Molecular Docking: Challenges, Advances and its Use in Drug Discovery Perspective

Author(s): Surovi Saikia, Manobjyoti Bordoloi*.

Journal Name: Current Drug Targets

Volume 20 , Issue 5 , 2019

  Journal Home
Translate in Chinese
Become EABM
Become Reviewer

Graphical Abstract:


Abstract:

Molecular docking is a process through which small molecules are docked into the macromolecular structures for scoring its complementary values at the binding sites. It is a vibrant research area with dynamic utility in structure-based drug-designing, lead optimization, biochemical pathway and for drug designing being the most attractive tools. Two pillars for a successful docking experiment are correct pose and affinity prediction. Each program has its own advantages and drawbacks with respect to their docking accuracy, ranking accuracy and time consumption so a general conclusion cannot be drawn. Moreover, users don’t always consider sufficient diversity in their test sets which results in certain programs to outperform others. In this review, the prime focus has been laid on the challenges of docking and troubleshooters in existing programs, underlying algorithmic background of docking, preferences regarding the use of docking programs for best results illustrated with examples, comparison of performance for existing tools and algorithms, state of art in docking, recent trends of diseases and current drug industries, evidence from clinical trials and post-marketing surveillance are discussed. These aspects of the molecular drug designing paradigm are quite controversial and challenging and this review would be an asset to the bioinformatics and drug designing communities.

Keywords: Molecular docking, algorithms, scoring functions, molecular dynamics, pharmacophore, drug designing.

[1]
Turner PR, Denny WA. The Genome as a Drug Target: Sequence specific minor groove binding ligands. Curr Drug Targets 2000; 1: 1-14.
[2]
Jorgensen WL. The many roles of computation in drug discovery. Science 2004; 303(5665): 1813-8.
[3]
Berman HM. The protein data bank. Nucleic Acids Res 2000; 28: 235-42.
[4]
Hughes JP, Rees S, Kalindjian SB, Philpott KL. Principles of early drug discovery. Br J Pharmacol 2011; 162: 1239-49.
[5]
Boehm HJ, Boehringer M, Bur D, et al. Novel inhibitors of DNA gyrase: 3D structure based biased needle screening, hit validation by biophysical methods, and 3D guided optimization. A promising alternative to random screening. J Med Chem 2000; 43(14): 2664-74.
[6]
Shih-Jen L, Fok-Ching C. Combining molecular docking and molecular dynamics to predict the binding modes of flavonoid derivatives with the neuraminidase of the 2009 h1n1 influenza a virus. Int J Mol Sci 2012; 13: 4496-507.
[7]
Kumar RG, Sahu S, Sonkar KS, Debnath M, Kumar A. Modeling and Molecular docking studies on RNAseaspergillusniger and leishmaniadonovani actin: antileishmanial activity. Am J Biochem Biotechnol 2013; 9(3): 318-28.
[8]
López-Vallejo F, Caulfield T, Martínez-Mayorga K, et al. Integrating virtual screening and combinatorial chemistry for accelerated drug discovery. Comb Chem High Throughput Screen 2011; 14: 475-87.
[9]
Coleman RG, Carchia M, Sterling T, Irwin JJ, Shoichet BK. Ligand pose and orientational sampling in molecular docking. PLoS One 2013; 8(10): e75992.
[10]
Wang R, Lu Y, Fang X, Wang S. An extensive test of 14 scoring functions using the pdbbind refined set of 800 protein-ligand complexes. J Chem Inf Comput Sci 2004; 44: 2114-25.
[11]
Kitchen DB, Decornez H, Furr JR, Bajorath J. Docking and scoring in virtual screening for drug discovery: Methods and applications. Nat Rev Drug Discov 2004; 3: 935-49.
[12]
Dastmalchi S, Hamzeh-Mivehroud M, Sokouti B. Methods and algorithms for molecular docking-based drug design and discovery. Hershey, PA: IGI Global 2016; pp. 1-456.
[13]
De Vivo M, Cavalli A. Recent advances in dynamic docking for drug discoveryWIREs Comput Mol Sci 2017, e1320
[14]
Qing X, Lee XY, De Raeymaecker J, et al. Pharmacophore modeling: advances, limitations, and current utility in drug discovery. J Receptor Ligand Channel 2014; 7: 81-92.
[15]
Zhang Q, Feng T, Xu L, et al. Recent advances in protein-protein docking. Curr Drug Targets 2016; 17(14): 1586-94.
[16]
Krüger J, Thiel P, Merelli I, Grunzke R, Gesing S. Portals and web-based resources for virtual screening. Curr Drug Targets 2016; 17(14): 1649-60.
[17]
de Azevedo WF. Targeting multiple cyclin-dependent kinases (cdks): a new strategy for molecular docking studies. Curr Drug Targets 2016; 17(1): 2.
[18]
Chiappori F, Milanesi L, Merelli I. HPC. Analysis of multiple binding sites communication and allosteric modulations in drug design: The HSP Case Study. Curr Drug Targets 2016; 17(14): 1610-25.
[19]
Abdolmaleki A, Ghasemi JB, Ghasemi F. Computer aided drug design for multi-target drug design: SAR /QSAR, molecular docking and pharmacophore methods. Curr Drug Targets 2017; 18(5): 556-75.
[20]
Scotti L, Mendonca FJ Junior, Ishiki HM, et al. Docking studies for multi-target drugs. Curr Drug Targets 2017; 18(5): 592-604.
[21]
Cardamone F, Pizzi S, Iacovelli F, Falconi M, Desideri A. Virtual screening for the development of dual-inhibitors targeting topoisomerase ib and tyrosyl-dna phosphodiesterase 1. Curr Drug Targets 2017; 18(5): 544-55.
[22]
Ganai SA. Designing isoform-selective inhibitors against Classical HDACs for effective anticancer therapy: Insight and perspectives from in silico. Curr Drug Targets 2018; 19(7): 815-24.
[23]
Maggio ET, Ramnarayan K. Recent developments in computational proteomics. Trends Biotechnol 2001; 19: 266-72.
[24]
Abagyan R, Totrov M. High-throughput docking for lead generation. Curr Opin Chem Biol 2001; 5: 375-82.
[25]
Branden C, Tooze J. 1991 Introduction to Protein StructureGarland Publishing New York, London.
[26]
Koshland D. Application of a theory of enzyme specificity to protein synthesis. Proc Natl Acad Sci USA 1958; 44: 98.
[27]
Monod J, Wyman J, Changeux JP. On the nature of allosteric transitions: a plausible model. J Mol Biol 1965; 12: 88-118.
[28]
Pennec X, Ayache N. A geometric algorithm to find small but highly similar 3D substructures in proteins. Bioinformatics 1998; 14(6): 516-22.
[29]
Teague SJ. Implications of protein flexibility for drug discovery. Nat Rev Drug Discov 2003; 2(7): 527-39.
[30]
Yuriev E, Agostino M, Ramsland PA. Challenges and advances in computational docking: 2009 in review. J Mol Recognit 2011; 24: 149-64.
