1932

Abstract

Due to the massive data sets available for drug candidates, modern drug discovery has advanced to the big data era. Central to this shift is the development of artificial intelligence approaches to implementing innovative modeling based on the dynamic, heterogeneous, and large nature of drug data sets. As a result, recently developed artificial intelligence approaches such as deep learning and relevant modeling studies provide new solutions to efficacy and safety evaluations of drug candidates based on big data modeling and analysis. The resulting models provided deep insights into the continuum from chemical structure to in vitro, in vivo, and clinical outcomes. The relevant novel data mining, curation, and management techniques provided critical support to recent modeling studies. In summary, the new advancement of artificial intelligence in the big data era has paved the road to future rational drug development and optimization, which will have a significant impact on drug discovery procedures and, eventually, public health.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-pharmtox-010919-023324
2020-01-06
2024-03-28
Loading full text...

Full text loading...

/deliver/fulltext/pharmtox/60/1/annurev-pharmtox-010919-023324.html?itemId=/content/journals/10.1146/annurev-pharmtox-010919-023324&mimeType=html&fmt=ahah

Literature Cited

  1. 1. 
    Waring MJ, Arrowsmith J, Leach AR, Leeson PD, Mandrell S et al. 2015. An analysis of the attrition of drug candidates from four major pharmaceutical companies. Nat. Rev. Drug Discov. 14:475–86
    [Google Scholar]
  2. 2. 
    Fleming N. 2018. How artificial intelligence is changing drug discovery. Nature 557:S55–57
    [Google Scholar]
  3. 3. 
    Zhang L, Tan J, Han D, Zhu H 2017. From machine learning to deep learning: progress in machine intelligence for rational drug discovery. Drug Discov. Today 22:111680–85
    [Google Scholar]
  4. 4. 
    Gawehn E, Hiss JA, Schneider G 2016. Deep learning in drug discovery. Mol. Inform. 35:3–14
    [Google Scholar]
  5. 5. 
    Beresford AP, Segall M, Tarbit MH 2004. In silico prediction of ADME properties: Are we making progress?. Curr. Opin. Drug Discov. Dev. 7:36–42
    [Google Scholar]
  6. 6. 
    Hughes JP, Rees S, Kalindjian SB, Philpott KL 2011. Principles of early drug discovery. Br. J. Pharmacol. 162:1239–49
    [Google Scholar]
  7. 7. 
    Collins FS, Gray GM, Bucher JR 2008. Transforming environmental health protection. Science 319:906–7
    [Google Scholar]
  8. 8. 
    Cherkasov A, Muratov EN, Fourches D, Varnek A, Baskin II et al. 2014. QSAR modeling: Where have you been? Where are you going to?. J. Med. Chem 57:4977–5010
    [Google Scholar]
  9. 9. 
    Golbraikh A, Wang X, Zhu H, Tropsha A 2016. Predictive QSAR modeling: methods and applications in drug discovery and chemical risk assessment. Handbook of Computational Chemistry J Leszczynski, A Kaczmarek-Kedziera, T Puzyn, MG Papadopoulos, H Reis, MK Shukla 2303–40 Dordrecht: Neth.: Springer
    [Google Scholar]
  10. 10. 
    Zhu H, Bouhifd M, Donley E, Egnash L, Kleinstreuer N et al. 2016. Supporting read-across using biological data. ALTEX 33:167–82
    [Google Scholar]
  11. 11. 
    Roy PP, Leonard JT, Roy K 2008. Exploring the impact of size of training sets for the development of predictive QSAR models. Chemometr. Intell. Lab. 90:31–42
    [Google Scholar]
  12. 12. 
    Zhao L, Wang W, Sedykh A, Zhu H 2017. Experimental errors in QSAR modeling sets: What we can do and what we cannot do. ACS Omega 2:2805–12
    [Google Scholar]
  13. 13. 
    Fourches D, Muratov E, Tropsha A 2010. Trust, but verify: on the importance of chemical structure curation in cheminformatics and QSAR modeling research. J. Chem. Inf. Model. 50:71189–204
    [Google Scholar]
  14. 14. 
    Tropsha A. 2010. Best practices for QSAR model development, validation, and exploitation. Mol. Informat. 29:476–88
    [Google Scholar]
  15. 15. 
    Stouch TR, Kenyon JR, Johnson SR, Chen XQ, Doweyko A, Li Y 2003. In silico ADME/Tox: why models fail. J. Comput.-Aided Mol. Des. 17:83–92
    [Google Scholar]
  16. 16. 
