Abstract
The drug discovery process typically involves target identification and design of suitable drug molecules against these targets. Despite decades of experimental investigations in the drug discovery domain, about 96% overall failure rate has been recorded in drug development due to the “undruggability” of various identified disease targets, in addition to other challenges. Likewise, the high attrition rate of drug candidates in the drug discovery process has also become an enormous challenge for the pharmaceutical industry. To alleviate this negative outlook, new trends in drug discovery have emerged. By drifting away from experimental research methods, computational tools and big data are becoming valuable in the prediction of biological target druggability and the drug-likeness of potential therapeutic agents. These tools have proven to be useful in saving time and reducing research costs. As with any emerging technique, however, controversial opinions have been presented regarding the validation of predictive computational tools. To address the challenges associated with these varying opinions, this review attempts to highlight the principles of druggability and drug-likeness and their recent advancements in the drug discovery field. Herein, we present the different computational tools and their reliability of predictive analysis in the drug discovery domain. We believe that this report would serve as a comprehensive guide towards computational-oriented drug discovery research.
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References
Dang CV, Reddy EP, Shokat KM, Soucek L (2017) Drugging the “undruggable” cancer targets. Nat Rev Cancer 17(8):502–508
Galdeano C (2017) Drugging the undruggable: targeting challenging E3 ligases for personalized medicine. Future Med Chem 9(4):347–350
Zhang ZY (2017) Drugging the undruggable: therapeutic potential of targeting protein tyrosine phosphatases. Acc Chem Res 50(1):122–129
Machado D, Girardini M, Viveiros M, Pieroni M (2018) Challenging the drug-likeness dogma for new drug discovery in tuberculosis. Front Microbiol 9:1367
Sakharkar M, Sakharkar K (2007) Targetability of human disease genes. Curr Drug Discov Technol 4:48–58
Taboureau O, Nielsen S, Audouze K (2011) ChemProt: a disease chemical biology database. Nucleic Acids Res 39:D367–D372
Dixon S, Stockwell B (2009) Identifying druggable disease-modifying gene products. Curr Opin Chem Biol 13:549–555
Scannell J, Blanckley A, Boldon H et al (2012) Diagnosing the decline in pharmaceutical R&D efficiency. Nat Rev Drug Discov 11:191–200
Hingorani A, Kuan V, Finan C et al (2019) Improving the odds of drug development success through human genomics: modelling study. Sci Rep 9:18911
Hay M, Thomas D, Craighead J, Economides C, Rosenthal J (2014) Clinical development success rates for investigational drugs. Nat Biotechnol 32:40–51
Schmidtke P, Barril X (2010) Understanding and predicting druggability. A high-throughput method for detection of drug binding sites. J Med Chem 53(15):5858–5867
Vistoli G, Pedretti A, Testa B (2008) Assessing drug-likeness - what are we missing? Drug Discov Today 13(7–8):285–294
Kozakov D, Hall DR, Napoleon RL, Yueh C, Whitty A, Vajda S (2015) New frontiers in druggability. J Med Chem 58(23):9063–9088
Oprea TI, Bologa CG, Brunak S, Campbell A, Gan GN, Gaulton A et al (2018) Unexplored therapeutic opportunities in the human genome. Nat Rev Drug Discov 7:317–332
Finan C, Gaulton A, Kruger FA, Lumbers RT, Shah T, Engmann J et al (2017) The druggable genome and support for target identification and validation in drug development. Sci Transl Med 9(383):eaag1166
Hopkins AL, Groom CR (2002) The druggable genome. Nat Rev Drug Discov 1(9):727–730
Cheng AC, Coleman RG, Smyth KT, Cao Q, Soulard P, Caffrey DR et al (2007) Structure-based maximal affinity model predicts small-molecule druggability. Nat Biotechnol 25(1):71–75
Egner U, Hillig RC (2008) A structural biology view of target druggability. Expert Opin Drug Discovery 3(4):391–401
Sheridan RP, Maiorov VN, Holloway MK, Cornell WD, Gao YD (2010) Drug-like density: a method of quantifying the “bindability” of a protein target based on a very large set of pockets and drug-like ligands from the protein data bank. J Chem Inf Model 50(11):2029–2040