[31]
Guedes IA, de Magalhães CS, Dardenne LE. Receptor–ligand molecular docking. Biophys Rev 2014; 6: 75.
[32]
Buonfiglio R, Recanatini M, Masetti M. Protein flexibility in drug discovery: From theory to computation. ChemMedChem 2015; 10: 1141-8.
[33]
Lill MA. Efficient incorporation of protein flexibility and dynamics into molecular docking simulations. Biochem 2011; 50: 6157-69.
[34]
Ferrara P, Gohlke H, Price DJ, Klebe G, Brooks CL III. Assessing scoring functions for protein ligand interactions. J Med Chem 2004; 47: 3032-47.
[35]
Feixas F, Lindert S, Sinko W, McCammon JA. Exploring the role of receptor flexibility in structure-based drug discovery. Biophys Chem 2014; 186: 31-45.
[36]
Petrone P, Pande VS. Can conformational change be described by only a few normal modes? Biophys J 2006; 90: 1583-93.
[37]
Cavasotto CN, Kovacs JA, Abagyan RA. Representing receptor flexibility in ligand docking through relevant normal modes. J Am Chem Soc 2005; 127: 9632-40.
[38]
Cukier RI. Apo adenylate kinase encodes its holo form: a principal component and varimax analysis. J Phys Chem B 2009; 113: 1662-72.
[39]
Ferrari AM, Wei BQ, Costantino L, Shoichet BK. Soft docking and multiple receptor conformations in virtual screening. J Med Chem 2004; 47: 5076-84.
[40]
B-Rao C. Subramanian J, Sharma SD. Managing protein flexibility in docking and its applications. Drug Discov Today 2009; 14: 394-400.
[41]
Beier C, Zacharias M. Tackling the challenges posed by target flexibility in drug design. Expert Opin Drug Discov 2010; 5: 347-59.
[42]
Feixas F, Lindert S, Sinko W, McCammon JA. Exploring the role of receptor flexibility in structure-based drug discovery. Biophys Chem 2014; 186: 31-45.
[43]
Davis IW, Baker D. RosettaLigand docking with full ligand and receptor flexibility. J Mol Biol 2009; 385: 381-92.
[44]
Kuntz ID, Blaney JM, Oatley SJ, Langridge R, Ferrin TE. A geometric approach to macromolecule-ligand interactions. J Mol Biol 1982; 161(2): 269-88.
[45]
Kuntz ID, Leach AR. Conformational analysis of flexible ligands in macromolecular receptor sites. J Comput Chem 1992; 13: 730-48.
[46]
Ewing TJ, Makino S, Skillman AG, Kuntz ID. DOCK 4.0: search strategies for automated molecular docking of flexible molecule databases. J Comput Aided Mol Des 2001; 15(5): 411-28.
[47]
Shoichet BK, Stroud RM, Santi DV, Kuntz ID, Perry KM. Structure-based discovery of inhibitors of thymidylate synthase. Science 1993; 259(5100): 1445-50.
[48]
Gabb HA, Jackson RM, Sternberg MJ. Modelling protein docking using shape complementarity, electrostatics and biochemical information. J Mol Biol 1997; 272(1): 106-20.
[49]
Sherman W, Day T, Jacobson MP, et al. Novel procedure for modeling ligand/receptor induced fit effects. J Med Chem 2006; 49: 534-3.
[50]
Sokkar P, Sathis V, Ramachandran M. Computational modeling on the recognition of the HRE motif by HIF-1: Molecular docking and molecular dynamics studies. J Mol Model 2012; 18: 1691-700.
[51]
Schaffer L, Verkhivker GM. Predicting structural effects in HIV-1 protease mutant complexes with flexible ligand docking and protein side-chain optimization. Proteins 1998; 33: 295-310.
[52]
Luty BA, Wasserman ZR, Stouten PF, et al. A molecular mechanics / grid method for evaluation of ligand-receptor interactions. J Comput Chem 1995; 16: 454-64.
[53]
Mangoni M, Roccatano D, Di Nola A. Docking of flexible ligands to flexible receptors in solution by molecular dynamics simulation. Proteins 1999; 35: 153-62.
[54]
Nowosielski M, Hoffmann M, Kuron A, et al. The MM2QM tool for combining docking, molecular dynamics, molecular mechanics, and quantum mechanics. J Comput Chem 2013; 34: 750-6.
[55]
Huang Z, Wong CF, Wheeler RA. Flexible protein-flexible ligand docking with disrupted velocity simulated annealing. Proteins 2008; 71: 440-54.
[56]
Antes I. DynaDock: A new molecular dynamics-based algorithm for protein-peptide docking including receptor flexibility. Proteins 2010; 78: 1084-04.
[57]
Whalen KL, Chang KM, Spies MA. Hybrid steered molecular dynamics-docking: An efficient solution to the problem of ranking inhibitor affinities against a flexible drug target. Mol Inform 2011; 30: 459-71.
[58]
Armen RS, Chen J, Brooks III CL. An evaluation of explicit receptor flexibility in molecular docking using molecular dynamics and torsion angle molecular dynamics. J Chem Theory Comput 2009; 5: 2909-23.
[59]
Teodoro ML, Kavraki LE. Conformational flexibility models for the receptor in structure based drug design. Curr Pharm Des 2003; 9: 1635-48.
[60]
Borrelli KW, Cossins B, Guallar V. Exploring hierarchical refinement techniques for induced fit docking with protein and ligand flexibility. J Comput Chem 2010; 31: 1224-35.
[61]
Leis S, Zacharias M. Efficient inclusion of receptor flexibility in grid based protein-ligand docking. J Comput Chem 2011; 32: 3433-9.
[62]
Teodoro ML, Phillips Jr GN, Kavraki LE. Understanding protein flexibility through dimensionality reduction. J Comput Biol 2003; 10: 617-34.
[63]
Zacharias M. Rapid protein-ligand docking using soft modes from molecular dynamics simulations to account for protein deformability: Binding of FK506 to FKBP. Proteins 2004; 54: 759-67.
[64]
Bottegoni G, Kufareva I, Totrov M, Abagyan R. Four-dimensional docking: A fast and accurate account of discrete receptor flexibility in ligand docking. J Med Chem 2009; 52: 397-406.
[65]
Nabuurs SB, Wagener M, de Vlieg J. A flexible approach to induced fit docking. J Med Chem 2007; 50: 6507-18.
[66]
Yan Y, Wen Z, Wang X, Huang S-Y. Addressing recent docking challenges: A hybrid strategy to integrate template‐based and free protein‐protein docking. Proteins 2017; 85(3): 497-512.
[67]
Corbeil CR, Englebienne P, Moitessier N. Docking ligands into flexible and solvated macromolecules. 1. Development and validation of FITTED 1.0. J Chem Inf Model 2007; 47: 435-49.
[68]
Huang SY, Zou X. Ensemble docking of multiple protein structures: Considering protein structural variations in molecular docking. Proteins 2007; 66: 399-421.
[69]
Knegtel RM, Kuntz ID, Oshiro CM. Molecular docking to ensembles of protein structures. J Mol Biol 1997; 266: 424-40.