    Maggiora GM. 2006. On outliers and activity cliffs—why QSAR often disappoints. J. Chem. Inf. Model. 46:1535
    [Google Scholar]
  17. 17. 
    Tetko IV, Sushko I, Pandey AK, Zhu H, Tropsha A et al. 2008. Critical assessment of QSAR models of environmental toxicity against Tetrahymenapyriformis: focusing on applicability domain and overfitting by variable selection. J. Chem. Inf. Model. 48:1733–46
    [Google Scholar]
  18. 18. 
    Tetko IV. 1995. Neural network studies. 1. Comparison of overfitting and overtraining. J. Chem. Inf. Comput. Sci. 35:826–33
    [Google Scholar]
  19. 19. 
    Wang WY, Kim MT, Sedykh A, Zhu H 2015. Developing enhanced blood-brain barrier permeability models: integrating external bio-assay data in QSAR modeling. Pharm. Res. 32:3055–65
    [Google Scholar]
  20. 20. 
    Kim MT, Sedykh A, Chakravarti SK, Saiakhov RD, Zhu H 2014. Critical evaluation of human oral bioavailability for pharmaceutical drugs by using various cheminformatics approaches. Pharm. Res. 31:1002–14
    [Google Scholar]
  21. 21. 
    Zhang J, Hsieh JH, Zhu H 2014. Profiling animal toxicants by automatically mining public bioassay data: a big data approach for computational toxicology. PLOS ONE 9:e99863
    [Google Scholar]
  22. 22. 
    Liu R, Li X, Lam KS 2017. Combinatorial chemistry in drug discovery. Curr. Opin. Chem. Biol. 38:117–26
    [Google Scholar]
  23. 23. 
    Kennedy JP, Williams L, Bridges TM, Daniels RN, Weaver D, Lindsley CW 2008. Application of combinatorial chemistry science on modern drug discovery. J. Comb. Chem. 10:345–54
    [Google Scholar]
  24. 24. 
    Inglese J, Auld DS, Jadhav A, Johnson RL, Simeonov A et al. 2006. Quantitative high-throughput screening: a titration-based approach that efficiently identifies biological activities in large chemical libraries. PNAS 103:11473–78
    [Google Scholar]
  25. 25. 
    Malo N, Hanley JA, Cerquozzi S, Pelletier J, Nadon R 2006. Statistical practice in high-throughput screening data analysis. Nat. Biotechnol. 24:167–75
    [Google Scholar]
  26. 26. 
    Zhu H, Xia M. 2016. High-Throughput Screening Assays in Toxicology New York: Springer
  27. 27. 
    Zhu H, Zhang J, Kim MT, Boison A, Sedykh A, Moran K 2014. Big data in chemical toxicity research: the use of high-throughput screening assays to identify potential toxicants. Chem. Res. Toxicol. 27:1643–51
    [Google Scholar]
  28. 28. 
    Broach JR, Thorner J. 1996. High-throughput screening for drug discovery. Nature 384:14–16
    [Google Scholar]
  29. 29. 
    Klekota J, Brauner E, Roth FP, Schreiber SL 2006. Using high-throughput screening data to discriminate compounds with single-target effects from those with side effects. J. Chem. Inform. Model. 46:1549–62
    [Google Scholar]
  30. 30. 
    Macarron R, Banks MN, Bojanic D, Burns DJ, Cirovic DA et al. 2011. Impact of high-throughput screening in biomedical research. Nat. Rev. Drug Discov. 10:188–95
    [Google Scholar]
  31. 31. 
    Ciallella HL, Zhu H. 2019. Advancing computational toxicology in the big data era by artificial intelligence: data-driven and mechanism-driven modeling for chemical toxicity. Chem. Res. Toxicol. 32:4536–47
    [Google Scholar]
  32. 32. 
    Lee CH, Yoon HJ. 2017. Medical big data: promise and challenges. Kidney Res. Clin. Pract. 36:3–11
    [Google Scholar]
  33. 33. 
    Santos R, Ursu O, Gaulton A, Bento AP, Donadi RS et al. 2017. A comprehensive map of molecular drug targets. Nat. Rev. Drug Discov. 16:19–34
    [Google Scholar]
  34. 34. 
    Scheeder C, Heigwer F, Boutros M 2018. Machine learning and image-based profiling in drug discovery. Curr. Opin. Syst. Biol. 10:43–52
    [Google Scholar]
  35. 35. 
