Use of machine learning approaches for novel drug discovery

Expert Opinion on Drug Discovery

Volumn 11, Issue 3, 2016, Pages 225-239

  Lima, Angélica Nakagawa ^a   Philot, Eric Allison ^a   Trossini, Gustavo Henrique Goulart ^b   Scott, Luis Paulo Barbour ^a   Maltarollo, Vinícius Gonçalves ^b   Honorio, Kathia Maria ^a,b  

^a UNIVERSIDADE FEDERAL DO ABC   (Brazil)

^b UNIVERSITY OF SÃO PAULO   (Brazil)

Author keywords

Drug Design; LBDD; Machine Learning; SBDD

Indexed keywords

LIGAND; NEW DRUG;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; DECISION TREE; DRUG ABSORPTION; DRUG ACTIVITY; DRUG BINDING SITE; DRUG CLASSIFICATION; DRUG DESIGN; DRUG DISTRIBUTION; DRUG EXCRETION; DRUG METABOLISM; DRUG PROTEIN BINDING; DRUG SCREENING; K NEAREST NEIGHBOR; LIGAND BASED DRUG DESIGN; MACHINE LEARNING; MEDICINAL CHEMISTRY; MODEL; MOLECULAR DOCKING; MOLECULAR DYNAMICS; NUCLEAR MAGNETIC RESONANCE; PERCEPTRON; PHARMACOPHORE; PREDICTION; PRIORITY JOURNAL; PROTEIN SECONDARY STRUCTURE; QUANTITATIVE STRUCTURE ACTIVITY RELATION; RANDOM FOREST; REVIEW; SCORING SYSTEM; STRUCTURE BASED DRUG DESIGN; SUPPORT VECTOR MACHINE; VIRTUAL REALITY; X RAY CRYSTALLOGRAPHY; BINDING SITE; BIOLOGICAL MODEL; DRUG DEVELOPMENT; HUMAN; PROCEDURES;

BINDING SITES; DECISION TREES; DRUG DESIGN; DRUG DISCOVERY; HUMANS; LIGANDS; MACHINE LEARNING; MODELS, BIOLOGICAL; MOLECULAR DOCKING SIMULATION; NEURAL NETWORKS (COMPUTER); SUPPORT VECTOR MACHINE;

EID: 84958543290     PISSN: 17460441     EISSN: 1746045X     Source Type: Journal    
DOI: 10.1517/17460441.2016.1146250     Document Type: Review

References (158)

1. Viegas C Jr, Bolzani VDS, Barreiro EJ. Os produtos naturais e a química medicinal moderna. Química Nova. 2006;29(2):326-337. doi:10.1590/S0100-40422006000200025.
2. Guido RVC, Oliva G, Andricopulo AD. Virtual screening and its integration with modern drug design technologies. Curr Med Chem. 2008;15(1):37-46. doi:10.2174/092986708783330683.
3. Macarron R. Critical review of the role of HTS in drug discovery. Drug Discov Today. 2006;11(7-8):277-279. doi:10.1016/j.drudis.2006. 02.001.
4. Reker D, Schneider G. Active-learning strategies in computer-assisted drug discovery. Drug Discov Today. 2015;20(4):458-465. doi:10.1016/ j.drudis.2014.12.004.
5. Drews J. Drug discovery: a historical perspective. Science. 2000;287(5460):1960-1964. doi:10.1126/science.287.5460.1960.
6. Patrick GL. An introduction to medical chemistry. New York: Oxford University Press; 1995.
7. Chandra N. Computational systems approach for drug target discovery. Expert Opin Drug Discov. 2009;4(12):1221-1236. doi:10.1517/ 17460440903380422.
8. Dean PM, Lloyd DG, Todorov NP. De novo drug design: integration of structure-based and ligand-based methods. Curr Opin Drug Discov Devel. 2004;7(3):347-353.
9. Nantasenamat C, Prachayasittikul V. Maximizing computational tools for successful drug discovery. Expert Opin Drug Discov. 2015;10(4):321-329. doi:10.1517/17460441.2015.1016497.
10. Nantasenamat C, Isarankura-Na-Ayudhya C, Prachayasittikul V. Advances in computational methods to predict the biological activity of compounds. Expert Opin Drug Discov. 2010;5(7):633-654. doi:10.1517/17460441.2010.492827.
11. Guido RVC, Oliva G, Andricopulo AD. Modern drug discovery technologies: opportunities and challenges in lead discovery. Comb Chem High Throughput Screen. 2011;14(10):830-839. doi:10.2174/ 138620711797537067.