[70]
Xu M, Lill MA. Significant enhancement of docking sensitivity using implicit ligand sampling. J Chem Inf Model 2011; 51: 693-706.
[71]
Xu M, Lill MA. Utilizing experimental data for reducing ensemble size in flexible-protein docking. J Chem Inf Model 2012; 52: 187-98.
[72]
Barril X, Fradera X. Incorporating protein flexibility into docking and structure-based drug design. Expert Opin Drug Discov 2006; 1: 335-49.
[73]
Corbeil CR, Therrien E, Moitessier N. Modeling reality for optimal docking of small molecules to biological targets. Curr Computeraided Drug Des 2009; 5: 241-63.
[74]
Rueda M, Bottegoni G, Abagyan R. Recipes for the selection of experimental protein conformations for virtual screening. J Chem Inf Model 2010; 50: 186-93.
[75]
Trott O, Olson AJ. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 2010; 31: 455-61.
[76]
Correa-Basurto J, Ramos-Morales FR, Matus MH, et al. Docking and DFT studies to explore the Topoisomerase II ATP pocket employing 3-substituted 2,6-piperazindiones for drug design. Mol Simul 2012; 38: 1072-84.
[77]
Shoichet BK, Bodian DL, Kuntz ID. Molecular docking using shape descriptors. J Comput Chem 1992; 13: 380-97.
[78]
Janin J, Cherfils J. Protein docking algorithms: simulating molecular recognition. Curr Opin Struct Biol 1993; 3: 265-9.
[79]
Apostolakis J, Plückthun A, Caflisch A. Docking small ligands inflexible binding sites. J Comput Chem 1998; 19: 21-37.
[80]
Schaffer L, Verkhivker GM. Predicting structural effects in HIV-1protease mutant complexes with flexible ligand docking and proteinside-chain optimization. Proteins 1998; 33: 295-310.
[81]
Burnett RM, Taylor JS. DARWIN: A program for docking flexible molecules. Proteins 2000; 41: 173-91.
[82]
Miranker A, Karplus M. Functionality maps of binding sites: a multiple copy simultaneous search method. Proteins 1991; 11(1): 29-34.
[83]
Roberts VA, Pique ME. Definition of the interaction domain for cytochrome c on cytochrome c oxidase. J Biol Chem 1999; 274: 38051-60.
[84]
Nichols SE, Baron R, Ivetac A, McCammon JA. Predictive power of molecular dynamics receptor structures in virtual screening. J Chem Inf Model 2011; 51: 1439-46.
[85]
Wu G, Robertson DH, Brooks CL, Vieth MD. Detailed analysis of grid-based molecular docking: A case study of CDOCKER? A CHARMm-based MD docking algorithm. J Comput Chem 2003; 24: 1549-62.
[86]
Korb O, Olsson TSG, Bowden SJ, et al. Potential and Limitations of Ensemble Docking. J Chem Inf Model 2012; 52(5): 1262-74.
[87]
Ewing TJ, Makino S, Skillman AG, Kuntz ID. DOCK 4.0: search strategies for automated molecular docking of flexible molecule databases. J Comput Aided Mol Des 2001; 15: 411-28.
[88]
Miller MD, Kearsley SK, Underwood DJ, Sheridan RP. FLOG: a system to select “quasi-flexible” ligands complementary to a receptor of known three-dimensional structure. J Comput Aided Mol Des 1994; 8: 153-74.
[89]
Kuhl FS, Crippen GM, Friesen DK. A combinatorial algorithm for calculating ligand binding. J Comput Chem 1984; 5: 24-34.
[90]
Smellie AS, Crippen GM, Richards WG. Fast drug-receptor mapping by site-directed distances: a novel method of predicting new pharmacological leads. J Chem Inf Model 1991; 31: 386-92.
[91]
Rarey M, Kramer B, Lengauer T, Klebe G. A fast flexible docking method using an incremental construction algorithm. J Mol Biol 1996; 261: 470-89.
[92]
Welch W, Ruppert J, Jain AN. Hammerhead: fast, fully automated docking of flexible ligands to protein binding sites. Chem Biol 1996; 3: 449-62.
[93]
Rarey M, Kramer B, Lengauer T. Time-efficient docking of flexible ligands into active sites of proteins. Proc Int Conf Intell Syst Mol Biol 1995; 3: 300-8.
[94]
Rarey M, Kramer B, Lengauer T, Klebe G. A fast flexible docking method using an incremental construction algorithm. J Mol Biol 1996; 261: 470-89.
[95]
Rarey M, Kramer B, Lengauer T. Multiple automatic base selection: protein-ligand docking based on incremental construction without manual intervention. J Comput Aided Mol Des 1997; 11: 369-84.
[96]
Schlosser J, Rarey M. Beyond the virtual screening paradigm: structure-based searching for new lead compounds. J Chem Inf Model 2009; 49: 800-9.
[97]
Huang N, Shoichet BK, Irwin JJ. Benchmarking Sets for Molecular Docking. J Med Chem 2006; 49: 6789-801.
[98]
Mark McGann. FRED pose prediction and virtual screening accuracy. J Chem Inf Model 2011; 51: 578-96.
[99]
Friesner RA, Banks JL, Murphy RB, et al. Glide: A new approach for rapid, accurate docking and scoring. 1. method and assessment of docking accuracy. J Med Chem 2004; 47: 1739-49.
[100]
Zsoldos Z, Reid D, Simon A, et al. eHiTS: a new fast, exhaustive flexible ligand docking system. J Mol Graph Model 2007; 26: 198-212.
[101]
Gorelik B, Goldblum A. High quality binding modes in docking ligands to proteins. Proteins Struct Funct Bioinform 2008; 71: 1373-86.
[102]
Eberhart RC, Kennedy J. A new optimizer using particle swarm theory. Proceedings of the sixth international symposium on micromachine and human science. Nagoya, Japan. 1995; pp. 39-43.
[103]
Bai Q. Analysis of particle swarm optimization algorithm, computer and information science, vol. volume 3 No 1, Pebruari In: 2010.
[104]
Rini DP, Shamsuddin SM, Yuhaniz SS. Particle swarm optimization: technique, system and challenges. Int J Comput Appl 2011; 14(1): 19-27.
[105]
Ng MCK, Fong S, Siu SWI. PSOVina: The hybrid particle swarm optimization algorithm for protein–ligand docking. J Bioinform Comput Biol 2015; 13(3)
[http://dx.doi.org/10.1142/S0219720015410073]
[106]
Dorigo M, Caro GD. The ant colony optimization meta-heuristic.In D. Corne, M. Dorigo, and F. Glover, editors, New Ideas in Optimization, pages 11–32. McGraw Hill, London, UK, 1999.
[107]
Dorigo M, Caro GD, Gambardella LM. Ant algorithms for discrete optimization. Artif Life 1999; 5(2): 137-72.
[108]
Dorigo M, St¨utzle T. The ant colony optimization metaheuristic: algorithms, applications, and Advances.2003.In F. Glover and GA. Kochenberger, editors, Handbook of Metaheuristics, Vol.57, pp 250-285, Springer, US, doi10.1007/0-306-48056-5_9.