    Hu Y, Bajorath J. 2013. Compound promiscuity: What can we learn from current data. ? Drug Discov. Today 18:644–50
    [Google Scholar]
  36. 36. 
    Chatzidakis M, Botton GA. 2019. Towards calibration-invariant spectroscopy using deep learning. Sci. Rep. 9:2126
    [Google Scholar]
  37. 37. 
    Jing Y, Bian Y, Hu Z, Wang L, Xie XQ 2018. Deep learning for drug design: an artificial intelligence paradigm for drug discovery in the big data era. AAPS J 20:58
    [Google Scholar]
  38. 38. 
    Ma J, Sheridan RP, Liaw A, Dahl GE, Svetnik V 2015. Deep neural nets as a method for quantitative structure-activity relationships. J. Chem. Inf. Model. 55:263–74
    [Google Scholar]
  39. 39. 
    Zhang Z, Beck MW, Winkler DA, Huang B, Sibanda W et al. 2018. Opening the black box of neural networks: methods for interpreting neural network models in clinical applications. Ann. Transl. Med. 6:216
    [Google Scholar]
  40. 40. 
    Dayhoff JE, DeLeo JM. 2001. Artificial neural networks: opening the black box. Cancer 91:1615–35
    [Google Scholar]
  41. 41. 
    Schadt EE, Linderman MD, Sorenson J, Lee L, Nolan GP 2011. Cloud and heterogeneous computing solutions exist today for the emerging big data problems in biology. Nat. Rev. Genet. 12:224
    [Google Scholar]
  42. 42. 
    Marx V. 2013. Biology: the big challenges of big data. Nature 498:255–60
    [Google Scholar]
  43. 43. 
    Swarup V, Geschwind DH. 2013. Alzheimer's disease: from big data to mechanism. Nature 500:34–35
    [Google Scholar]
  44. 44. 
    Sayers EW, Barrett T, Benson DA, Bolton E, Bryant SH et al. 2010. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res 38:D5–16
    [Google Scholar]
  45. 45. 
    Wang Y, Xiao J, Suzek TO, Zhang J, Wang J, Bryant SH 2009. PubChem: a public information system for analyzing bioactivities of small molecules. Nucleic Acids Res 37:W623–33
    [Google Scholar]
  46. 46. 
    Sayers EW, Barrett T, Benson DA, Bryant SH, Canese K et al. 2009. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res 37:D5–15
    [Google Scholar]
  47. 47. 
    Sayers EW, Agarwala R, Bolton EE, Brister JR, Canese K et al. 2019. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res 47:D23–28
    [Google Scholar]
  48. 48. 
    Gaulton A, Bellis LJ, Bento AP, Chambers J, Davies M et al. 2012. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res 40:D1100–7
    [Google Scholar]
  49. 49. 
    Wishart DS, Feunang YD, Guo AC, Lo EJ, Marcu A et al. 2018. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res 46:D1074–82
    [Google Scholar]
  50. 50. 
    Gilson MK, Liu T, Baitaluk M, Nicola G, Hwang L, Chong J 2016. BindingDB in 2015: a public database for medicinal chemistry, computational chemistry and systems pharmacology. Nucleic Acids Res 44:D1045–53
    [Google Scholar]
  51. 51. 
    Armbrust M, Fox A, Griffith R, Joseph AD, Katz R et al. 2010. A view of cloud computing. Commun. ACM 53:50–58
    [Google Scholar]
  52. 52. 
    Nickolls J, Dally WJ. 2010. The GPU computing era. IEEE Micro 30:56–69
    [Google Scholar]
  53. 53. 
    Russo DP, Kim MT, Wang W, Pinolini D, Shende S et al. 2017. CIIPro: a new read-across portal to fill data gaps using public large-scale chemical and biological data. Bioinformatics 33:464–66
    [Google Scholar]
  54. 54. 
    Russo DP, Strickland J, Karmaus AL, Wang W, Shende S et al. 2019. Nonanimal models for acute toxicity evaluations: applying data-driven profiling and read-across. Environ. Health Perspect. 127:447001
    [Google Scholar]
  55. 55. 
    Paolini GV, Shapland RHB, van Hoorn WP, Mason JS, Hopkins AL 2006. Global mapping of pharmacological space. Nat. Biotechnol. 24:805–15
    [Google Scholar]
  56. 56. 
    Mirza B, Wang W, Wang J, Choi H, Chung NC, Ping PP 2019. Machine learning and integrative analysis of biomedical big data. Genes 10:287
    [Google Scholar]
  57. 57. 
