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Licensed Unlicensed Requires Authentication Published by De Gruyter September 4, 2019

Fundamental physical and chemical concepts behind “drug-likeness” and “natural product-likeness”

Mohd Athar ORCID logo, Alfred Ndeme Sona, Boris Davy Bekono and Fidele Ntie-Kang
From the journal Physical Sciences Reviews


The discovery of a drug is known to be quite cumbersome, both in terms of the microscopic fundamental research behind it and the industrial scale manufacturing process. A major concern in drug discovery is the acceleration of the process and cost reduction. The fact that clinical trials cannot be accelerated, therefore, emphasizes the need to accelerate the strategies for identifying lead compounds at an early stage. We, herein, focus on the definition of what would be regarded as a “drug-like” molecule and a “lead-like” one. In particular, “drug-likeness” is referred to as resemblance to existing drugs, whereas “lead-likeness” is characterized by the similarity with structural and physicochemical properties of a “lead”compound, i.e. a reference compound or a starting point for further drug development. It is now well known that a huge proportion of the drug discovery is inspired or derived from natural products (NPs), which have larger complexity as well as size when compared with synthetic compounds. Therefore, similar definitions of “drug-likeness” and “lead-likeness” cannot be applied for the NP-likeness. Rather, there is the dire need to define and explain NP-likeness in regard to chemical structure. An attempt has been made here to give an overview of the general concepts associated with NP discovery, and to provide the foundational basis for defining a molecule as a “drug”, a “lead” or a “natural compound.”


MA acknowledges the generous support from the Department of Science and Technology (DST), Government of India in the form of SRF-INSPIRE fellowship (IF150167) and Central University of Gujarat, India. FNK acknowledges a return fellowship and an equipment subsidy from the Alexander von Humboldt Foundation, Germany. BDB thanks the African-German Network of Excellence in Science (AGNES) for granting a Mobility Grant in 2017, generously sponsored by German Federal Ministry of Education and Research and the Alexander von Humboldt Foundation, Germany. Financial support for this work is acknowledged from a ChemJets fellowship from the Ministry of Education, Youth and Sports of the Czech Republic awarded to FNK.

List of abbreviations


Full meaning


absorption, distribution, metabolism, and excretion


absorption, distribution, metabolism, excretion and toxicity


beyond the Ro5


the calculated logarithm of the n-octanol/water partition coefficient


computer assisted structure elucidation


Dictionary of Natural Products


Fragment-based drug design


sp3-hybridized carbon atoms


number of hydrogen-bond acceptors


number of hydrogen-bond donors


molar refractivity


molecular weight


new chemical entities


natural product


number of rotatable bonds


the plane of Best Fit


principal component analysis


Principal Moment of Inertia


protein–protein interactions


polar surface area


quantitative estimate of drug-likeness


quantitative structure–activity relationships


“Rule of three”


“Rule of five”


Structural Classification of Natural Products


total polar surface area


[1] CSDD-Tufts Center for the Study of Drug Development. CNS drugs take 20 % longer to develop and to improve vs. non CNS drugs. Tufts CSDD impact reports, 2018. Available at: Assessed: 26 Dec 2018.Search in Google Scholar

[2] Lipinski CA. Lead-and drug-like compounds: the rule-of-five revolution. Drug Discov Today. 2004;1:337–41.10.1016/j.ddtec.2004.11.007Search in Google Scholar

[3] Athar M, Lone MY, Jha PC. First protein drug target’s appraisal of lead-likeness descriptors to unfold the intervening chemical space. J Mol Graph Model. 2017;72:272–82.10.1016/j.jmgm.2016.12.019Search in Google Scholar PubMed

[4] Ghose AK, Herbertz T, Hudkins RL, Dorsey BD, Mallamo JP. Knowledge-based, central nervous system (CNS) lead selection and lead optimization for CNS drug discovery. ACS Chem Neurosci. 2011;3:50–68.10.1021/cn200100hSearch in Google Scholar PubMed

[5] Veber DF, Johnson SR, Cheng H-Y, Smith BR, Ward KW, Kopple KD. Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem. 2002;45:2615–23.10.1021/jm020017nSearch in Google Scholar PubMed

[6] Congreve M, Carr R, Murray C, Jhoti H. A ‘Rule of Three’ for fragment-based lead discovery. Drug Discov Today. 2003;8:876–7.10.1016/S1359-6446(03)02831-9Search in Google Scholar PubMed

[7] Ghose AK, Viswanadhan VN, Wendoloski JJ. 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. 1999;1:55–68.10.1021/cc9800071Search in Google Scholar PubMed