[109]
Goodsell DS, Lauble H, Stout CD, Olson AJ. Automated docking in crystallography: analysis of the substrates of aconitase. Proteins 1993; 17(1): 1-10.
[110]
Hart TN, Read RJ. A multiple-start monte carlo docking method. Proteins 1992; 13(3): 206-.
[111]
Michel J, Tirado-Rives J, Jorgensen WL. Energetics of displacing water molecules from protein binding sites: Consequences for ligand optimization. J Am Chem Soc 2009; 131: 15403-11.
[112]
Abagyan R, Totrov M, Kuznetsov D. ICM-A new method for protein modeling and design: Applications to docking and structure prediction from the distorted native conformation. J Comput Chem 1994; 15: 488-06.
[113]
McMartin C, Bohacek RS. QXP: powerful, rapid computer algorithms for structure-based drug design. J Comput Aided Mol Des 1997; 11(4): 333-44.
[114]
Molegro Virtual Docker – User manual and references cited therein.
[115]
Schneider G. Automating drug discovery. Nat Rev Drug Discov 2018; 17(2): 97-113.
[116]
Halperin I, Ma B, Wolfson H, Nussinov R. Principles of docking: an overview of search algorithms and a guide to scoring functions. Proteins 2002; 47: 409-43.
[117]
Cheng T, Li X, Li Y, Liu ZC, Wang R. Comparative assessment of scoring functions on a diverse test set. J Chem Inf Model 2009; 49: 1079-93.
[118]
Jain AN. Surflex: Fully automatic flexible molecular docking using a molecular similarity-based search engine. J Med Chem 2003; 46: 499-511.
[119]
Korb O, Stützle T, Exner TE. empirical scoring functions for advanced protein−ligand docking with PLANTS. J Chem Inf Model 2009; 49: 84-96.
[120]
Englebienne P, Moitessier N. Docking ligands into flexible and solvated macromolecules. Force-field-based prediction of binding affinities of ligands to proteins. J Chem Inf Model 2009; 49: 2564-71.
[121]
De Azevedo WF Jr, Dias R. Computational methods for calculation of ligand binding affinity. Curr Drug Targets 2008; 9: 1031-9.
[122]
Verdonk ML, Cole JC, Hartshorn MJ, Murray CW, Taylor RD. Improved protein-ligand docking using GOLD. Proteins 2003; 52(4): 609-23.
[123]
Morris GM, Goodsell DS, Halliday RS, et al. Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J Comput Chem 1998; 19(14): 1639-62.
[124]
Gohlke H, Hendlich M, Klebe G. Knowledge-based scoring function to predict protein-ligand interactions. J Mol Biol 2000; 295: 337-56.
[125]
Meng XY, Zhang HX, Mezei M, Cui M. Molecular Docking: A powerful approach for structure-based drug discovery. Curr Comput Aided Drug Des 2011; 7(2): 146-57.
[126]
DeWitte RS, Shakhnovich EI. SMoG: De novo design method based on simple, fast, and accurate free energy estimates. 1. Methodology and supporting evidence. J Am Chem Soc 1996; 118: 11733-44.
[127]
Fan H, Schneidman-Duhovny D, Irwin JJ, et al. Statistical potential for modeling and ranking of protein-ligand interactions. J Chem Inf Model 2011; 51: 3078-92.
[128]
Kolb P, Irwin JJ. Docking screens: right for the right reasons? Curr Top Med Chem 2009; 9: 755-70.
[129]
Davis IW, Raha K, Head MS, Baker D. Blind docking of pharmaceutically relevant compounds using Rosetta Ligand. Protein Sci 2009; 18: 1998-2002.
[130]
Cheng T, Li X, Li Y, Liu ZC, Wang R. Comparative assessment of scoring functions on a diverse test set. J Chem Inf Model 2009; 49: 1079-93.
[131]
Corbeil CR, Therrien E, Moitessier N. Modeling reality for optimal docking of small molecules to biological targets. Curr Comp Aided Drug Des 2009; 5: 241-63.
[132]
Pearce BC, Langley DR, Kang J, Huang H, Kulkarni A. E-novo: an automated workflow for efficient structure-based lead optimization. J Chem Inf Model 2009; 49: 1797-809.
[133]
Shin W-H, Seok C. Galaxy Dock: Protein-ligand docking with flexible protein side chains. J Chem Inf Model 2012; 52: 3225-32.
[134]
Meng EC, Shoichet BK, Kuntz ID. Automated docking with grid-based energy approach to macromolecule-ligand interactions. J Comput Chem 1992; 13: 505-24.
[135]
Wang W, Donini O, Reyes CM, Kollman PA. Biomolecular simulations: Recent developments in force fields, simulations of enzyme catalysis, protein-ligand, protein-protein, and protein-nucleic acid noncovalent interactions. Annu Rev Biophys Biomol Struct 2001; 30: 211-43.
[136]
Rocchia W, Sridharan S, Nicholls A, Alexov E, Chiabrera A, Honig B. Rapid grid-based construction of the molecular surface and the use of induced surface charge to calculate reaction field energies: Applications to the molecular systems and geometric objects. J Comput Chem 2002; 23: 128-37.
[137]
Still WC, Tempczyk A, Hawley RC, Hendrickson T. Semi analytical treatment of salvation for molecular mechanics and dynamics. J Am Chem Soc 1990; 112: 6127-9.
[138]
Zou X, Sun Y, Kuntz ID. Inclusion of solvation in ligand binding free energy calculations using the generalized-Born model. J Am Chem Soc 1999; 121: 8033-43.
[139]
Liu H-Y, Kuntz ID, Zou X. Pairwise GB/SA scoring function for structure-based drug design. J Phys Chem B 2004; 108: 5453-62.
[140]
Liu H-Y, Zou X. Electrostatics of ligand binding: Parametrization of the generalized born model and comparison with the Poisson-Boltzmann approach. J Phys Chem B 2006; 110: 9304-13.
[141]
Liu H-Y, Grinter SZ, Zou X. Multiscale generalized born modeling of ligand binding energies for virtual database screening. J Phys Chem B 2009; 113: 11793-9.
[142]
Majeux N, Scarsi M, Apostolakis J, Ehrhardt C, Caflisch A. Exhaustive docking of molecular fragments with electrostatic solvation. Proteins 1999; 37: 88-105.
[143]
Cecchini M, Kolb P, Majeux N, Caflisch A. Automated docking of highly flexible ligands by genetic algorithms: A critical assessment. J Comput Chem 2004; 25: 412-22.
[144]
Huang D, Luthi U, Kolb P, et al. Discovery of cell-permeable non-peptide inhibitors of beta-secretase by high-throughput docking and continuum electrostatics calculations. J Med Chem 2005; 48: 5108-11.
[145]
Cho AE, Wendel JA, Vaidehi N, et al. The MPSim-Dock hierarchical docking algorithm: Application to the eight trypsin inhibitor cocrystals. J Comput Chem 2005; 26: 48-71.