    Kim MT, Huang R, Sedykh A, Wang W, Xia M, Zhu H 2016. Mechanism profiling of hepatotoxicity caused by oxidative stress using antioxidant response element reporter gene assay models and big data. Environ. Health Perspect. 124:634–41
    [Google Scholar]
  58. 58. 
    Pedersen AB, Mikkelsen EM, Cronin-Fenton D, Kristensen NR, Pham TM et al. 2017. Missing data and multiple imputation in clinical epidemiological research. Clin. Epidemiol. 9:157–65
    [Google Scholar]
  59. 59. 
    Sterne JAC, White IR, Carlin JB, Spratt M, Royston P et al. 2009. Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ 338:b2393
    [Google Scholar]
  60. 60. 
    Vadivelan S, Sinha BN, Rambabu G, Boppana K, Jagarlapudi SA 2008. Pharmacophore modeling and virtual screening studies to design some potential histone deacetylase inhibitors as new leads. J. Mol. Graph. Model. 26:935–46
    [Google Scholar]
  61. 61. 
    Marriott DP, Dougall IG, Meghani P, Liu YJ, Flower DR 1999. Lead generation using pharmacophore mapping and three-dimensional database searching: application to muscarinic M-3 receptor antagonists. J. Med. Chem. 42:3210–16
    [Google Scholar]
  62. 62. 
    Gussio R, Pattabiraman N, Kellogg GE, Zaharevitz DW 1998. Use of 3D QSAR methodology for data mining the National Cancer Institute Repository of Small Molecules: application to HIV-1 reverse transcriptase inhibition. Methods 14:255–63
    [Google Scholar]
  63. 63. 
    Zhang L, Fourches D, Sedykh A, Zhu H, Golbraikh A et al. 2013. Discovery of novel antimalarial compounds enabled by QSAR-based virtual screening. J. Chem. Inf. Model. 53:475–92
    [Google Scholar]
  64. 64. 
    Ribay K, Kim MT, Wang W, Pinolini D, Zhu H 2016. Hybrid modeling of estrogen receptor binding agents using advanced cheminformatics tools and massive public data. Front. Environ. Sci. 4:12
    [Google Scholar]
  65. 65. 
    Bharti DR, Hemrom AJ, Lynn AM 2019. GCAC: galaxy workflow system for predictive model building for virtual screening. BMC Bioinform 19:Suppl. 13550
    [Google Scholar]
  66. 66. 
    Russell SJ, Norvig P. 2003. Artificial Intelligence: A Modern Approach Upper Saddle River, NJ: Prentice Hall/Pearson Ed.
  67. 67. 
    Hansch C, Fujita T. 1964. ρ-σ-π Analysis. A method for the correlation of biological activity and chemical structure. J. Am. Chem. Soc. 86:1616–26
    [Google Scholar]
  68. 68. 
    Martin YC. 2010. Quantitative Drug Design: A Critical Introduction Boca Raton, FL: CRC Press. , 2nd ed..
  69. 69. 
    Zefirov NS, Palyulin VA. 2002. Fragmental approach in QSPR. J. Chem. Inform. Comput. Sci. 42:1112–22
    [Google Scholar]
  70. 70. 
    Labute P. 2000. A widely applicable set of descriptors. J. Mol. Graph. Model. 18:464–77
    [Google Scholar]
  71. 71. 
    Gozalbes R, Doucet JP, Derouin F 2002. Application of topological descriptors in QSAR and drug design: history and new trends. Curr. Drug Targets Infect. Disord. 2:93–102
    [Google Scholar]
  72. 72. 
    Willett P. 2006. Similarity-based virtual screening using 2D fingerprints. Drug Discov. Today 11:1046–53
    [Google Scholar]
  73. 73. 
    McGregor MJ, Muskal SM. 1999. Pharmacophore fingerprinting. 1. Application to QSAR and focused library design. J. Chem. Inf. Comput. Sci. 39:569–74
    [Google Scholar]
  74. 74. 
    Leardi R, Boggia R, Terrile M 1992. Genetic algorithms as a strategy for feature-selection. J. Chemomet. 6:267–81
    [Google Scholar]
  75. 75. 
    Sheridan RP, SanFeliciano SG, Kearsley SK 2000. Designing targeted libraries with genetic algorithms. J. Mol. Graph. Model. 18:320–34
    [Google Scholar]
  76. 76. 