[8] Santos GB, Ganesan A, Emery FS. Oral administration of peptide-based drugs: beyond Lipinski’s rule. ChemMedChem. 2016;11:2245–51.10.1002/cmdc.201600288Search in Google Scholar PubMed

[9] Bickerton GR, Paolini GV, Besnard J, Muresan S, Hopkins AL. Quantifying the chemical beauty of drugs. Nat Chem. 2012;4:90.10.1038/nchem.1243Search in Google Scholar PubMed PubMed Central

[10] Ntie-Kang F, Lifongo LL, Judson PN, Sippl W, Efange SM. How “drug-like” are naturally occurring anti-cancer compounds? J Mol Model. 2014;20:2069.10.1007/s00894-014-2069-zSearch in Google Scholar PubMed

[11] Ntie-Kang F. An in silico evaluation of the ADMET profile of the StreptomeDB database. SpringerPlus. 2013;2:353.10.1186/2193-1801-2-353Search in Google Scholar PubMed PubMed Central

[12] Ntie-Kang F, Zofou D, Babiaka SB, Meudom R, Scharfe M, Lifongo LL, et al. AfroDb: a select highly potent and diverse natural product library from African medicinal plants. PLoS One. 2013;8:e78085.10.1371/journal.pone.0078085Search in Google Scholar

[13] Newman DJ, Cragg GM. Natural products as sources of new drugs from 1981 to 2014. J Nat Prod. 2016;79:629–61. (add 3 refs each till the end).10.1021/acs.jnatprod.5b01055Search in Google Scholar PubMed

[14] Dobson PD, Kell DB. Carrier-mediated cellular uptake of pharmaceutical drugs: an exception or the rule? Nat Rev Drug Discov. 2008;7:205.10.1038/nrd2438Search in Google Scholar PubMed

[15] Bauer RA, Wurst JM, Tan DS. Expanding the range of ‘druggable’targets with natural product-based libraries: an academic perspective. Curr Opin Chem Biol. 2010;14:308–14.10.1016/j.cbpa.2010.02.001Search in Google Scholar PubMed

[16] Overington JP, Al-Lazikani B, Hopkins AL. How many drug targets are there? Nat Rev Drug Discov. 2006;5:993.10.1038/nrd2199Search in Google Scholar PubMed

[17] Hopkins AL, Groom CR. The druggable genome. Nat Rev Drug Discov. 2002;1:727.10.1038/nrd892Search in Google Scholar PubMed

[18] Wells JA, McClendon CL. Reaching for high-hanging fruit in drug discovery at protein–protein interfaces. Nature. 2007;450:1001.10.1038/nature06526Search in Google Scholar PubMed

[19] Hert J, Irwin JJ, Laggner C, Keiser MJ, Shoichet BK. Quantifying biogenic bias in screening libraries. Nat Chem Biol. 2009;5:479.10.1038/nchembio.180Search in Google Scholar PubMed

[20] Dobson CM. Chemical space and biology. Nature. 2004;432:824.10.1038/nature03192Search in Google Scholar PubMed

[21] Bohacek RS, McMartin C, Guida WC. The art and practice of structure‐based drug design: a molecular modeling perspective. Med Res Rev. 1996;16:3–50.10.1002/(SICI)1098-1128(199601)16:1<3::AID-MED1>3.0.CO;2-6Search in Google Scholar PubMed

[22] Newman DJ, Cragg GM. Natural products as sources of new drugs over the last 25 years. J Nat Prod. 2007;70:461–77.10.1021/np068054vSearch in Google Scholar PubMed

[23] Butler MS. Natural products to drugs: natural product-derived compounds in clinical trials. Nat Prod Rep. 2008;25:475–516.10.1039/b514294fSearch in Google Scholar PubMed

[24] Al H. Natural products in drug discovery. Drug Discov Today. 2008;13:894–901.10.1016/j.drudis.2008.07.004Search in Google Scholar PubMed

[25] Sukuru SC, Jenkins JL, Beckwith RE, Scheiber J, Bender A, Mikhailov D, et al. Plate-based diversity selection based on empirical HTS data to enhance the number of hits and their chemical diversity. J Biomolecul Screen. 2009;14:690–9.10.1177/1087057109335678Search in Google Scholar PubMed

[26] Grabowski K, Schneider G. Properties and architecture of drugs and natural products revisited. Curr Chem Biol. 2007;1:115–27.Search in Google Scholar