[146]
Ghosh A, Rapp CS, Friesner RA. Generalized Born model based on a surface integral formulation. J Phys Chem B 1998; 102: 10983-90.
[147]
Lyne PD, Lamb ML, Saeh JC. Accurate prediction of the relative potencies of members of a series of kinase inhibitors using molecular docking and MM-GBSA scoring. J Med Chem 2006; 49: 4805-8.
[148]
Guimaraes CRW, Cardozo M. MM-GB/SA rescoring of docking poses in structure-based lead optimization. J Chem Inf Model 2008; 48: 958-70.
[149]
Tang YT, Marshall GR. PHOENIX: a scoring function for affinity prediction derived using high-resolution crystal structures and measurements. J Chem Inf Model 2011; 51: 214-28.
[150]
Thomas PD, Dill KA. An iterative method for extracting energy-like quantities from protein structures. Proc Natl Acad Sci USA 1996; 93: 11628-33.
[151]
Koppensteiner WA, Sippl MJ. Knowledge-based potentials–Back to the roots. Biochemistry (Mosc) 1998; 63: 247-52.
[152]
Thomas PD, Dill KA. Statistical potentials extracted from protein structures: How accurate are they? J Mol Biol 1996; 257: 457-69.
[153]
McQuarrie DA. Statistical Mechanics. Harper Collins Publishers New York, NY, USA 1976.
[154]
Zhang C, Liu S, Zhu Q, Zhou Y. A knowledge-based energy function for protein-ligand, protein-protein, and protein-DNA complexes. J Med Chem 2005; 48: 2325-35.
[155]
Zhao X, Liu X, Wang Y, et al. An improved PMF scoring function for universally predicting the interactions of a ligand with protein, DNA, and RNA. J Chem Inf Model 2008; 48: 1438-47.
[156]
Huang S-Y, Zou X. An iterative knowledge-based scoring function to predict protein-ligand interactions: II. Validation of the scoring function. J Comput Chem 2006; 27: 1876-82.
[157]
Sousa SF, Ribeiro AJ, Coimbra J, et al. Protein-ligand docking in the new millennium—A retrospective of 10 years in the field. Curr Med Chem 2013; 20: 2296-314.
[158]
Bissantz C, Kuhn B, Stahl M. A medicinal chemist’s guide to molecular interactions. J Med Chem 2010; 53: 5061-84.
[159]
Michel J, Verdonk ML, Essex JW. Protein-ligand binding affinity predictions by implicit solvent simulations: a tool for lead optimization? J Med Chem 2006; 49(25): 7427-39.
[160]
Amadasi A, Spyrakis F, Cozzini P, et al. Mapping the energetics of water-protein and water-ligand interactions with the “natural” HINT forcefield: Predictive tools for characterizing the roles of water in biomolecules. J Mol Biol 2006; 358: 289-309.
[161]
Kellogg GE, Chen DL. The importance of being exhaustive. Optimization of bridging structural water molecules and water networks in models of biological systems. Chem Biodivers 2004; 1: 98-105.
[162]
Fuller JC, Burgoyne NJ, Jackson RM. Predicting druggable binding sites at the protein-protein interface. Drug Discov Today 2009; 14: 155-61.
[163]
Meireles LM, Dömling AS, Camacho CJ. ANCHOR: A web server and database for analysis of protein-protein interaction binding pockets for drug discovery. Nucleic Acids Res 2010; 38: W407-11.
[164]
Laurie AT, Jackson RM. Q-SiteFinder: An energy-based method for the prediction of protein-ligand binding sites. Bioinformatics 2005; 21: 1908-16.
[165]
Dominguez C, Boelens R, Bonvin AM. HADDOCK: A protein-protein docking approach based on biochemical or biophysical information. J Am Chem Soc 2003; 125: 1731-7.
[166]
O’Boyle NM, Liebeschuetz JW, Cole JC. Testing assumptions and hypotheses for rescoring success in protein-ligand docking. J Chem Inf Model 2009; 49: 1871-8.
[167]
Kukol A. Consensus virtual screening approaches to predict protein ligands. Eur J Med Chem 2011; 46: 4661-4.
[168]
Huang N, Shoichet BK, Irwin JJ. Benchmarking sets for molecular docking. J Med Chem 2006; 49: 6789-801.
[169]
Chang MW, Ayeni C, Breuer S, Torbett BE. Virtual screening for HIV protease inhibitors: A comparison of AutoDock 4 and Vina. PLoS One 2010; 5: e11955.
[170]
Houston DR, Walkinshaw MD. Consensus Docking: Improving the Reliability of Docking in a Virtual Screening Context. J Chem Inf Model 2013; 53: 384-90.
[171]
Wandzik I. Current Molecular docking tools and comparisons thereof. MATCH Commun Math Comput Chem 2006; 55: 271-8.
[172]
Murray CW, Baxter CA, David Frenkel AD. The sensitivity of the results of molecular docking to induced fit effects: Application to thrombin, thermolysin and neuraminidase. J Comput Aided Mol Des 1999; 13: 547-62.
[173]
Saikia S, Kolita B, Dutta PP, et al. Marine steroids as potential anticancer drug candidates: In silico investigation in search of inhibitors of Bcl-2 and CDK-4/Cyclin D1. Steroid 2015; 102: 7-16.
[174]
Bordoloi MJ, Saikia S, Kolita B, et al. Volatile Inhibitors of Phosphatidylinositol-3-Kinase (PI3K) Pathway: Anti-Cancer Potential of Aroma Compounds of Plant Essential Oils. Anticancer Agents Med Chem 2018; 18(1): 87-109.
[175]
Fan H, Irwin JJ, Webb BM, Klebe G, Shoichet BK, Sali A. Molecular docking screens using comparative models of proteins. J Chem Inf Model 2009; 49: 2512-27.
[176]
Talukdar M, Bordoloi M, Dutta PP, et al. Structure elucidation and biological activity of antibacterial compound from Micromonospora auratinigra, a soil Actinomycetes. J Appl Microbiol 2016; 121(4): 973-87.
[177]
Morris GM, Huey R, Lindstrom W, et al. AutoDock4 and AutoDock Tools4: automated docking with selective receptor flexibility. J Comput Chem 2009; 30: 2785-91.
[178]
Davis IW, Baker D. RosettaLigand docking with full ligand and receptor flexibility. J Mol Biol 2009; 385: 381-92.
[179]
Sherman W, Beard HS, Farid R. Use of an induced fit receptor structure in virtual screening. Chem Biol Drug Des 2006; 67: 83-4.
[180]
Lauria A, Ippolito M, Almerico AM. Inside the Hsp90 inhibitors binding mode through induced fit docking. J Mol Graph Model 2009; 27: 712-22.
[181]
Barreca ML, Iraci N, De Luca L, Chimirri A. Induced-fit docking approach provides insight into the binding mode and mechanism of action of HIV-1 integrase inhibitors. ChemMedChem 2009; 4: 1446-56.
[182]
King AR, Dotsey EY, Lodola A, et al. Discovery of potent and reversible monoacylglycerol lipase inhibitors. Chem Biol 2009; 16: 1045-52.