    Sun L, Xie Y, Song X, Wang J, Yu R 1994. Cluster analysis by simulated annealing. Comp. Chem. 18:103–8
    [Google Scholar]
  77. 77. 
    Zheng W, Tropsha A. 2000. Novel variable selection quantitative structure–property relationship approach based on the k-nearest-neighbor principle. J. Chem. Inf. Comput. Sci. 40:185–94
    [Google Scholar]
  78. 78. 
    Burbidge R, Trotter M, Buxton B, Holden S 2001. Drug design by machine learning: support vector machines for pharmaceutical data analysis. Comput. Chem. 26:5–14
    [Google Scholar]
  79. 79. 
    Sprague B, Shi Q, Kim MT, Zhang L, Sedykh A et al. 2014. Design, synthesis and experimental validation of novel potential chemopreventive agents using random forest and support vector machine binary classifiers. J. Comput.-Aided Mol. Des. 28:631–46
    [Google Scholar]
  80. 80. 
    Breiman L. 2001. Random forests. Mach. Learn. 45:5–32
    [Google Scholar]
  81. 81. 
    Golbraikh A, Tropsha A. 2002. Beware of q2!. J. Mol. Graph. Model. 20:269–76
    [Google Scholar]
  82. 82. 
    Solimeo R, Zhang J, Kim M, Sedykh A, Zhu H 2012. Predicting chemical ocular toxicity using a combinatorial QSAR approach. Chem. Res. Toxicol. 25:2763–69
    [Google Scholar]
  83. 83. 
    Gramatica P. 2007. Principles of QSAR models validation: internal and external. QSAR Comb. Sci. 26:694–701
    [Google Scholar]
  84. 84. 
    Zhu H, Martin TM, Ye L, Sedykh A, Young DM, Tropsha A 2009. Quantitative structure-activity relationship modeling of rat acute toxicity by oral exposure. Chem. Res. Toxicol. 22:1913–21
    [Google Scholar]
  85. 85. 
    Zhu H, Tropsha A, Fourches D, Varnek A, Papa E et al. 2008. Combinatorial QSAR modeling of chemical toxicants tested against Tetrahymenapyriformis. J. Chem. Inf. Model 48:766–84
    [Google Scholar]
  86. 86. 
    Tropsha A, Golbraikh A. 2007. Predictive QSAR modeling workflow, model applicability domains, and virtual screening. Curr. Pharm. Des. 13:3494–504
    [Google Scholar]
  87. 87. 
    Ekins S, Boulanger B, Swaan PW, Hupcey MA 2002. Towards a new age of virtual ADME/TOX and multidimensional drug discovery. Mol. Divers. 5:255–75
    [Google Scholar]
  88. 88. 
    Muster W, Breidenbach A, Fischer H, Kirchner S, Muller L, Pahler A 2008. Computational toxicology in drug development. Drug Discov. Today 13:303–10
    [Google Scholar]
  89. 89. 
    Khedkar SA, Malde AK, Coutinho EC, Srivastava S 2007. Pharmacophore modeling in drug discovery and development: an overview. Med. Chem. 3:187–97
    [Google Scholar]
  90. 90. 
    Duch W, Swaminathan K, Meller J 2007. Artificial intelligence approaches for rational drug design and discovery. Curr. Pharm. Des. 13:1497–508
    [Google Scholar]
  91. 91. 
    Hecht D. 2011. Applications of machine learning and computational intelligence to drug discovery and development. Drug Dev. Res. 72:53–65
    [Google Scholar]
  92. 92. 
    Hopfield JJ. 1982. Neural networks and physical systems with emergent collective computational abilities. PNAS 79:2554–58
    [Google Scholar]
  93. 93. 
    Aoyama T, Suzuki Y, Ichikawa H 1989. Neural networks applied to pharmaceutical problems. 1. Method and application to decision-making. Chem. Pharm. Bull. 37:2558–60
    [Google Scholar]
  94. 94. 
    Baskin II, Winkler D, Tetko IV 2016. A renaissance of neural networks in drug discovery. Expert Opin. Drug Dis. 11:785–95
    [Google Scholar]
  95. 95. 
    Tetko IV, Villa AE, Aksenova TI, Zielinski WL, Brower J et al. 1998. Application of a pruning algorithm to optimize artificial neural networks for pharmaceutical fingerprinting. J. Chem. Inf. Comput. Sci. 38:660–68
    [Google Scholar]
  96. 96. 