12. Ferreira RS, Glaucius O, Andricopulo AD. Integração das técnicas de triagem virtual e triagem biológica automatizada em alta escala: oportunidades e desafios em P&D de fármacos. Química Nova. 2011;34(10):1770-1778. doi:10.1590/S0100-40422011001000010.
13. Larrañaga P, Calvo B, Santana R, et al. Machine learning in bioinformatics. Brief Bioinform. 2006;7(1):86-112. doi:10.1093/bib/bbk007.
14. Nielsen H, Brunak S, Von Heijne G. Machine learning approaches for the prediction of signal peptides and other protein sorting signals. Protein Eng Des Selection. 1999;12(1):3-9. doi:10.1093/protein/12.1.3.
15. King RD, Sternberg MJ. Machine learning approach for the prediction of protein secondary structure. J Mol Biol. 1990;216(2):441-457. doi:10.1016/S0022-2836(05)80333-X.
16. Lee BJ, Shin MS, Oh YJ, et al. Identification of protein functions using a machine-learning approach based on sequence-derived properties. Proteome Sci. 2009;7:27. doi:10.1186/1477-5956-7-27.
17. Melville JL, Burke EK, Hirst JD. Machine learning in virtual screening. Comb Chem High Throughput Screen. 2009;12(4):332-343. doi:10.2174/138620709788167980.
18. King RD, Hirst JD, Sternberg MJE. New approaches to QSAR: neural networks and machine learning. Perspect Drug Discov Des. 1993;1(2):279-290. doi:10.1007/BF02174529.
19. Kinnings SL, Liu N, Tonge PJ, et al. A machine learning-based method to improve docking scoring functions and its application to drug repurposing. J Chem Inf Model. 2011;51(2):408-419. doi:10.1021/ ci100369f.
20. Eckert H, Bajorath J. Molecular similarity analysis in virtual screening: foundations, limitations and novel approaches. Drug Discov Today. 2007;12(5-6):225-233. doi:10.1016/j.drudis.2007.01.011.
21. Fox T, Kriegl JM. Machine learning techniques for in silico modeling of drug metabolism. Curr Top Med Chem. 2006;6(15):1579-1591. doi:10.2174/156802606778108915.
22. Maltarollo VG, Gertrudes JC, Oliveira PR, et al. Applying machine learning techniques for ADME-Tox prediction: a review. Expert Opin Drug Metab Toxicol. Informa Healthcare. 2015;11(2):259-271. doi:10.1517/17425255.2015.980814.
23. Kell DB. Metabolomics, modelling and machine learning in systems biology-towards an understanding of the languages of cells. FEBS J. 2006;273(5):873-894.
24. Maltarollo VG, Honório KM, Da Silva ABF. Applications of artificial neural networks in chemical problems. In: Suzuki K, editor. Artificial neural networks-architectures and applications. 1st ed. InTech; 2013. p. 203-223. [cited 2015 Dec 2]. Available at: http://www.intechopen. com/books/artificial-neural-networks-architectures-and-applications/ applications-of-artificial-neural-networks-in-chemical-problems
25. Lavecchia A. Machine-learning approaches in drug discovery: methods and applications. Drug Discov Today. 2015;20(3):318-331. doi:10.1016/j.drudis.2014.10.012.
26. Braga RC, Alves VM, Silva AC, et al. Virtual screening strategies in medicinal chemistry: the state of the art and current challenges. Curr Top Med Chem. 2014;14(16):1899-1912.
27. Glen RC, Adams SE. Similarity metrics and descriptor spaces-which combinations to choose? QSAR Comb Sci. 2006;25(12):1133-1142. doi:10.1002/(ISSN)1611-0218.
28. Cereto-Massagué A, Ojeda MJ, Valls C, et al. Molecular fingerprint similarity search in virtual screening. Methods. 2015;71:58-63. doi:10.1016/j.ymeth.2014.08.005.
29. Hawkins PCD, Skillman AG, Nicholls A. Comparison of shape-matching and docking as virtual screening tools. J Med Chem. American Chemical Society. 2007;50(1):74-82.
30. Smusz S, Kurczab R, Sata?a G, et al. Fingerprint-based consensus virtual screening towards structurally new 5-HT(6)R ligands. Bioorg Med Chem Lett. 2015;25(9):1827-1830. doi:10.1016/j.bmcl.2015.03.049.
31. Tropsha A. Best practices for QSAR model development, validation, and exploitation. Mol Inform. 2010;29(6-7):476-488. doi:10.1002/ minf.201000061.