Rathi PC, Ludlow RF, Hall RJ, Murray CW, Mortenson PN, Verdonk ML (2017) Predicting “hot” and “warm” spots for fragment binding. J Med Chem 60(9):4036–4046
Hajduk PJ, Huth JR, Fesik SW (2005) Druggability indices for protein targets derived from NMr-based screening data. J Med Chem 48(7):2518–2525
Seco J, Luque FJ, Barril X (2009) Binding site detection and druggability index from first principles. J Med Chem 52(8):2363–2371
Halgren TA (2009) Identifying and characterizing binding sites and assessing druggability. J Chem Inf Model 49(2):377–389
Hajduk PJ, Huth JR, Tse C (2005) Predicting protein druggability. Drug Discov Today 10(23–24):1675–1682
Krasowski A, Muthas D, Sarkar A, Schmitt S, Brenk R (2011) DrugPred: a structure-based approach to predict protein druggability developed using an extensive nonredundant data set. J Chem Inf Model 51(11):2829–2842
Volkamer A, Kuhn D, Rippmann F, Rarey M (2012) Dogsitescorer: a web server for automatic binding site prediction, analysis and druggability assessment. Bioinformatics. 28(15):2074–2075
Perola E, Herman L, Weiss J (2012) Development of a rule-based method for the assessment of protein druggability. J Chem Inf Model 2(4):1027–1038
Walters W, Ajay A, Murcko M (1999) Recognizing molecules with drug-like properties. Curr Opin Chem Biol 3(4):384–387
Walters W, Stahl M, Murcko M (1998) Virtual screening – an overview. Drug Discov Today 3:160–178
Bickerton GR, Paolini GV, Besnard J, Muresan S, Hopkins AL (2012) Quantifying the chemical beauty of drugs. Nat Chem 4:90–98
Oprea TI (2000) Property distribution of drug-related chemical databases. J Comput Aided Mol Des 14(3):251–264
Leeson PD, Springthorpe B (2007) The influence of drug-like concepts on decision-making in medicinal chemistry. Nat Rev Drug Discov 6(11):881–890
Katsila T, Spyroulias GA, Patrinos GP, Matsoukas MT (2016) Computational approaches in target identification and drug discovery. Comput Struct Biotechnol J 14(1):177–184
Barril X (2013) Druggability predictions: methods, limitations, and applications. Wiley Interdiscip Rev Comput Mol Sci 3(4):327–338
Cheng T, Hao M, Takeda T, Bryant SH, Wang Y (2017) Large-scale prediction of drug-target interaction: a data-centric review. AAPS J 19(5):1264–1275
Neal KB, Mahmoud ES (2017) Can we rely on computational predictions to correctly identify ligand binding sites on novel protein drug targets? Assessment of binding site prediction methods and a protocol for validation of predicted binding sites. Cell Biochem Biophys 75(1):15–23
Sun T, Lai L, Pei J (2018) Analysis of protein features and machine learning algorithms for prediction of druggable proteins. Quant Biol 6(4):334–343
Li YH, Yu CY, Li XX, Zhang P, Tang J, Yang Q et al (2018) Therapeutic target database update 2018: enriched resource for facilitating bench-to-clinic research of targeted therapeutics. Nucleic Acids Res 46(D1):D1121–D1127
Jiang Q, Wang J, Wu X, Ma R, Zhang T, Jin S et al (2015) LncRNA2Target: a database for differentially expressed genes after IncRNA knockdown or overexpression. Nucleic Acids Res 43(D1):D193–D196
Griffiths-Jones S (2010) MiRBase: MicroRNA sequences and annotation. Curr Protoc Bioinformatics 34(29):1291–12910
Kandoi G, Acencio ML, Lemke N (2015) Prediction of druggable proteins using machine learning and systems biology: a mini-review. Front Physiol 6(9):54–65
Wyatt PG, Gilbert IH, Read KD, Fairlamb AH (2011) Target validation: linking target and chemical properties to desired product profile. Curr Top Med Chem 11(10):1275–1283
Fauman E, Rai B, Huang E (2011) Structure-based druggability assessment—identifying suitable targets for small molecule therapeutics. Curr Opin Chem Biol 15(4):463–468
Huang B (2009) MetaPocket: a meta approach to improve protein ligand binding site prediction. OMICS 13(4):325–330 [Internet]. Available from: http://www.liebertonline.com/doi/abs/10.1089/omi.2009.0045
Hussein HA, Borrel A, Geneix C, Petitjean M, Regad L, Camproux AC (2015) PockDrug-server: a new web server for predicting pocket druggability on holo and apo proteins. Nucleic Acids Res 43(W1):W436–W442