    Tetko IV, Tanchuk VY, Chentsova NP, Antonenko SV, Poda GI et al. 1994. HIV-1 reverse transcriptase inhibitor design using artificial neural networks. J. Med. Chem. 37:2520–26
    [Google Scholar]
  97. 97. 
    Tetko IV, Villa AE, Livingstone DJ 1996. Neural network studies. 2. Variable selection. J. Chem. Inf. Comput. Sci. 36:794–803
    [Google Scholar]
  98. 98. 
    Agatonovic-Kustrin S, Beresford R. 2000. Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. J. Pharm. Biomed. Anal. 22:717–27
    [Google Scholar]
  99. 99. 
    Roy K, Roy PP. 2009. Comparative chemometric modeling of cytochrome 3A4 inhibitory activity of structurally diverse compounds using stepwise MLR, FA-MLR, PLS, GFA, G/PLS and ANN techniques. Eur. J. Med. Chem. 44:2913–22
    [Google Scholar]
  100. 100. 
    Simmons K, Kinney J, Owens A, Kleier D, Bloch K et al. 2008. Comparative study of machine-learning and chemometric tools for analysis of in-vivo high-throughput screening data. J. Chem. Inform. Model. 48:1663–68
    [Google Scholar]
  101. 101. 
    Hinton G, Deng L, Yu D, Dahl GE, Mohamed AR et al. 2012. Deep neural networks for acoustic modeling in speech recognition. IEEE Signal. Proc. Mag. 29:82–97
    [Google Scholar]
  102. 102. 
    Silver D, Huang A, Maddison CJ, Guez A, Sifre L et al. 2016. Mastering the game of Go with deep neural networks and tree search. Nature 529:484–89
    [Google Scholar]
  103. 103. 
    LeCun Y, Bengio Y, Hinton G 2015. Deep learning. Nature 521:436–44
    [Google Scholar]
  104. 104. 
    Schadt EE, Linderman MD, Sorenson J, Lee L, Nolan GP 2010. Computational solutions to large-scale data management and analysis. Nat. Rev. Genet. 11:647–57
    [Google Scholar]
  105. 105. 
    Xie L, Draizen EJ, Bourne PE 2017. Harnessing big data for systems pharmacology. Annu. Rev. Pharmacol. 57:245–62
    [Google Scholar]
  106. 106. 
    Huang RL, Xia MH, Nguyen DT, Zhao TG, Sakamuru S et al. 2016. Tox21 challenge to build predictive models of nuclear receptor and stress response pathways as mediated by exposure to environmental chemicals and drugs. Front. Env. Sci. 3:85
    [Google Scholar]
  107. 107. 
    Mayr A, Klambauer G, Unterthiner T, Hochreiter S 2016. DeepTox: toxicity prediction using deep learning. Front. Environ. Sci. 3:80
    [Google Scholar]
  108. 108. 
    Wen M, Zhang Z, Niu S, Sha H, Yang R et al. 2017. Deep-learning-based drug-target interaction prediction. J. Proteome Res. 16:1401–9
    [Google Scholar]
  109. 109. 
    Xie L, He S, Song X, Bo X, Zhang Z 2018. Deep learning-based transcriptome data classification for drug-target interaction prediction. BMC Genom 19:667
    [Google Scholar]
  110. 110. 
    Xu Y, Pei J, Lai L 2017. Deep learning based regression and multiclass models for acute oral toxicity prediction with automatic chemical feature extraction. J. Chem. Inf. Model. 57:2672–85
    [Google Scholar]
  111. 111. 
    Cai C, Guo P, Zhou Y, Zhou J, Wang Q et al. 2019. Deep learning-based prediction of drug-induced cardiotoxicity. J. Chem. Inf. Model. 59:31073–84
    [Google Scholar]
  112. 112. 
    Wenzel J, Matter H, Schmidt F 2019. Predictive multitask deep neural network models for ADME-Tox properties: learning from large data sets. J. Chem. Inf. Model. 59:1253–68
    [Google Scholar]
  113. 113. 
    Li X, Xu YJ, Lai LH, Pei JF 2018. Prediction of human cytochrome P450 inhibition using a multitask deep autoencoder neural network. Mol. Pharm. 15:4336–45
    [Google Scholar]
  114. 114. 
    Russo DP, Zorn KM, Clark AM, Zhu H, Ekins S 2018. Comparing multiple machine learning algorithms and metrics for estrogen receptor binding prediction. Mol. Pharm. 15:4361–70
    [Google Scholar]
  115. 115. 