32. Gramatica P. Principles of QSAR models validation: internal and external. QSAR Comb Sci. 2007;26(5):694-701. doi:10.1002/(ISSN) 1611-0218.
33. Gertrudes JC, Maltarollo VG, Silva RA, et al. Machine learning techniques and drug design. Curr Med Chem. 2012;19(25):4289-4297.
34. Scior T, Medina-Franco JL, Do Q-T, et al. How to recognize and workaround pitfalls in QSAR studies: a critical review. Curr Med Chem. 2009;16(32):4297-4313. doi:10.2174/092986709789578213.
35. Varnek A, Baskin I. Machine learning methods for property prediction in chemoinformatics: quo vadis? J Chem Inf Model. 2012;52(6):1413-1437.
36. Dobchev DA, Pillai GG, Karelson M. In silico machine learning methods in drug development. Curr Top Med Chem. 2014;14(16):1913-1922.
37. Guidance Document On The Validation Of(Quantitative) Structure-Activity Relationship [(Q)SAR] Models. OECD Environment Health and Safety Publications-Series on Testing and Assessment No. 69. Paris, France: Organisation for Economic Co-operation and Development(OECD); 2007. [cited 2016 Jan 4]. Available from: http://www.oecd.org/officialdocuments/publicdisplaydocumentpdf/ ?cote=env/jm/mono%282007%292&doclanguage=en
38. Gaudio AC, Zandonade E. Proposição, validação e análise dos modelos que correlacionam estrutura química e atividade biológica. Química Nova. 2001;24(5):658-671. doi:10.1590/S0100-40422001000500013.
39. Sukumar N, Prabhu G, Saha P. Applications of genetic algorithms in QSAR/QSPR modeling. In: Valadi J, Siarry P, editors. Applications of metaheuristics in process engineering. Cham: Springer International Publishing; 2014. p. 315-324.
40. Niazi A, Leardi R. Genetic algorithms in chemometrics. J Chemom. 2012;26(6):345-351. doi:10.1002/cem.2426.
41. Swamidass SJ, Azencott C-A, Lin T-W, et al. Influence relevance voting: an accurate and interpretable virtual high throughput screening method. J Chem Inf Model. 2009;49(4):756-766.
42. Piir G, Sild S, Maran U. Comparative analysis of local and consensus quantitative structure-activity relationship approaches for the prediction of bioconcentration factor. SAR QSAR Environ Res. 2013;24(3):175-199. doi:10.1080/1062936X.2012.762426.
43. Mitra I, Saha A, Roy K. Development of multiple QSAR models for consensus predictions and unified mechanistic interpretations of the free-radical scavenging activities of chromone derivatives. J Mol Model. 2012;18(5):1819-1840. doi:10.1007/s00894-011-1198-x.
44. Roy K, Kar S, Das RN. Statistical methods in QSAR/QSPR. In: A primer on QSAR/QSPR modeling. Cham: Springer International Publishing; 2015. p. 37-35.
45. Norinder U, Carlsson L, Boyer S, et al. Introducing conformal prediction in predictive modeling. A transparent and flexible alternative to applicability domain determination. J Chem Inf Model. American Chemical Society. 2014;54(6):1596-1603.
46. Sheridan RP. The relative importance of domain applicability metrics for estimating prediction errors in QSAR varies with training set diversity. J Chem Inf Model. 2015;55(6):1098-1107. doi:10.1021/acs. jcim.5b00110.
47. Ferreira MMC. Multivariate QSAR. J Braz Chem Soc. 2002;13(6):742-753. doi:10.1590/S0103-50532002000600004.
48. Hawkins DM. The problem of overfitting. J Chem Inf Comput Sci. 2004;44(1):1-12. doi:10.1021/ci0342472.
49. Eklund M, Norinder U, Boyer S, et al. Benchmarking variable selection in QSAR. Mol Inform. 2012;31(2):173-179. doi:10.1002/minf.201100142.
50. Mehmood T, Liland KH, Snipen L, et al. A review of variable selection methods in partial least squares regression. Chemom Intell Lab Syst. 2012;118:62-69. doi:10.1016/j.chemolab.2012.07.010.
51. Araujo SC, Maltarollo VG, Silva DC, et al. ALK-5 inhibition: a molecular interpretation of the main physicochemical properties related to bioactive ligands. J Braz Chem Soc. 2015;26(9):1936-1946.
