci 27:152
Barnum D, Greene J, Smellie A, Sprague P (1996) Identification of common functional configurations among molecules. J Chem Inf Comput Sci 36:5631
Lemmen C, Lengauer T (2000) Computational methods for the structural alignment of molecules. J Comput Aided Mol Des 14:215
Seidel T, Bendix F, Wolber G (2010) Strategies for 3D pharmacophore-based virtual screening. Drug Discov Today Technol 7:e203–e270
Güner OF (2000) Pharmacophore perception. Development and use in drug design. International University Line, La Jolla, CA
Kirchmair J, Ristic S, Eder K, Markt P, Wolber G, Laggner C, Langer T (2007) Fast and efficient in silico 3D screening: toward maximum computational efficiency of pharmacophore-based and shape-based approaches. J Chem Inf Model 47:2182
Langer T, Wolber G (2004) Pharmacophore definition and 3D searches. Drug Discov Today Technol 1:203
Pepe MS (2003) The statistical evaluation of medical tests for classification and prediction. Oxford University Press, New York
Triballeau N, Acher F, Brabet I, Pin J-P, Betrand H-O (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
Schneider G, Fechner U (2005) Computer-based de novo design of drug-like molecules. Nat Rev Drug Discov 4:649
Gillet VJ, Johnson AP, Mata P, Sike S (1990) Automated structure design in 3D. Tetrahedron Comput Methodol 3:681
Nishibata Y, Itai A (1991) Automatic creation of drug candidate structures based on receptor structure. Starting point for artificial lead generation. Tetrahedron 47:8985
Tschinke V, Cohen NC (1993) The NEWLEAD program: a new method for the design of candidate structures from pharmacophoric hypotheses. J Med Chem 36:3863
Pearlman DA, Murcko MA (1993) CONCEPTS: new dynamic algorithm for de novo drug suggestion. J Comput Chem 14:1184
Gillet V, Johnson AP, Mata P, Sike S, Williams P (1993) SPROUT: a program for structure generation. J Comput Aided Mol Des 7:127
Eisen MB, Wiley DC, Karplus M, Hubbard RE (1994) HOOK: a program for finding novel molecular architectures that satisfy the chemical and steric requirements of a macromolecule binding site. Proteins 19:199
DeWitte RS, Shakhnovich EI (1996) SMoG: de novo design method based on simple, fast, and accurate free energy estimates. 1. Methodology and supporting evidence. J Am Chem Soc 118:11733
Pearlman DA, Murcko MA (1996) CONCERTS: dynamic connection of fragments as an approach to de novo ligand design. J Med Chem 39:1651
Douguet D, Thoreau E, Grassy G (2000) A genetic algorithm for the automated generation of small organic molecules: drug design using an evolutionary algorithm. J Comput Aided Mol Des 14:449
Wang R, Gao Y, Lai L (2000) LigBuilder: a multi-purpose program for structure-based drug design. Mol Mod Ann 6:498
Schneider G, Lee ML, Stahl M, Schneider P (2000) De novo design of molecular architectures by evolutionary assembly of drug-derived building blocks. J Comput Aided Mol Des 14:487
Zhu J, Fan H, Liu H, Shi Y (2001) Structure-based ligand design for flexible proteins: application of new F-DycoBlock. J Comput Aided Mol Des 15:979
Pegg SC, Haresco JJ, Kuntz ID (2001) A genetic algorithm for structure-based de novo design. J Comput Aided Mol Des 15:911
Vinkers HM, de Jonge MR, Daeyaert FFD, Daeyaert FF, Heeres J, Koymans LM, van Lenthe JH, Lewi PJ, Timmerman H, Van Aken K, Janssen PA (2003) SYNOPSIS: SYNthesize and OPtimize System in Silico. J Med Chem 46:2765
Brown N, McKay B, Gilardoni F, Gasteiger J (2004) A graph-based genetic algorithm and its application to the multiobjective evolution of median molecules. ChemInform 35:1079
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
Huang Q, Li L-L, Yang S-Y (2010) PhDD: a new pharmacophore-based de novo design method of drug-like molecules combined with assessment of synthetic accessibility. J Mol Graph Model 28:775
Mirjalili V, Noyes K, Feig M (2014) Physics-based protein structure refinement through multiple molecular dynamics trajectories and structure averaging. Proteins 82(Suppl 2):196
Whitesides GM, Krishnamurthy VM (2005) Designing ligands to bind proteins. Q Rev Biophys 38:385
Yang S-Y (2010) Pharmacophore modeling and applications in drug discovery: challenges and recent advances. Drug Discov Today 15:444
Wu F, Xu T, He G, Ouyang L, Peng C, Song Z, Xiang M (2012) Discovery of novel focal adhesion kinase inhibitors using a hybrid protocol of virtual screening approach based on multicomplex-based pharmacophore and molecular docking. Int J Mol Sci 13:15668