    Zhou Y, Cahya S, Combs SA, Nicolaou CA, Wang J et al. 2019. Exploring tunable hyperparameters for deep neural networks with industrial ADME data sets. J. Chem. Inf. Model. 59:1005–16
    [Google Scholar]
  116. 116. 
    Minko T, Rodriguez-Rodriguez L, Pozharov V 2013. Nanotechnology approaches for personalized treatment of multidrug resistant cancers. Adv. Drug Deliv. Rev. 65:1880–95
    [Google Scholar]
  117. 117. 
    Smith TT, Stephan SB, Moffett HF, McKnight LE, Ji WH et al. 2017. In situ programming of leukaemia-specific T cells using synthetic DNA nanocarriers. Nat. Nanotechnol. 12:813–20
    [Google Scholar]
  118. 118. 
    Liu JZ, Hopfinger AJ. 2008. Identification of possible sources of nanotoxicity from carbon nanotubes inserted into membrane bilayers using membrane interaction quantitative structure-activity relationship analysis. Chem. Res. Toxicol. 21:459–66
    [Google Scholar]
  119. 119. 
    Liu J, Yang L, Hopfinger AJ 2009. Affinity of drugs and small biologically active molecules to carbon nanotubes: a pharmacodynamics and nanotoxicity factor?. Mol. Pharm. 6:873–82
    [Google Scholar]
  120. 120. 
    Shaw SY, Westly EC, Pittet MJ, Subramanian A, Schreiber SL, Weissleder R 2008. Perturbational profiling of nanomaterial biologic activity. PNAS 105:7387–92
    [Google Scholar]
  121. 121. 
    Liu W, Wu Y, Wang C, Li HC, Wang T et al. 2010. Impact of silver nanoparticles on human cells: effect of particle size. Nanotoxicology 4:319–30
    [Google Scholar]
  122. 122. 
    Fourches D, Pu D, Tassa C, Weissleder R, Shaw SY et al. 2010. Quantitative nanostructure-activity relationship modeling. ACS Nano 4:5703–12
    [Google Scholar]
  123. 123. 
    Liu R, Rallo R, George S, Ji ZX, Nair S et al. 2011. Classification NanoSAR development for cytotoxicity of metal oxide nanoparticles. Small 7:1118–26
    [Google Scholar]
  124. 124. 
    Epa VC, Burden FR, Tassa C, Weissleder R, Shaw S, Winkler DA 2012. Modeling biological activities of nanoparticles. Nano Lett 12:5808–12
    [Google Scholar]
  125. 125. 
    Chen R, Zhang Y, Monteiro-Riviere NA, Riviere JE 2016. Quantification of nanoparticle pesticide adsorption: computational approaches based on experimental data. Nanotoxicology 10:1118–28
    [Google Scholar]
  126. 126. 
    Pathakoti K, Huang MJ, Watts JD, He X, Hwang HM 2014. Using experimental data of Escherichia coli to develop a QSAR model for predicting the photo-induced cytotoxicity of metal oxide nanoparticles. J. Photochem. Photobiol. B 130:234–40
    [Google Scholar]
  127. 127. 
    Puzyn T, Leszczynska D, Leszczynski J 2009. Toward the development of “nano-QSARs”: advances and challenges. Small 5:2494–509
    [Google Scholar]
  128. 128. 
    Li S, Zhai S, Liu Y, Zhou H, Wu J et al. 2015. Experimental modulation and computational model of nano-hydrophobicity. Biomaterials 52:312–17
    [Google Scholar]
  129. 129. 
    Wang WY, Yan XL, Zhao LL, Russo DP, Wang SQ et al. 2019. Universal nanohydrophobicity predictions using virtual nanoparticle library. J. Cheminform. 11:6
    [Google Scholar]
  130. 130. 
    Wang W, Sedykh A, Sun H, Zhao L, Russo DP et al. 2017. Predicting nano–bio interactions by integrating nanoparticle libraries and quantitative nanostructure activity relationship modeling. ACS Nano 11:12641–49
    [Google Scholar]
  131. 131. 
    Mnih V, Kavukcuoglu K, Silver D, Rusu AA, Veness J et al. 2015. Human-level control through deep reinforcement learning. Nature 518:529–33
    [Google Scholar]
  132. 132. 
    Chougrad H, Zouaki H, Alheyane O 2018. Deep convolutional neural networks for breast cancer screening. Comput. Meth. Prog. Biomed. 157:19–30
    [Google Scholar]
  133. 133. 