52. Teófilo RF, Martins JPA, Ferreira MMC. Sorting variables by using informative vectors as a strategy for feature selection in multivariate regression. J Chemom. 2009;23(1):32-48. doi:10.1002/ cem.1192.
53. Aha D, Bankert RL. A comparative evaluation of sequential feature selection algorithms. In: Fisher D, Lenz H-J, editors. Learning from data. New York, NY: Springer-Verlag; 1996. p. 199-206.
54. Eklund M, Norinder U, Boyer S, et al. Choosing feature selection and learning algorithms in QSAR. J Chem Inf Model. 2014;54(3):837-843. doi:10.1021/ci400573c.
55. Anzanello MJ, Fogliatto FS. A review of recent variable selection methods in industrial and chemometrics applications. Eur J Ind Eng. 2014;8(5):619-645. doi:10.1504/EJIE.2014.065731.
56. Cong Y, Li B, Yang X, et al. Quantitative structure-activity relationship study of influenza virus neuraminidase A/PR/8/34(H1N1) inhibitors by genetic algorithm feature selection and support vector regression. Chemom Intell Lab Syst. 2013;127:35-42. doi:10.1016/j.chemolab.2013.05.012.
57. Vasanthanathan P, Taboureau O, Oostenbrink C, et al. Classification of cytochrome P450 1A2 inhibitors and noninhibitors by machine learning techniques. Drug Metab Dispos. 2009;37(3):658-664. doi:10.1124/dmd.108.023507.
58. Korkmaz S, Zararsiz G, Goksuluk D. Drug/nondrug classification using support vector machines with various feature selection strategies. Comput Methods Programs Biomed. 2014;117(2):51-60 doi:10.1016/j.cmpb.2014.08.009.
59. Smusz S, Kurczab R, Bojarski AJ. The influence of the inactives subset generation on the performance of machine learning methods. J Cheminform. 2013;5(1):17. doi:10.1186/1758-2946-5-17.
60. Kurczab R, Smusz S, Bojarski AJ. The influence of negative training set size on machine learning-based virtual screening. J Cheminform. 2014;6:32. doi:10.1186/1758-2946-6-6.
61. Platt JC. Fast training of support vector machines using sequential minimal optimization. In: Schölkopf B, Burges CJC, Smola AJ, editors. Advances in Kernel methods: support vector learning. Cambridge, MA: MIT Press; 1999. p. 185-208.
62. Roberts NA, Martin JA, Kinchington D, et al. Rational design of peptide-based HIV proteinase inhibitors. Science. 1990;248(4953):358-361. doi:10.1126/science.2183354.
63. Anderson AC. The process of structure-based drug design. Chem Biol. 2003;10(9):787-797. doi:10.1016/j.chembiol.2003.09.002.
64. Berman HM, Westbrook J, Feng Z, et al. The protein data bank. Nucleic Acids Res. 2000;28(1):235-242. doi:10.1093/nar/ 28.1.235.
65. Ferreira L, Dos Santos R, Oliva G, et al. Molecular docking and structurebased drug design strategies. Molecules. 2015;20(7):13384-13421. doi:10.3390/molecules200713384.
66. Sali A, Blundell TL. Comparative protein modelling by satisfaction of spatial restraints. J Mol Biol. 1993;234(3):779-815.
67. Saxena A, Sangwan RS, Mishra S. Fundamentals of homology modeling steps and comparison among important bioinformatics tools: an overview. Sci Int. 2013;1(7):237-252.
68. Sánchez R, Sali A. Large-scale protein structure modeling of the Saccharomyces cerevisiae genome. Proc Natl Acad Sci U S A. 1998;95(23):13597-13602. doi:10.1073/pnas.95.23.13597.
69. Cheng J, Tegge AN, Baldi P. Machine learning methods for protein structure prediction. IEEE Rev Biomed Eng. 2008;1(8):41-49. doi:10.1109/RBME.2008.2008239.
70. Eickholt J, Wang Z. PCP-ML: protein characterization package for machine learning. BMC Res Notes. 2014;7(1):810. doi:10.1186/1756-0500-7-810.
71. Chou PY, Fasman GD. Conformational parameters for amino acids in helical, ?-sheet, and random coil regions calculated from proteins. Biochemistry. 1974;13(2):211-222. doi:10.1021/bi00699a001.
72. Rost B, Sander C. Prediction of protein secondary structure at better than 70% accuracy. J Mol Biol. 1993;232(2):584-599. doi:10.1006/ jmbi.1993.1413.
73. Rost B. PHD: predicting one-dimensional protein structure by profilebased neural networks. Methods Enzymol. 1996;266:525-539.