Hernandez M, Ghersi D, Sanchez R (2009) SITEHOUND-web: a server for ligand binding site identification in protein structures. Nucleic Acids Res 37:413–416
Koscielny G, An P, Carvalho-Silva D, Cham JA, Fumis L, Gasparyan R et al (2017) Open targets: a platform for therapeutic target identification and validation. Nucleic Acids Res 45(D1):D985–D994
Liu T, Lin Y, Wen X, Jorissen RN, Gilson MK (2007) BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities. Nucleic Acids Res 35(D4):198–201
Kim S, Chen J, Cheng T, Gindulyte A, He J, He S et al (2019) PubChem 2019 update: improved access to chemical data. Nucleic Acids Res 47(D1):D1102–D1109
Gfeller D, Grosdidier A, Wirth M, Daina A, Michielin O, Zoete V (2014) SwissTargetPrediction: a web server for target prediction of bioactive small molecules. Nucleic Acids Res 42(W1):32–38
Wang L, Ma C, Wipf P, Liu H, Su W, Xie X-Q (2013) TargetHunter: An in silico target identification tool for predicting therapeutic potential of small organic molecules based on Chemogenomic database. AAPS J 15(2):395–406
Harding SD, Sharman JL, Faccenda E, Southan C, Pawson AJ, Ireland S et al (2017) The IUPHAR/BPS guide to pharmacology In 2018: updates and expansion to encompass the new guide to immunopharmacology. Nucleic Acids Res 46(D1):D1091–D1106
Bethesda (MD) (1988) National Library of Medicine (US). National Center for Biotechnology Information (NCBI). Accessed [11/03/2018]
Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H et al (2000) The Protein Data Bank 28(1):235–42
Loving KA, Lin A, Cheng AC (2014) Structure-based druggability assessment of the mammalian structural proteome with inclusion of light protein flexibility. PLoS Comput Biol 10(7):321–329
Edfeldt F, Folmer R, Breeze A (2011) Fragment screening to predict druggability (ligandability) and lead discovery success. Drug Discov Today 16(7–8):284–287
Ursu O, Holmes J, Bologa CG, Yang JJ, Mathias SL, Stathias V et al (2019) DrugCentral 2018: An update. Nucleic Acids Res 47(D1):D963–D970
Tse T, Fain KM, Zarin DA (2018) How to avoid common problems when using CliicalTrials.gov in research. Bmj. 361(4):1452–1459
Wishart DS, Feunang YD, Guo AC, Lo EJ, Marcu A, Grant JR et al (2018) DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res 46(D1):D1074–D1082
Laskowski R (1995) SURFNET: a program for visualizing molecular surfaces, cavities, and intermolecular interactions. J Mol Graph 13:323–330
Brady G, Stouten P (2000) Fast prediction and visualization of protein binding pockets with PASS. J Comput Aided Mol Des 14:383–401
Huang B, Schroeder M (2006) LIGSITEcsc: predicting ligand binding sites using the connolly surface and degree of conservation. BMC Struct Biol 6:19
Laurie A, Jackson R (2005) Q-SiteFinder: an energy-based method for the prediction of protein-ligand binding sites. Bioinformatics. 21(9):1908–1916
Leis S, Schneider S, Zacharias M (2010) In Silico prediction of binding sites on proteins. Curr Med Chem 17(15):1550–1562
Kerns E, Di L (2008) Drug-like properties: concepts, structure design and methods, vol 552. Academic Press, Burlington
Schneider G Prediction of Drug-Like Properties. Madame Curie Bioscience Database. Landes Bioscience, Austin
Lipinski C, Dominy B, Feeney P (1997) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 23:3–25
Sadowski J, Kubinyi H (1998) A scoring scheme for discriminating between drugs and nondrugs. J Med Chem 41:3325–3329
Byvatov E, Fechner U, Sadowski J, Schneider G (2003) Comparison of support vector machine and artificial neural network systems for drug/nondrug classification. J Chem Inf Comput Sci 43:1882–1889
Takaoka Y, Endo Y, Yamanobe S, Kakinuma H, Okubo T, Shimazaki Y et al (2003) Development of a method for evaluating drug-likeness and ease of synthesis using a data set in which compounds are assigned scores based on chemists’ intuition. J Chem Inf Comput Sci 43:1269–1275
Ajay A, Walters W, Murcko M (1998) Can we learn to distinguish between “drug-like” and “nondrug-like” molecules? J Med Chem 41:3314–3324
Wagener M, van Geerestein V (2000) Potential drugs and nondrugs: prediction and identification of important structural features. J Chem Inf Comput Sci 40:280–292