[27] Koch MA, Wittenberg L-O, Basu S, Jeyaraj DA, Gourzoulidou E, Reinecke K, et al. Compound library development guided by protein structure similarity clustering and natural product structure. Proc Nat Acad Sci USA. 2004;101:16721–6.10.1073/pnas.0404719101Search in Google Scholar PubMed PubMed Central

[28] Koch MA, Schuffenhauer A, Scheck M, Wetzel S, Casaulta M, Odermatt A, et al. Charting biologically relevant chemical space: a structural classification of natural products (SCONP). Proc Nat Acad Sci USA. 2005;102:17272–7.10.1073/pnas.0503647102Search in Google Scholar PubMed PubMed Central

[29] Wetzel S, Klein K, Renner S, Rauh D, Oprea TI, Mutzel P, et al. Interactive exploration of chemical space with Scaffold Hunter. Nat Chem Biol. 2009;5:581.10.1038/nchembio.187Search in Google Scholar PubMed

[30] Renner S, Van Otterlo WA, Seoane MD, Möcklinghoff S, Hofmann B, Wetzel S, et al. Bioactivity-guided mapping and navigation of chemical space. Nat Chem Biol. 2009;5:585.10.1038/nchembio.188Search in Google Scholar PubMed

[31] Hung AW, Ramek A, Wang Y, Kaya T, Wilson JA, Clemons PA, et al. Route to three-dimensional fragments using diversity-oriented synthesis. Proc Nat Acad Sci USA. 2011;108:6799–804.10.1073/pnas.1015271108Search in Google Scholar PubMed PubMed Central

[32] Firth NC, Brown N, Blagg J. Plane of best fit: a novel method to characterize the three-dimensionality of molecules. J Chem Inf Model. 2012;52:2516–25.10.1021/ci300293fSearch in Google Scholar PubMed PubMed Central

[33] Liao JJ. Molecular recognition of protein kinase binding pockets for design of potent and selective kinase inhibitors. J Med Chem. 2007;50:409–24.10.1021/jm0608107Search in Google Scholar PubMed

[34] Zinzalla G, Thurston DE. Targeting protein–protein interactions for therapeutic intervention: a challenge for the future. Future Med Chem. 2009;1:65–93.10.4155/fmc.09.12Search in Google Scholar PubMed

[35] Murray JK, Gellman SH. Targeting protein–protein interactions: lessons from p53/MDM2. Biopolymers. 2007;88:657–86.10.1002/bip.20741Search in Google Scholar PubMed

[36] Verdine GL, Walensky LD. The challenge of drugging undruggable targets in cancer: lessons learned from targeting BCL-2 family members. Clin Cancer Res. 2007;13:7264–70.10.1158/1078-0432.CCR-07-2184Search in Google Scholar PubMed

[37] Berg T. Small-molecule inhibitors of protein–protein interactions. In: Martin Zacharias, editor, Protein-protein complexes: analysis, modeling and drug design. London, UK: World Scientific, 2010:318–39.10.1142/9781848163409_0012Search in Google Scholar

[38] Wilson AJ. Inhibition of protein–protein interactions using designed molecules. Chem Soc Rev. 2009;38:3289–300.10.1039/b807197gSearch in Google Scholar PubMed

[39] Fry DC. Drug-like inhibitors of protein-protein interactions: a structural examination of effective protein mimicry. Curr Protein Pept Sci. 2008;9:240–7.10.2174/138920308784533989Search in Google Scholar PubMed

[40] Keskin O, Gursoy A, Ma B, Nussinov R. Principles of protein− protein interactions: What are the preferred ways for proteins to interact? Chem Rev. 2008;108:1225–44.10.1021/cr040409xSearch in Google Scholar PubMed

[41] Singh N, Guha R, Giulianotti MA, Pinilla C, Houghten RA, Medina-Franco JL. Chemoinformatic analysis of combinatorial libraries, drugs, natural products, and molecular libraries small molecule repository. J Chem Inf Model. 2009;49:1010–24.10.1021/ci800426uSearch in Google Scholar PubMed PubMed Central

[42] Feher M, Schmidt JM. Property distributions: differences between drugs, natural products, and molecules from combinatorial chemistry. J Chem Inf Comput Sci. 2003;43:218–27.10.1021/ci0200467Search in Google Scholar PubMed

[43] Shelat AA, Guy RK. Scaffold composition and biological relevance of screening libraries. Nat Chem Biol. 2007;3:442.10.1038/nchembio0807-442Search in Google Scholar PubMed