74. Jones DT. Protein secondary structure prediction based on positionspecific scoring matrices. J Mol Biol. 1999;292(2):195-202. doi:10.1006/jmbi.1999.3091.
75. Petersen TN, Lundegaard C, Nielsen M, et al. Prediction of protein secondary structure at 80% accuracy. Proteins. 2000;41(1):17-20.
76. Shepherd AJ, Gorse D, Thornton JM. Prediction of the location and type of ?-turns in proteins using neural networks. Protein Sci. 1999;8(5):1045-1055. doi:10.1110/(ISSN)1469-896X.
77. Kaur H, Raghava GPS. Prediction of beta-turns in proteins from multiple alignment using neural network. Protein Sci. 2003;12(3):627-634. doi:10.1110/ps.0301903.
78. Kaur H, Raghava GPS. A neural network method for prediction of betaturn types in proteins using evolutionary information. Bioinformatics. 2004;20(16):2751-2758. doi:10.1093/bioinformatics/bth322.
79. Drozdetskiy A, Cole C, Procter J, et al. JPred4: a protein secondary structure prediction server. Nucleic Acids Res. 2015;43(W1):W389-94. doi:10.1093/nar/gkv332.
80. Cuff JA, Barton GJ. Application of multiple sequence alignment profiles to improve protein secondary structure prediction. Proteins. 2000;40(3):502-511. doi:10.1002/(ISSN)1097-0134.
81. Baldi P, Brunak S, Frasconi P, et al. Exploiting the past and the future in protein secondary structure prediction. Bioinformatics. 1999;15(11):937-946. doi:10.1093/bioinformatics/15.11.937.
82. Pollastri G, Przybylski D, Rost B, et al. Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles. Proteins. 2002;47(2):228-235. doi:10.1002/prot.10082.
83. Cheng J, Randall AZ, Sweredoski MJ, et al. SCRATCH: a protein structure and structural feature prediction server. Nucleic Acids Res. 2005;33(Web Server issue):W72-6. doi:10.1093/nar/gki396.
84. Tusnády GE, Simon I. The HMMTOP transmembrane topology prediction server. Bioinformatics. 2001;17(9):849-850. doi:10.1093/bioinformatics/ 17.9.849.
85. Tusnády GE, Simon I. Principles governing amino acid composition of integral membrane proteins: application to topology prediction. J Mol Biol. 1998;283(2):489-506. doi:10.1006/jmbi.1998.2107.
86. Krogh A, Larsson B, Von Heijne G, et al. Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol. 2001;305(3):567-580. doi:10.1006/ jmbi.2000.4339.
87. Bagos PG, Liakopoulos TD, Spyropoulos IC, et al. A hidden Markov model method, capable of predicting and discriminating beta-barrel outer membrane proteins. BMC Bioinformatics. 2004;5:29. doi:10.1186/1471-2105-5-29.
88. Yi TM, Lander ES. Protein secondary structure prediction using nearestneighbor methods. J Mol Biol. 1993;232(4):1117-1129. doi:10.1006/ jmbi.1993.1464.
89. Lin K, Simossis VA, Taylor WR, et al. A simple and fast secondary structure prediction method using hidden neural networks. Bioinformatics. 2005;21(2):152-159. doi:10.1093/bioinformatics/bth487.
90. Remmert M, Biegert A, Hauser A, et al. HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment. Nat Methods. 2012;9(2):173-175. doi:10.1038/nmeth.1818.
91. Söding J. Protein homology detection by HMM-HMM comparison. Bioinformatics. 2005;21(7):951-960. doi:10.1093/bioinformatics/ bti125.
92. Söding J, Biegert A, Lupas AN. The HHpred interactive server for protein homology detection and structure prediction. Nucleic Acids Res. 2005;33(Web Server):W244-8. doi:10.1093/nar/gki408.
93. Qian N, Sejnowski TJ. Predicting the secondary structure of globular proteins using neural network models. J Mol Biol. 1988;202(4):865-884 doi:10.1016/0022-2836(88)90564-5.
94. Przybylski D, Rost B. Alignments grow, secondary structure prediction improves. Proteins. 2002;46(2):197-205. doi:10.1002/(ISSN)1097-0134.
95. Buchan DW, Minneci F, Nugent TCO, et al. Scalable web services for the PSIPRED protein analysis workbench. Nucleic Acids Res. 2013;41(W1):W349-57. doi:10.1093/nar/gkt381.
96. Hua S, Sun Z. A novel method of protein secondary structure prediction with high segment overlap measure: support vector machine approach. J Mol Biol. 2001;308(2):397-407.