Zou J, Xie H-Z, Yang S-Y, Chen JJ, Ren JX, Wei YQ (2008) Towards more accurate pharmacophore modeling: multicomplex-based comprehensive pharmacophore map and most-frequent-feature pharmacophore model of CDK2. J Mol Graph Model 27:430
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
Sohn Y-S, Park C, Lee Y, Thangapandian S, Kim Y, Kim HH, Suh JK, Lee KW (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
Thangapandian S, John S, Arooj M, Lee KW (2012) Molecular dynamics simulation study and hybrid pharmacophore model development in human LTA4H inhibitor design. PLoS One 7:e34593
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
Spyrakis F, Benedetti P, Decherchi S, Rocchia W, Cavalli A, Alcaro S, Ortuso F, Baroni M, Cruciani G (2015) A pipeline to enhance ligand virtual screening: integrating molecular dynamics and fingerprints for ligand and proteins. J Chem Inf Model 55:2256
Sydow D (2015) Dynophores: novel dynamic pharmacophores. Humboldt-Universität zu Berlin, Lebenswissenschaftliche Fakultät
Sinko W, Lindert S, McCammon JA (2013) Accounting for receptor flexibility and enhanced sampling methods in computer-aided drug design. Chem Biol Drug Des 81:41
Plattner N, Noé F (2015) Protein conformational plasticity and complex ligand-binding kinetics explored by atomistic simulations and Markov models. Nat Commun 6:7653
Wieder M, Perricone U, Boresch S, Seidel T, Langer T (2016) Evaluating the stability of pharmacophore features using molecular dynamics simulations. Biochem Biophys Res Commun 470:685
Wieder M, Garon A, Perricone U, Boresch S, Seidel T, Almerico AM, Langer T (2017) Common hits approach: combining pharmacophore modeling and molecular dynamics simulations. J Chem Inf Model 57:365
Ortuso F, Langer T, Alcaro S (2006) GBPM: GRID-based pharmacophore model: concept and application studies to protein–protein recognition. Bioinformatics 22:1449
Mortier J, Dhakal P, Volkamer A (2018) Truly target-focused pharmacophore modeling: a novel tool for mapping intermolecular surfaces. Molecules 23:99E1959
Kastenholz MA, Pastor M, Cruciani G, Haaksma EEJ, T l F (2000) GRID/CPCA: a new computational tool to design selective ligands. J Med Chem 43:3033
Filer CN (2008) Book review of molecular design. Concepts and applications molecular design. Concepts and applications. By Schneider G, Baringhaus K-H. J Med Chem 51:7020
Schreiber SL (2000) Target-oriented and diversity-oriented organic synthesis in drug discovery. Science 287:1964
Grabowski K, Baringhaus K-H, Schneider G (2008) Scaffold diversity of natural products: inspiration for combinatorial library design. Nat Prod Rep 25:892
Lipinski CA (2004) Lead- and drug-like compounds: the rule-of-five revolution. Drug Discov Today Technol 1:337
Elumalai N, Berg A, Natarajan K, Scharow A, Berg T (2015) Nanomolar inhibitors of the transcription factor STAT5b with high selectivity over STAT5a. Angew Chem Int Ed Eng 54:4758
Rodrigues T, Reker D, Schneider P, Schneider G (2016) ChemInform abstract: counting on natural products for drug design. ChemInform 47. https://doi.org/10.1002/chin.201630259
Larsson J, Gottfries J, Muresan S, Backlund A (2007) ChemGPS-NP: tuned for navigation in biologically relevant chemical space. J Nat Prod 70:789
Schuster D, Maurer EM, Laggner C, Nashev LG, Wilckens T, Langer T, Odermatt A (2006) The discovery of new 11β-hydroxysteroid dehydrogenase type 1 inhibitors by common feature pharmacophore modeling and virtual screening. J Med Chem 49:3454
Rollinger JM, Kratschmar DV, Schuster D, Pfisterer PH, Gumy C, Aubry EM, Brandstötter S, Stuppner H, Wolber G, Odermatt A (2010) 11β-Hydroxysteroid dehydrogenase 1 inhibiting constituents from Eriobotrya japonica revealed by bioactivity-guided isolation and computational approaches. Bioorg Med Chem 18:1507
Abdallah BM, Beck-Nielsen H, Gaster M (2005) Increased expression of 11β-hydroxysteroid dehydrogenase type 1 in type 2 diabetic myotubes. Eur J Clin Investig 35:627
Kannisto K, Pietiläinen KH, Ehrenborg E, Rissanen A, Kaprio J, Hamsten A, H l Y-J (2004) Overexpression of 11β-hydroxysteroid dehydrogenase-1 in adipose tissue is associated with acquired obesity and features of insulin resistance: studies in young adult monozygotic twins. J Clin Endocrinol Metab 89:4414