[44] Ertl P, Roggo S, Schuffenhauer A. Natural product-likeness score and its application for prioritization of compound libraries. J Chem Inf Model. 2008;48:68–74.10.1021/ci700286xSearch in Google Scholar PubMed

[45] Silva DG, Emery FS. Strategies towards expansion of chemical space of natural product-based compounds to enable drug discovery. Braz J Pharm Sci. 2018;54:e01004.10.1590/s2175-97902018000001004Search in Google Scholar

[46] Lovering F, Bikker J, Humblet C. Escape from flatland: increasing saturation as an approach to improving clinical success. J Med Chem. 2009;52:6752–6.10.1021/jm901241eSearch in Google Scholar PubMed

[47] Firn RD, Jones CG. Natural products–a simple model to explain chemical diversity. Nat Prod Rep. 2003;20:382–91.10.1039/b208815kSearch in Google Scholar PubMed

[48] Maplestone RA, Stone MJ, Williams DH. The evolutionary role of secondary metabolites—a review. Gene. 1992;115:151–7.10.1016/0378-1119(92)90553-2Search in Google Scholar PubMed

[49] Balamurugan R, Dekker FJ, Waldmann H. Design of compound libraries based on natural product scaffolds and protein structure similarity clustering (PSSC). Mol Biosyst. 2005;1:36–45.10.1039/b503623bSearch in Google Scholar PubMed

[50] Haustedt L, Mang C, Siems K, Schiewe H. Rational approaches to natural-product-based drug design. Curr Opin Drug Discov Devel. 2006;9:445–62.Search in Google Scholar PubMed

[51] Jayaseelan KV, Moreno P, Truszkowski A, Ertl P, Steinbeck C. Natural product-likeness score revisited: an open-source, open-data implementation. BMC Bioinformics. 2012;13:106.10.1186/1471-2105-13-106Search in Google Scholar PubMed PubMed Central

[52] Dobson PD, Patel Y, Kell DB. ‘Metabolite-likeness’ as a criterion in the design and selection of pharmaceutical drug libraries. Drug Discov Today. 2009;14:31–40.10.1016/j.drudis.2008.10.011Search in Google Scholar PubMed

[53] Baker M. Fragment-based lead discovery grows up. Nat Rev Drug Discov. 2013;12:5–7.10.1038/nrd3926Search in Google Scholar PubMed

[54] Chen H, Zhou X, Wang A, Zheng Y, Gao Y, Zhou J. Evolutions in fragment-based drug design: the deconstruction–reconstruction approach. Drug Discov Today. 2015;20:105–13.10.1016/j.drudis.2014.09.015Search in Google Scholar PubMed PubMed Central

[55] Joseph-McCarthy D, Campbell AJ, Kern G, Moustakas D. Fragment-based lead discovery and design. J Chem Inf Model. 2014;54:693–704.10.1021/ci400731wSearch in Google Scholar PubMed

[56] Murray CW, Rees DC. Opportunity knocks: organic chemistry for fragment‐based drug discovery (FBDD). Angew Chem Int Ed Engl. 2016;55:488–92.10.1002/anie.201506783Search in Google Scholar PubMed

[57] Scott DE, Coyne AG, Hudson SA, Abell C. Fragment-based approaches in drug discovery and chemical biology. Biochemistry. 2012;51:4990–5003.10.1021/bi3005126Search in Google Scholar PubMed

[58] Genis D, Kirpichenok M, Kombarov R. A minimalist fragment approach for the design of natural-product-like synthetic scaffolds. Drug Discov Today. 2012;17:1170–4.10.1016/j.drudis.2012.05.013Search in Google Scholar PubMed

[59] Austin MJ, Hearnshaw SJ, Mitchenall LA, McDermott PJ, Howell LA, Maxwell A, et al. A natural product inspired fragment-based approach towards the development of novel anti-bacterial agents. MedChemComm. 2016;7:1387–91.10.1039/C6MD00229CSearch in Google Scholar

[60] Pascolutti M, Campitelli M, Nguyen B, Pham N, Gorse A-D, Quinn RJ. Capturing nature’s diversity. PLoS One. 2015;10:e0120942.10.1371/journal.pone.0120942Search in Google Scholar PubMed PubMed Central

[61] Prescher H, Koch G, Schuhmann T, Ertl P, Bussenault A, Glick M, et al. Construction of a 3D-shaped, natural product like fragment library by fragmentation and diversification of natural products. Bioorg Med Chem. 2017;25:921–5.10.1016/j.bmc.2016.12.005Search in Google Scholar PubMed