97. Hu H, Pan Y, Harrison R, et al. Improved protein secondary structure prediction using support vector machine with a new encoding scheme and an advanced tertiary classifier. IEEE Trans Nanobioscience. 2004;3(4):265-271. doi:10.1109/TNB.2004.837906.
98. Salamov AA, Solovyev VV. Protein secondary structure prediction using local alignments. J Mol Biol. 1997;268(1):31-36. doi:10.1006/ jmbi.1997.0958.
99. Eswar N, Webb B, Marti-Renom MA, et al. Comparative protein structure modeling using MODELLER. Curr Protoc Protein Sci. 2007;50:2.9:2.9.1-2.9.31.
100. Bordner AJ. Predicting small ligand binding sites in proteins using backbone structure. Bioinformatics. 2008;24(24):2865-2871. doi:10.1093/bioinformatics/btn543.
101. Nayal M, Honig B. On the nature of cavities on protein surfaces: application to the identification of drug-binding sites. Proteins. 2006;63(4):892-906. doi:10.1002/prot.20897.
102. Volkamer A, Kuhn D, Rippmann F, et al. DoGSiteScorer: a web server for automatic binding site prediction, analysis and druggability assessment. Bioinformatics. 2012;28(15):2074-2075. doi:10.1093/ bioinformatics/bts310.
103. Krivák R, Hoksza D. Improving protein-ligand binding site prediction accuracy by classification of inner pocket points using local features. J Cheminform. 2015;7(1):12. doi:10.1186/s13321-015-0059-5.
104. Durrant JD, McCammon JA. NNScore: a neural-network-based scoring function for the characterization of protein-ligand complexes. J Chem Inf Model. American Chemical Society. 2010;50(10):1865-1871. doi:10.1021/ci100244v.
105. Durrant JD, McCammon JA. NNScore 2.0: a neural-network receptorligand scoring function. J Chem Inf Model. 2011;51(11):2897-2903. doi:10.1021/ci2003889.
106. Volkamer A, Griewel A, Grombacher T, et al. Analyzing the topology of active sites: on the prediction of pockets and subpockets. J Chem Inf Model. 2010;50(11):2041-2052.
107. Schmidtke P, Barril X. Understanding and predicting druggability. A high-throughput method for detection of drug binding sites. J Med Chem. 2010;53(15):5858-5867. doi:10.1021/jm100574m.
108. Volkamer A, Kuhn D, Grombacher T, et al. Combining global and local measures for structure-based druggability predictions. J Chem Inf Model. 2012;52(2):360-372. doi:10.1021/ci200321u.
109. Le Guilloux V, Schmidtke P, Tuffery P. Fpocket: an open source platform for ligand pocket detection. BMC Bioinformatics. 2009;10(1):168. doi:10.1186/1471-2105-10-168.
110. Capra JA, Laskowski RA, Thornton JM, et al. Predicting protein ligand binding sites by combining evolutionary sequence conservation and 3D structure. PLoS Comput Biol. 2009;5(12):e1000585. doi:10.1371/ journal.pcbi.1000509.
111. Yuriev E, Agostino M, Ramsland PA. Challenges and advances in computational docking: 2009 in review. J Mol Recognit. 2011;24(2):149-164. doi:10.1002/jmr.v24.2.
112. Sousa SF, Fernandes PA, Ramos MJ. Protein-ligand docking: current status and future challenges. Proteins. 2006;65(1):15-26. doi:10.1002/ prot.21130.
113. Brooijmans N, Kuntz ID. Molecular recognition and docking algorithms. Annu Rev Biophys Biomol Struct. 2003;32:335-373. doi:10.1146/ annurev.biophys.32.110601.142532.
114. Maiocchi A. Genetic algorithms in molecular modeling: a review. In: Leardi R, editor. Nature-inspired methods in chemometrics: genetic algorithms and artificial neural networks. 1st ed. Amsterdam: Elsevier Science; 2003. p. 335-373.
115. Gani OABSM. Signposts of docking and scoring in drug design. Chem Biol Drug Des. 2007;70(4):360-365. doi:10.1111/j.1747-0285.2007.00552.x.
116. Lima AN, Philot EA, Perahia D, et al. GANM: A protein-ligand docking approach based on genetic algorithm and normal modes. Appl Math Comput. 2012;219(2):511-520. doi:10.1016/j.amc.2012.06.030.