Schuster D, Wolber G (2010) Identification of bioactive natural products by pharmacophore-based virtual screening. Curr Pharm Des 16:1666
Barf T, Vallgårda J, Emond R, Häggström C, Kurz G, Nygren A, Larwood V, Mosialou E, Axelsson K, Olsson R, Engblom L, Edling N, Rönquist-Nii Y, Ohman B, Alberts P, Abrahmsén L (2002) Arylsulfonamidothiazoles as a new class of potential antidiabetic drugs. Discovery of potent and selective inhibitors of the 11β-hydroxysteroid dehydrogenase type 1. J Med Chem 45:3813
Vicker N, Su X, Ganeshapillai D, Smith A, Purohitb A, Reed MJ, Potter BVL (2007) Novel non-steroidal inhibitors of human 11β-hydroxysteroid dehydrogenase type 1. J Steroid Biochem Mol Biol 104:123
Amico V, Barresi V, Condorelli D, Spatafora C, Tringali C (2006) Antiproliferative terpenoids from almond hulls (Prunus dulcis): identification and structure-activity relationships. J Agric Food Chem 54:810
He X, Liu RH (2007) Triterpenoids isolated from apple peels have potent antiproliferative activity and may be partially responsible for apple’s anticancer activity. J Agric Food Chem 55:4366
Gilar M (2001) Analysis and purification of synthetic oligonucleotides by reversed-phase high-performance liquid chromatography with photodiode array and mass spectrometry detection. Anal Biochem 298:196
Liang ZZ, Aquino R, de Feo V, De Simone F, Pizza C (1990) Polyhydroxylated triterpenes from Eriobotrya japonica. Planta Med 56:330
Gumy C, Thurnbichler C, Aubry EM, Balazs Z, Pfisterer P, Baumgartner L, Stuppner H, Odermatt A, Rollinger JM (2009) Inhibition of 11β-hydroxysteroid dehydrogenase type 1 by plant extracts used as traditional antidiabetic medicines. Fitoterapia 80:200
Hughes KA, Webster SP, Walker BR (2008) 11-β-Hydroxysteroid dehydrogenase type 1 (11β-HSD1) inhibitors in Type 2 diabetes mellitus and obesity. Exp Opin Invest Drugs 17:481
Herrmann F, Lenz M, Jose J, Kaiser M, Brun R, Schmidt TJ (2015) In silico identification and in vitro activity of novel natural inhibitors of Trypanosoma brucei glyceraldehyde-3-phosphate-dehydrogenase. Molecules 20:16154
World Health Organization (2015) Investing to overcome the global impact of neglected tropical diseases: third WHO report on neglected tropical diseases 2015. World Health Organization
Gualdrón-López M, Michels PAM, Quiñones W, Cáceres AJ, Avilán L, Concepción JL (2013) Function of glycosomes in the metabolism of trypanosomatid parasites and the promise of glycosomal proteins as drug targets. In: Jäger T, Koch O, Fiohé L (eds) Trypanosomatid diseases: molecular routes to drug discovery. Wiley-VCH, Weinheim, p 121
Cáceres AJ, Michels PAM, Hannaert V (2010) Genetic validation of aldolase and glyceraldehyde-3-phosphate dehydrogenase as drug targets in Trypanosoma brucei. Mol Biochem Parasitol 169:50
Schuster R, Holzhutter H-G (1995) Use of mathematical models for predicting the metabolic effect of large-scale enzyme activity alterations. Application to enzyme deficiencies of red blood cells. Eur J Biochem 229:403
Schmidt TJ, Khalid SA, Romanha AJ, Alves TM, Biavatti MW, Brun R, Da Costa FB, de Castro SL, Ferreira VF, de Lacerda MV, Lago JH, Leon LL, Lopes NP, das Neves Amorim RC, Niehues M, Ogungbe IV, Pohlit AM, Scotti MT, Setzer WN, de N C Soeiro M, Steindel M, Tempone AG (2012) The potential of secondary metabolites from plants as drugs or leads against protozoan neglected diseases – Part I. Curr Med Chem 19:2128
Schmidt TJ, Khalid SA, Romanha AJ, Alves TM, Biavatti MW, Brun R, Da Costa FB, de Castro SL, Ferreira VF, de Lacerda MV, Lago JH, Leon LL, Lopes NP, das Neves Amorim RC, Niehues M, Ogungbe IV, Pohlit AM, Scotti MT, Setzer WN, de N C Soeiro M, Steindel M, Tempone AG (2012) The potential of secondary metabolites from plants as drugs or leads against protozoan neglected diseases – Part II. Curr Med Chem 19:2176
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Seidel, T., Schuetz, D.A., Garon, A., Langer, T. (2019). The Pharmacophore Concept and Its Applications in Computer-Aided Drug Design. In: Kinghorn, A., Falk, H., Gibbons, S., Kobayashi, J., Asakawa, Y., Liu, JK. (eds) Progress in the Chemistry of Organic Natural Products 110. Progress in the Chemistry of Organic Natural Products, vol 110. Springer, Cham. https://doi.org/10.1007/978-3-030-14632-0_4
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