[62] Rodrigues T, Reker D, Schneider P, Schneider G. Counting on natural products for drug design. Nat Chem. 2016;8:531.10.1038/nchem.2479Search in Google Scholar PubMed

[63] Over B, Wetzel S, Grütter C, Nakai Y, Renner S, Rauh D, et al. Natural-product-derived fragments for fragment-based ligand discovery. Nat Chem. 2013;5:21.10.1038/nchem.1506Search in Google Scholar PubMed

[64] Zaid H, Raiyn J, Nasser A, Saad B, Rayan A. Physicochemical properties of natural based products versus synthetic chemicals. Open Nutraceuticals J. 2010;3:194–202.10.2174/18763960010030100194Search in Google Scholar

[65] Wetzel S, Schuffenhauer A, Roggo S, Ertl P, Waldmann H. Cheminformatic analysis of natural products and their chemical space. CHIMIA Int J Chem. 2007;61:355–60.10.2533/chimia.2007.355Search in Google Scholar

[66] Lewell XQ, Judd DB, Watson SP, Hann MM. Recap retrosynthetic combinatorial analysis procedure: a powerful new technique for identifying privileged molecular fragments with useful applications in combinatorial chemistry. J Chem Inf Comput Sci. 1998;38:511–22.10.1021/ci970429iSearch in Google Scholar PubMed

[67] Flaherty KT, Yasothan U, Kirkpatrick P. Vemurafenib. Nat Rev Drug Discov. 2011;10:811–12.10.1038/nrd3579Search in Google Scholar PubMed

[68] Irwin JJ, Sterling T, Mysinger MM, Bolstad ES, Coleman RG. ZINC: a free tool to discover chemistry for biology. J Chem Inform Model. 2012;52:1757–68.10.1021/ci3001277Search in Google Scholar PubMed PubMed Central

[69] Hann MM, Oprea T. Pursuing the leadlikeness concept in pharmaceutical research. Curr Opin Chem Biol. 2004;8:255–63.10.1016/j.cbpa.2004.04.003Search in Google Scholar PubMed

[70] Oprea TI, Davis AM, Teague SJ, Leeson PD. Is there a difference between leads and drugs? a historical perspective. J Chem Inf Comput Sci. 2001;41:1308–15.10.1021/ci010366aSearch in Google Scholar PubMed

[71] Harvey AL, Edrada-Ebel R, Quinn RJ. The re-emergence of natural products for drug discovery in the genomics era. Nat Rev Drug Discov. 2015;14:111–29.10.1038/nrd4510Search in Google Scholar PubMed

[72] Quinn RJ, Carroll AR, Pham NB, Baron P, Palframan ME, Suraweera L, et al. Developing a drug-like natural product library. J Nat Prod. 2008;71:464–8.10.1021/np070526ySearch in Google Scholar PubMed

[73] McArdle BM, Campitelli MR, Quinn RJ. A common protein fold topology shared by flavonoid biosynthetic enzymes and therapeutic targets. J Nat Prod. 2006;69:14–7.10.1021/np050229ySearch in Google Scholar PubMed

[74] Kellenberger E, Hofmann A, Quinn RJ. Similar interactions of natural products with biosynthetic enzymes and therapeutic targets could explain why nature produces such a large proportion of existing drugs. Nat Prod Rep. 2011;8:1483–92.10.1039/c1np00026hSearch in Google Scholar

[75] Saldívar-González FI, Pilón-Jiménez BA, Medina-Franco JL. Chemical space of naturally occurring compounds. Phys Sci Rev. 2018. doi:10.1515/psr-2018-0103.Search in Google Scholar

[76] Benet LZ, Hosey CM, Ursu O, Oprea TI. BDDCS, the rule of 5 and drugability. Adv Drug Deliv Rev. 2016;101:89–98.10.1016/j.addr.2016.05.007Search in Google Scholar PubMed

[77] Zhang MQ, Wilkinson B. Drug discovery beyond the ‘rule-of-five’. Curr Opin Biotechnol. 2007;18:478–88.10.1016/j.copbio.2007.10.005Search in Google Scholar PubMed

[78] Newman DJ. From natural products to drugs. Phys Sci Rev. 2018. doi:10.1515/psr-2018-0111.Search in Google Scholar

[79] Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Delivery Rev. 1997;23:3–25.10.1016/S0169-409X(96)00423-1Search in Google Scholar

[80] Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev. 2001;46:3–26.10.1016/S0169-409X(00)00129-0Search in Google Scholar PubMed

Published Online: 2019-09-04

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