117. Halperin I, Ma B, Wolfson H, et al. Principles of docking: an overview of search algorithms and a guide to scoring functions. Proteins. 2002;47(4):409-443. doi:10.1002/prot.10115.
118. Smith RD, Dunbar JB, Ung PM-U, et al. CSAR benchmark exercise of 2010: combined evaluation across all submitted scoring functions. J Chem Inf Model. 2011;51(9):2115-2131. doi:10.1021/ci200269q.
119. Rognan D. Proteome-scale docking: myth and reality. Drug Discov Today Technol. 2013;10(3):e403-9. doi:10.1016/j.ddtec.2013.01.003.
120. Moitessier N, Englebienne P, Lee D, et al. Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go. Br J Pharmacol. 2008;153(Suppl):S7-26. doi:10.1038/ bjp.2008.22.
121. Perahia LMD. All-atom normal mode calculations of large molecular systems using iterative methods. In: Bahar QCI, editor. Normal mode analysis: theory and applications to biological and chemical systems. Boca Raton(FL): Chapman and Hall/CRC; 2005. p. 17-40.
122. Floquet N, Marechal J-D, Badet-Denisot M-A, et al. Normal mode analysis as a prerequisite for drug design: application to matrix metalloproteinases inhibitors. FEBS Lett. 2006;580(22):5130-5136. doi:10.1016/j.febslet.2006.08.037.
123. Lexa KW, Carlson HA. Protein flexibility in docking and surface mapping. Q Rev Biophys. 2012;45(3):301-343. doi:10.1017/S00335835 12000066.
124. Da Rocha Pita SS, Fernandes TVA, Caffarena ER, et al. Studies of molecular docking between fibroblast growth factor and heparin using generalized simulated annealing. Int J Quantum Chem. 2008;108(13):2608-2614. doi:10.1002/qua.21731.
125. Shin W-H, Seok C. GalaxyDock: protein-ligand docking with flexible protein side-chains. J Chem Inf Model. 2012;52(12):3225-3232. doi:10.1021/ci200321u.
126. Hui-fang L, Qing S, Jian Z, et al. Evaluation of various inverse docking schemes in multiple targets identification. J Mol Graph Model. 2010;29(3):326-330. doi:10.1016/j.jmgm.2010.09.004.
127. Lee M, Kim D. Large-scale reverse docking profiles and their applications. BMC Bioinformatics. 2012;13 Suppl 1(Suppl 17):S6.
128. Grinter SZ, Liang Y, Huang S-Y, et al. An inverse docking approach for identifying new potential anti-cancer targets. J Mol Graph Model. 2011;29(6):795-799. doi:10.1016/j.jmgm.2011.01.002.
129. Chen YZ, Zhi DG. Ligand-protein inverse docking and its potential use in the computer search of protein targets of a small molecule. Proteins. 2001;43(2):217-226. doi:10.1002/(ISSN)1097-0134.
130. Vasseur R, Baud S, Steffenel LA, et al. Inverse docking method for new proteins targets identification: a parallel approach. Parallel Comput. 2015;42:48-59. doi:10.1016/j.parco.2014.09.008.
131. Hu L, Benson ML, Smith RD, et al. Binding MOAD(Mother of All Databases). Proteins. 2005;60(3):333-340. doi:10.1002/prot.20568.
132. Wang R, Fang X, Lu Y, et al. The PDBbind database: collection of binding affinities for protein-ligand complexes with known threedimensional structures. J Med Chem. 2004;47(12):2977-2980. doi:10.1021/jm030580l.
133. Wang R, Fang X, Lu Y, et al. The PDBbind database: methodologies and updates. J Med Chem. 2005;48(12):4111-4119. doi:10.1021/ jm049494r.
134. 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(2):455-461.
135. Durrant JD, McCammon JA. BINANA: a novel algorithm for ligandbinding characterization. J Mol Graph Model. 2011;29(6):888-893. doi:10.1016/j.jmgm.2011.01.004.
136. Huang N, Shoichet BK, Irwin JJ. Benchmarking sets for molecular docking. J Med Chem. 2006;49(23):6789-6801. doi:10.1021/jm0600592.
137. Durrant JD, Friedman AJ, Rogers KE, et al. Comparing neural-network scoring functions and the state of the art: applications to common library screening. J Chem Inf Model. 2013;53(7):1726-1735. doi:10.1021/ci400042y.
138. Halgren TA, Murphy RB, Friesner RA, et al. Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J Med Chem. 2004;47(7):1750-1759. doi:10.1021/ jm030644s.
139. 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(7):1739-1749. doi:10.1021/ jm0306430.