[183]
Onodera K, Satou K, Hirota H. Evaluations of molecular docking programs for virtual screening. J Chem Inf Model 2007; 47(4): 1609-18.
[184]
Cole JC, Murray CW, Nissink JW, Taylor RD, Taylor R. Comparing protein-ligand docking programs is difficult. Proteins 2005; 60(3): 325-32.
[185]
Warren GL, Andrews CW, Capelli AM, et al. A critical assessment of docking programs and scoring functions. J Med Chem 2006; 49(20): 5912-31.
[186]
Oda A, Yamaostu N, Hirono S, et al. Effects of initial settings on computational protein–ligand docking accuracies for several docking programs. Mol Simul 2015; 41: 10-2.
[187]
Huang S-Y. Comprehensive assessment of flexible-ligand docking algorithms: current effectiveness and challenges. Brief Bioinform 2017; 1-13.
[188]
Ban T, Ohue M, Akiyama Y. Multiple grid arrangement improves ligand docking with unknown binding sites: Application to the inverse docking problem. Comput Biol Chem 2018; 73: 139-46.
[189]
Ashtawy HM, Mahapatra NR. Task-Specific Scoring Functions for Predicting Ligand Binding Poses and Affinity and for Screening Enrichment. J Chem Inf Model 2018; 58(1): 119-33.
[190]
Huang S, Song C, Wang X, et al. Discovery of new sirt2 inhibitors by utilizing a consensus docking/scoring strategy and structure-activity relationship analysis. J Chem Inf Model 2017; 57(4): 669-79.
[191]
Ren X, Shi YS, Zhang Y, et al. A novel consensus docking strategy to improve the ligand pose prediction. J Chem Inf Model 2018; 58(8): 1662-8.
[192]
Scarpino A, Ferenczy GG, Keserű GM. Comparative Evaluation of Covalent Docking Tools. J Chem Inf Model 2018; 58(7): 1441-58.
[193]
Agnihotri P, Mishra AK, Mishra S, et al. Identification of novel inhibitors of leishmania donovani γ-glutamylcysteine synthetase using structure-based virtual screening, docking, molecular dynamics simulation, and in vitro studies. J Chem Inf Model 2017; 57(4): 815-25.
[194]
Frączek T, Siwek A, Paneth P. Assessing molecular docking tools for relative biological activity prediction: a case study of triazole HIV-1 NNRTIs. J Chem Inf Model 2013; 53(12): 3326-42.
[195]
Nurisso A, Bravo J, Carrupt PA, Daina A. Molecular docking using the molecular lipophilicity potential as hydrophobic descriptor: impact on GOLD docking performance. J Chem Inf Model 2012; 52(5): 1319-27.
[196]
Ericksen SS, Wu H, Zhang H, et al. Machine learning consensus scoring improves performance across targets in structure-based virtual screening. J Chem Inf Model 2017; 57(7): 1579-90.
[197]
Sønderby P, Rinnan Å, Madsen JJ, et al. Small-angle x-ray scattering data in combination with rosettadock improves the docking energy landscape. J Chem Inf Model 2017; 57(10): 2463-75.
[198]
Chaskar P, Zoete V, Röhrig UF. On-the-fly qm/mm docking with attracting cavities. J Chem Inf Model 2017; 57(1): 73-84.
[199]
Zhou P, Li B, Yan Y, et al. Hierarchical flexible peptide docking by conformer generation and ensemble docking of peptides. J Chem Inf Model 2018; 58(6): 1292-302.
[200]
Vistoli G, Mazzolari A, Testa B, Pedretti A. Binding space concept: a new approach to enhance the reliability of docking scores and its application to predicting butyrylcholinesterase hydrolytic activity. J Chem Inf Model 2017; 57(7): 1691-702.
[201]
Takemura K, Sato C, Kitao A. ColDock: concentrated ligand docking with all-atom molecular dynamics simulation. J Phys Chem B 2018; 122(29): 7191-200.
[202]
Alogheli H, Olanders G, Schaal W, Brandt P, Karlén A. Docking of macrocycles: comparing rigid and flexible docking in glide. J Chem Inf Model 2017; 57(2): 190-202.
[203]
Timofeeva OA, Tarasova NI, Zhang X, et al. STAT3 suppresses transcription of proapoptotic genes in cancer cells with the involvement of its N-terminal domain. Proc Natl Acad Sci USA 2013; 110: 1267-72.
[204]
Matsuno K, Masuda Y, Uehara Y, et al. Identification of a new series of STAT3 inhibitors by virtual screening. ACS Med Chem Lett 2010; 1: 371-5.
[205]
Marrakchi H, Lanéelle G, Quémard A. InhA, a target of the antituberculous drug isoniazid, is involved in a mycobacterial fatty acid elongation system, FAS-II. Microbiology 2000; 146: 289-96.
[206]
Pauli I, dos Santos RN, Rostirolla DC, et al. Discovery of new inhibitors of Mycobacterium tuberculosis InhA enzyme using virtual screening and a 3D-pharmacophore-based approach. J Chem Inf Model 2013; 53: 2390-401.
[207]
Dadashpour S. TuyluKucukkilinc T, Unsal Tan O, et al Design, synthesis and in vitro study of 5,6-diaryl-1,2,4-triazine-3-ylthioacetate derivatives as COX-2 and β-amyloid aggregation inhibitors. Arch Pharm 2015; 348: 179-87.
[208]
Ren JX, Li LL, Zheng RL, et al. Discovery of novel Pim-1 kinase inhibitors by a hierarchical multistage virtual screening approach based on SVM model, pharmacophore, and molecular docking. J Chem Inf Model 2011; 51: 1364-75.
[209]
Wang L, Gu Q, Zheng X, et al. Discovery of new selective human aldose reductase inhibitors through virtual screening multiple binding pocket conformations. J Chem Inf Model 2013; 53: 2409-22.
[210]
Efferth T, Koch E. complex interactions between phytochemicals. the multi-target therapeutic concept of phytotherapy. Curr Drug Targets 2011; 12: 122-32.
[211]
Jorgensen WL. The many roles of computation in drug. Science (New York, NY) 2004; 303: 1813-8.
[212]
Hardy LW, Malikayil A. The impact of structure-guided drug design on clinical agents www.currentdrugdiscovery.com 2003; 15- 9.
[213]
Maryanoff BE. Inhibitors of serine proteases as potential therapeutic agents: The road from thrombin to tryptase to cathepsin g. J Med Chem 2004; 7(4): 770-87.
[214]
Kitchen DB, Decornez H, Furr JR, Bajorath J. Docking and scoring in virtual screening for drug discovery: methods and applications. Nat Rev Drug Discov 2004; 3(11): 935-49.
[215]
Wang L, Wu Y, Deng Y, et al. Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way of a modern free-energy calculation protocol and force field. J Am Chem Soc 2015; 137(7): 2695-703.
[216]
Vilar S, Karpiak J, Costanzi S. Ligand and structure-based models for the prediction of ligandreceptor affinities and virtual screenings: Development and application to the β2-adrenergic receptor. J Comput Chem 2010; 31: 707-20.