Schneider N, Jaeckels C, Andres C, Hutter M (2008) Gradual in silico filtering for druglike substances. J Chem Inf Model 48:613–628
Zernov V, Balakin K, Ivaschenko A, Savchuk N, Pletnev I (2003) Drug discovery using support vector machines. The case studies of drug-likeness, agrochemical-likeness, and enzyme inhibition predictions. J Chem Inf Model 43:2048–2056
Gillet V, Khatib W, Willett P, Fleming P, Green D (2002) Combinatorial library design using a multiobjective genetic algorithm. J Chem Inf Comput Sci 42:375–385
Gillet V, Willett P, Bradshaw J (1998) Identification of biological activity profiles using substructural analysis and genetic algorithms. J Chem Inf Comput Sci 38:165–179
Feher M, Schmidt J (2003) Property distribution: differences between drugs, natural products, and molecules from combinatorial chemistry. J Chem Inf Comput Sci 43:218–227
Rowland M, Peck C, Tucker G (2011) Physiologically-based pharmacokinetics in drug development and regulatory science. Annu Rev Pharmacol Toxicol 51:45–73
Gabrielsson J, Green A (2009) Quantitative pharmacology or pharmacokinetic pharmacodynamic integration should be a vital component in integrative pharmacology. J Pharmacol Exp Ther 331:767–774
Wager T, Hou X, Villalobos A (2010) Moving beyond rules: the development of a central nervous system multiparameter optimization (CNS MPO) approach to enable alignment of druglike properties. ACS Chem Neurosci 1:435–449
Li D, Kerns E, Carter G (2009) Drug-like property concepts in pharmaceutical design. Curr Pharm Des 15:2184–2194
Veber D, Johnson S, Cheng H, Smith B, Ward K, Kopple K (2002) Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem 45:2615–2623
Waring M (2009) Defining optimum lipophilicity and molecular weight ranges for drug candidates—molecular weight dependent lower logD limits based on permeability. Bioorg Med Chem Lett 19:2844–2851
Johnson T, Dress K, Edwards M (2009) Using the golden triangle to optimize clearance and oral absorption. Bioorg Med Chem Lett 19:5560–5564
Lipinski C (2000) Drug-like properties and the causes of poor solubility and poor permeability. J Pharmacol Toxicol Methods 44(1):235–249
Lipinski CA (2016) Rule of five in 2015 and beyond: target and ligand structural limitations, ligand chemistry structure and drug discovery project decisions. Adv Drug Deliv Rev 101:34–41
Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (2012) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development setting. Adv Drug Deliv Rev 64:4–17
Kadam R, Roy N (2007) Recent trends in drug-likeness prediction: a comprehensive review of in silico methods. Indian J Pharm Sci 69(5):609–615
Bhal S, Kassam K, Peirson I, Pearl G (2007) The rule of five revisited: applying log D in place of log P in drug-likeness filters. Mol Pharm 4:556–560
Murphy RB, Philipp DM, Friesner RA (2000) A mixed quantum mechanics/molecular mechanics (QM/MM) method for large-scale modeling of chemistry in protein environments. J Comput Chem 21(16):1442–1457
Clark D, Pickett S (2000) Computational methods for the prediction of “drug-likeness.”. Drug Discov Today 5(2):49–58
Lewis R, Mason J, McLay I (1997) Similarity measures for rational set selection and analysis of combinatorial libraries: the diverse property-derived (DPD) approach. J Chem Inf Comput Sci 37:599–614
Rishton G (1997) Reactive compounds and in vitro false positives in HTS. Drug Discov Today 2:382–384
Bioinformatics SI of Click2Drug (2013) p 1–10
Polinsky A (1999) Combichem and chemoinformatics. Curr Opin Drug Discov Devel 2:197–203
Acknowledgments
The authors appreciate the College of Health Sciences, University of KwaZulu-Natal for financial and infrastructural support, while we also thank the Center for High-Performance Computing (CHPC, www.chpc.ac.za) Cape-Town, South Africa, for providing computational resources.
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Agoni, C., Olotu, F.A., Ramharack, P. et al. Druggability and drug-likeness concepts in drug design: are biomodelling and predictive tools having their say?. J Mol Model 26, 120 (2020). https://doi.org/10.1007/s00894-020-04385-6
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DOI: https://doi.org/10.1007/s00894-020-04385-6