    Lin WM, Tong T, Gao QQ, Guo D, Du XF et al. 2018. Convolutional neural networks-based MRI image analysis for the Alzheimer's disease prediction from mild cognitive impairment. Front. Neurosci. 12:777
    [Google Scholar]
  134. 134. 
    Nirschl JJ, Janowczyk A, Peyster EG, Frank R, Margulies KB et al. 2018. A deep-learning classifier identifies patients with clinical heart failure using whole-slide images of H&E tissue. PLOS ONE 13:e0192726
    [Google Scholar]
  135. 135. 
    Hofmarcher M, Rumetshofer E, Clevert DA, Hochreiter S, Klambauer G 2019. Accurate prediction of biological assays with high-throughput microscopy images and convolutional networks. J. Chem. Inf. Model. 59:1163–71
    [Google Scholar]
  136. 136. 
    Stepniewska-Dziubinska MM, Zielenkiewicz P, Siedlecki P 2018. Development and evaluation of a deep learning model for protein-ligand binding affinity prediction. Bioinformatics 34:3666–74
    [Google Scholar]
  137. 137. 
    Ragoza M, Hochuli J, Idrobo E, Sunseri J, Koes DR 2017. Protein-ligand scoring with convolutional neural networks. J. Chem. Inf. Model. 57:942–57
    [Google Scholar]
  138. 138. 
    Altae-Tran H, Ramsundar B, Pappu AS, Pande V 2017. Low data drug discovery with one-shot learning. ACS Cent. Sci. 3:283–93
    [Google Scholar]
  139. 139. 
    Mobadersany P, Yousefi S, Amgad M, Gutman DA, Barnholtz-Sloan JS et al. 2018. Predicting cancer outcomes from histology and genomics using convolutional networks. PNAS 115:E2970–79
    [Google Scholar]
  140. 140. 
    Zhao ZH, Yang ZH, Luo L, Lin HF, Wang J 2016. Drug drug interaction extraction from biomedical literature using syntax convolutional neural network. Bioinformatics 32:3444–53
    [Google Scholar]
  141. 141. 
    Xie L, Ge XX, Tan HP, Xie L, Zhang YL et al. 2014. Towards structural systems pharmacology to study complex diseases and personalized medicine. PLOS Comput. Biol. 10:e1003554
    [Google Scholar]
  142. 142. 
    Hamburg MA, Collins FS. 2010. The path to personalized medicine. N. Engl. J. Med. 363:301–4
    [Google Scholar]
  143. 143. 
    Collins FS, Morgan M, Patrinos A 2003. The Human Genome Project: lessons from large-scale biology. Science 300:286–90
    [Google Scholar]
  144. 144. 
    Sydow D, Burggraaff L, Szengel A, van Vlijmen HWT, IJzerman AP et al. 2019. Advances and challenges in computational target prediction. J. Chem. Inf. Model. 59:1728–42
    [Google Scholar]
  145. 145. 
    Chang RL, Xie L, Xie L, Bourne PE, Palsson BO 2010. Drug off-target effects predicted using structural analysis in the context of a metabolic network model. PLOS Comput. Biol. 6:e1000938
    [Google Scholar]
  146. 146. 
    Schrider DR, Kern AD. 2018. Supervised machine learning for population genetics: a new paradigm. Trends Genet 34:301–12
    [Google Scholar]
  147. 147. 
    Collins FS, Varmus H. 2015. A new initiative on precision medicine. N. Engl. J. Med. 372:793–95
    [Google Scholar]
  148. 148. 
    Grossman RL, Heath AP, Ferretti V, Varmus HE, Lowy DR et al. 2016. Toward a shared vision for cancer genomic data. N. Engl. J. Med. 375:1109–12
    [Google Scholar]
  149. 149. 
    Marti-Renom MA, Stuart AC, Fiser A, Sanchez R, Melo F, Sali A 2000. Comparative protein structure modeling of genes and genomes. Annu. Rev. Biophys. Biomol. Struct. 29:291–325
    [Google Scholar]
  150. 150. 
    Vinga S, Almeida J. 2003. Alignment-free sequence comparison—a review. Bioinformatics 19:513–23
    [Google Scholar]
  151. 151. 
    Lippmann C, Kringel D, Ultsch A, Lotsch J 2018. Computational functional genomics-based approaches in analgesic drug discovery and repurposing. Pharmacogenomics 19:783–97
    [Google Scholar]
/content/journals/10.1146/annurev-pharmtox-010919-023324
Loading
/content/journals/10.1146/annurev-pharmtox-010919-023324
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error