[217]
Costanzi S, Tikhonova IG, Ohno M, et al. P2Y1 antagonists: Combining receptor-based modeling and QSAR for a quantitative prediction of the biological activity based on consensus scoring. J Med Chem 2007; 50: 3229-41.
[218]
Robertson JG. Enzymes as a special class of therapeutic target: Clinical drugs and modes of action. Curr Opin Struct Biol 2007; 17: 674-9.
[219]
Ouyang X, Zhou S, Su CTT, et al. Covalent Dock: Automated covalent docking with parameterized covalent linkage energy estimation and molecular geometry constraints. J Comput Chem 2013; 34: 326-36.
[220]
Zhu K, Bonelli KW, Greenwood JR, et al. Docking covalent inhibitors: a parameter free approach to pose prediction and scoring. J Chem Inf Model 2014; 54: 1932-40.
[221]
Wallach I, Dzamba M, Heifets A. Atomnet: A deep convolutional neural network for bioactivity prediction in structure-based drug discovery. arXiv preprint arXiv:1510.02855, 2015
[222]
Kashima H, Hisashi, and Akihiro Inokuchi A. Kernels for graph classification. ICDM Workshop on Active Mining 2002: 2002.
[223]
von Behren MM, Bietz S, Nittinger E, Rarey M. mRAISE: an alternative algorithmic approach to ligand-based virtual screening. J Comput Aided Mol Des 2016; 30(8): 583-94.
[224]
Okuno T, Kato K, Terada TP, Sasai M, Chikenji G. VS-APPLE: A Virtual Screening Algorithm Using Promiscuous Protein−Ligand Complexes. J Chem Inf Model 2015; 55: 1108-19.
[225]
Wang N, Wang L, Xie XQ. ProSelection: A novel algorithm to select proper protein structure subsets for in silico target identification and drug discovery research. J Chem Inf Model 2017; 57(11): 2686-98.
[226]
Krull F, Korff G, Elghobashi-Meinhardt N, Knapp EW. ProPairs: a data set for protein-protein docking. J Chem Inf Model 2015; 55(7): 1495-507.
[227]
Iakovou G, Hayward S, Laycock SD. Virtual environment for studying the docking interactions of rigid biomolecules with haptics. J Chem Inf Model 2017; 57(5): 1142-52.
[228]
Szalay A, Gray J. 2020 computing: Science in an exponential world. Nature 2006; 440(7083): 413-4.
[229]
Zou J, Han Y, So SS. Overview of artificial neural networks. inArtificial Neural Networks (Methods in Molecular Biology) D J Livingstone, Ed. Totowa, NJ, USA: Humana Press 2009; Vol. 458: pp. 14-22.
[230]
Wei Wang FP, Tung AKH, Yang J. Finding representative set from massive data in Proc 5th IEEE Int Conf Data Mining (ICDM) Sep 2005, pp. s8-15.
[231]
Ballester PJ. Ultrafast shape recognition: Method and applications. Future Med Chem 2011; 3(1): 65-78.
[232]
Schneider G. Virtual screening: An endless staircase? Nat Rev Drug Discov 2010; 9(4): 273-6.
[233]
Ruddigkeit L, van Deursen R, Blum LC, Reymond JL. Enumeration of 166 billion organic small molecules in the chemical universe database GDB-17. J Chem Inf Model 2012; 52(11): 2864-75.
[234]
Ghosh-Dastidar S, Adeli H. Spiking neural networks. Int J Neural Syst 2009; 19(4): 295-308.
[235]
Rossello JL, Canals V, Morro A, Oliver A. Hardware implementation of stochastic spiking neural networks. Int J Neural Syst 2012; 22(4): 1250014.
[236]
Ballester PJ, Westwood I, Laurieri N, Sim E, Richards WG. Prospective virtual screening with ultrafast shape recognition: The identification of novel inhibitors of arylamine N-acetyltransferases. J R Soc Interface 2009; 7(43): 335-42.
[237]
Morro A, Canals V, Oliver A, et al. A stochastic spiking neural network for virtual screening. IEEE Trans Neural Netw Learn Syst 2017 Feb 7
[http://dx.doi.org/10.1109/TNNLS.2017.2657601]
[238]
Hongjian Li, Leung K-S, Wong M-H, Ballester PJ. Correcting the impact of docking pose generation error on binding affinity prediction. BMC Bioinfo 2016; 17(Suppl. 11): 308. https://doi.org/10.1186/s12859-016-1169-4
[239]
Ballester PJ, Mitchell JBO. A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking. Bioinformatics 2010; 26(9): 1169-75.
[240]
Ballester PJ, Schreyer A, Blundell TL. Does a more precise chemical description of protein–ligand complexes lead to more accurate prediction of binding affinity? J Chem Inf Model 2014; 54(3): 944-55.
[241]
Li H, Leung KS, Wong MH, Ballester PJ. Improving autodock vina using random forest: the growing accuracy of binding affinity prediction by the effective exploitation of larger data sets. Mol Inform 2015; 34(2-3): 115-26.
[242]
Durrant JD, McCammon JA. NNScore 2.0: a neural-network receptor-ligand scoring function. J Chem Inf Model 2011; 51: 2897-903.
[243]
Ain QU, Aleksandrova A, Roessler FD, Ballester PJ. Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screeningWIREs Comput Mol Sci 2015.
[244]
Cheng T, Li Q, Zhou Z, Wang Y, Bryant SH. Structure-based virtual screening for drug discovery: a problem-centric review. AAPS J 2012; 14: 133-41.
[245]
Huang S-Y, Grinter SZ, Zou X. Scoring functions and their evaluation methods for protein–ligand docking: recent advances and future directions. Phys Chem Chem Phys 2010; 12: 12899-908.
[246]
Ma D-L, Chan DS-H, Leung C-H. Drug repositioning by structure-based virtual screening. Chem Soc Rev 2013; 42: 2130-41.
[247]
Wójcikowski M, Ballester PJ, Siedlecki P. Performance of machine-learning scoring functions in structure-based virtual screening. Sci Rep2017, Apr 25 7: 46710.
[http://dx.doi.org/10.1038/srep46710]
[248]
Lin C, Chen W, Qiu C, et al. LibD3C: Ensemble classifiers with a clustering and dynamic selection strategy. Neurocomputing 2014; 123: 424-35.
[249]
Pan AC, Borhani DW, Dror RO, Shaw DE. Molecular determinants of drug-receptor binding kinetics. Drug Discov Today 2013; 18: 667-73.
[250]
Copeland RA, Pompliano DL, Meek TD. Opinion–drug-target residence time and its implications for lead optimization. Nat Rev Drug Discov 2006; 5: 730-9.
[251]
Hughes JP, Rees S, Kalindjian SB, Philpott KL. Principles of early drug discovery. Br J Pharmacol 2011; 162: 1239-49.
[252]
Bains W. Failure rates in drug discovery and development: will we ever get any better? Drug Discov World 2004; 5: 9-18.
[253]
Mullard A. New drug costs US $2.6 billion to develop. Nat Rev Drug Discov 2014; 13: 877.