140. Cortes-Ciriano I, Van Westen GJ, Lenselink EB, et al. Proteochemometric modeling in a Bayesian framework. J Cheminform. 2014;6:35. doi:10.1186/1758-2946-6-6.
141. Lapins M, Eklund M, Spjuth O, et al. Proteochemometric modeling of HIV protease susceptibility. BMC Bioinformatics. 2008 Jan;9:181.
142. Lapins M, Wikberg JES. Proteochemometric modeling of drug resistance over the mutational space for multiple HIV protease variants and multiple protease inhibitors. J Chem Inf Model. 2009;49(5):1202-1210. doi:10.1021/ci800453k.
143. Junaid M, Lapins M, Eklund M, et al. Proteochemometric modeling of the susceptibility of mutated variants of the HIV-1 virus to reverse transcriptase inhibitors. PLoS One. 2010;Jan;5(12):e14353. doi:10.1371/ journal.pone.0014353.
144. Prusis P, Lapins M, Yahorava S, et al. Proteochemometrics analysis of substrate interactions with dengue virus NS3 proteases. Bioorganic Med Chem. 2008;16(20):9369-9377. doi:10.1016/j.bmc. 2008.08.081.
145. Lu-J-J, Pan W, Hu Y-J, et al. Multi-target drugs: the trend of drug research and development. PLoS One. 2012 Jan;7(6):e40262.
146. Korcsmáros T, Szalay MS, Böde C, et al. How to design multi-target drugs. Expert Opin Drug Discov. 2007 Jun;2(6):799-808. doi:10.1517/ 17460441.2.1.115.
147. Abuhammad A, Taha MO. QSAR studies in the discovery of novel type-II diabetic therapies. Expert Opin Drug Discov. 2015;1-18. doi:10.1517/17460441.2016.1118046.
148. Csermely P, Agoston V, Pongor S. The efficiency of multi-target drugs: the network approach might help drug design. Trends Pharmacol Sci. 2005;26(4):178-182. doi:10.1016/j.tips.2005.02.007.
149. Espinoza-Fonseca LM. The benefits of the multi-target approach in drug design and discovery. Bioorganic Med Chem. 2006;14(4):896-897 doi:10.1016/j.bmc.2005.09.011.
150. Costantino L, Barlocco D. Challenges in the design of multitarget drugs against multifactorial pathologies: a new life for medicinal chemistry? Future Med Chem. 2013;5(1):5-7. doi:10.4155/fmc.12.193.
151. Heikamp K, Bajorath J. Prediction of compounds with closely related activity profiles using weighted support vector machine linear combinations. J Chem Inf Model. 2013;53(4):791-801. doi:10.1021/ ci400090t.
152. Ajmani S, Kulkarni SA. Application of GQSAR for scaffold hopping and lead optimization in multitarget inhibitors. Mol Inform. 2012;31(6-7):473-490. doi:10.1002/minf.201100160.
153. Schweikert G, Widmer C, Scholkopf B RG. An empirical analysis of domain adaptation algorithms for genomic sequence analysis. In: Koller D, Schuurmans D, Bengio Y, Bottou L, editors. Advances in neural information processing systems 21: Proceedings of the 2008 Conference. La Jolla: NIPS Foundation; 2009. p. 1433-1440.
154. Wang T, Wu M-B, Lin J-P, et al. Quantitative structure-activity relationship: promising advances in drug discovery platforms. Expert Opin Drug Discov. 2015;10(12):1283-1300. doi:10.1517/17460441.2015.1083006.
155. Zanni R, Gálvez-Llompart M, Gálvez J, et al. QSAR multi-target in drug discovery: a review. Curr Comput Aided Drug Des. 2014;10(2):129-136. doi:10.2174/157340991002140708105124.
156. Liu Q, Zhou H, Liu L, et al. Multi-target QSAR modelling in the analysis and design of HIV-HCV co-inhibitors: an in-silico study. BMC Bioinformatics. 2011;12:294. doi:10.1186/1471-2105-12-294.
157. Ma XH, Shi Z, Tan C, et al. In-silico approaches to multi-target drug discovery : computer aided multi-target drug design, multi-target virtual screening. Pharm Res. 2010;27(5):739-749. doi:10.1007/ s11095-010-0065-2.
158. Castillo-Garit JA, Abad C, Rodríguez-Borges JE, et al. A review of QSAR studies to discover new drug-like compounds actives against leishmaniasis and trypanosomiasis. Curr Top Med Chem. 2012;12(8):852-865. doi:10.2174/156802612800166756.