[254]
Wishart DS, Jewison T, Guo AC, et al. HMDB 3.0 - the Human Metabolome Database in 2013. Nucleic Acids Res 2013; 41: D801-7.
[255]
Kim JW, Dang CV. Cancer’s molecular sweet tooth and the Warburg effect. Cancer Res 2006; 66: 8927-30.
[256]
Wishart DS. Emerging applications of metabolomics in drug discovery and precision medicine. Nat Rev Drug Discov 2016; 15(7): 473-84.
[257]
Thomas DW, Burn J, Audette J, et al. Clinical development duccess Rates 2006 2015.
[258]
Smietana K, Siatkowski M, Møller M. Trends in clinical success rates. Nat Rev Drug Discov 2016; 15(6): 379-80.
[259]
Moffat JG, Vincent F, Lee JA, Eder J, Prunotto M. Opportunities and challenges in phenotypic drug discovery: an industry perspective. Nat Rev Drug Discov 2017; 16(8): 531-43.
[260]
Nature Reviews Drug Discovery, Published online 28 Apr 2017;
[http://dx.doi.org/10.1038/nrd.2017.69]
[261]
Nature Reviews Drug Discovery, Published online 28 Dec 2017;
[http://dx.doi.org/10.1038/nrd.2017.262]
[262]
Bollag G, Tsai J, Zhang J, et al. Vemurafenib: the first drug approved for BRAF-mutant cancer. Nat Rev Drug Discov 2012; 11(11): 873-86.
[263]
Szőllősi E, Bobok A, Kiss L, et al. Cell-based and virtual fragment screening for adrenergic α2C receptor agonists. Bioorg Med Chem 2015; 1523(14): 3991-9.
[264]
Scott DE, Bayly AR, Abell C, Skidmore J. Small molecules, big targets: drug discovery faces the protein-protein interaction challenge. Nat Rev Drug Discov 2016; 15(8): 533-50.
[266]
Harrison RK. Phase II and phase III failures: 2013-2015. Nat Rev Drug Discov 2016; 15(12): 817-8.
[267]
Blaschke TF, Osterberg L, Vrijens B, Urquhart J. Adherence to medications: insights arising from studies on the unreliable link between prescribed and actual drug dosing histories. Annu Rev Pharmacol Toxicol 2012; 52: 275-301.
[268]
Mullard A. 2016 FDA drug approvals. Nat Rev Drug Discov 2017
[http://dx.doi.org/10.1038/nrd.2017.14]
[269]
Mullard A. FDA drug approvals. Nat Rev Drug Discov 2017; 2018
[http://dx.doi.org/10.1038/nrd.2018.4]
[270]
Vlahović-Palčevski V, Mentzer D. Postmarketing surveillance. Handb Exp Pharmacol 2011; 205: 339-51.
[271]
Suvarna V. Phase IV of Drug Development. Perspect Clin Res 2010; 1(2): 57-60.
[272]
Pitts PJ, Louet HL, Moride Y, Conti RM. 21st century pharmacovigilance: efforts, roles, and responsibilities. Lancet Oncol 2016; 17: e486-92.
[273]
Mullard A. FDA unveils searchable adverse events system. Nat Rev Drug Discov 2017; 16(11): 743.
[274]
Zeitoun JD, Ross JS, Atal I, et al. Factors associated with post-marketing research for approved indications for novel medicines approved by both the FDA and EMA between 2005 and 2010: A multivariable analysis. Clin Pharmacol Ther 2018; 104(5): 1000-7.
[275]
Maeda K, Katashima R, Ishizawa K, Yanagawa H. Japanese Physicians’ Views on Drug Post-Marketing Surveillance. J Clin Med Res 2015; 7(12): 956-60.
[276]
Xiao C, Li Y, Baytas IM, Zhou J, Wang F. An MCEM Framework for drug safety signal detection and combination from heterogeneous real world evidence. Sci Rep 2018; 8
[http://dx.doi.org/10.1038/s41598-018-199797]
[277]
Butler SF, McNaughton EC, Black RA, Cassidy TA. Evaluation of the relative abuse of an oros® extended-release hydromorphone hci product: Results from three Post-market Surveillance Studies. Clin J Pain 2018; 34(7): 618-28.
[278]
DiMasi JA, Grabowski HG, Hansen RW. Innovation in the pharmaceutical industry: new estimates of R&D costs. J Health Econ 2016; 47: 20-33.
[279]
Dixit R, David FS. Market watch: Trends in pharmaceutical company R&D spending: 2005-2015. Nat Rev Drug Discov 2017; 16(6): 376.
[280]
Gilliland CT, Zuk D, Kocis P, et al. Putting translational science on to a global stage. Nat Rev Drug Discov 2016; 15(4): 217-8.
[281]
Boycott KM, Vanstone MR, Bulman DE, MacKenzie AE. Rare-disease genetics in the era of next-generation sequencing: discovery to translation. Nat Rev Genet 2013; 14: 681-91.
[282]
Mullard A. FDA approves first digital pill. Nat Rev Drug Discov 2017; 16: 818.
[283]
Hunter NL, Rao GR, Sherman RE. Flexibility in the FDA approach to orphan drug development. Nat Rev Drug Discov 2017; 16(11): 737-8.
[284]
Kodamullil AT, Zekri F, Sood M, et al. Tracing investment in drug development for Alzheimer disease. Nat Rev Drug Discov 2017; 16(12): 819.
[285]
King RD, Rowland J, Oliver SG, et al. The automation of science. Science 2009; 324: 85-9.
[286]
Sanderson K. March of the synthesis machines. Nat Rev Drug Discov 2015; 14: 299-300.
[287]
Harrison S, Lahue B, Peng Z, et al. Extending ‘predict first’ to the design-make-test cycle in small-molecule drug discovery. Future Med Chem 2017; 9(6): 533-6.
[288]
Reutlinger M, Rodrigues T, Schneider P, Schneider G. Combining On-chip synthesis of a focused combinatorial library with computational target prediction reveals imidazopyridine GPCR ligands. Angew Chem Int Ed 2014; 53: 582-5.
[289]
Schneider P, Röthlisberger M, Reker D, Schneider G. Spotting and designing promiscuous ligands for drug discovery. Chem Commun (Camb) 2016; 52: 1135-8.
[290]
Rodrigues T, Reker D, Welin M. De novo fragment design for drug discovery and chemical biology. Angew Chem Int Ed 2015; 54: 15079-83.
[291]
Friedrich L, Rodrigues T, Neuhaus CS, Schneider P, Schneider G. From complex natural products to simple synthetic mimetics by computational de novo design. Angew Chem Int Ed 2016; 55: 6789-92.
[292]
Schneider G. Automating drug discovery. Nat Rev Drug Discov 2018; 17(2): 97-113.


Rights & PermissionsPrintExport Cite as


Article Details

VOLUME: 20
ISSUE: 5
Year: 2019
Page: [501 - 521]
Pages: 21
DOI: 10.2174/1389450119666181022153016
Price: $58

Article Metrics

PDF: 42
HTML: 2
EPUB: 1