ARTIFICIAL INTELLIGENCE ON DRUG DISCOVERY AND DEVELOPMENT

References (111)

1. Schneider, P., Walters, W. P., Plowright, A. T., Sieroka, N., Listgarten, J., Goodnow, R. A. Schneider, G. (2020). Rethinking Drug Design in The Artificial İntelligence Era. Nature Reviews Drug Discovery, 19(5), 353–364. https://doi.org/10.1038/s41573-019-0050-3
2. Lo, Y. C., Ren, G., Honda, H., Davis, K. L. (2019). Artificial Intelligence-Based Drug Design and Discovery. ChemInformatics and Its Applications. Drug Discovery Today. http://dx.doi.org/10.5772/intechopen.89012
3. Mak, K. K. ve Pichika, M. R. (2019). Artificial intelligence in drug development: present status and future prospects. Drug Discovery Today, 24(3), 773–780. https://doi.org/10.1016/j.drudis.2018.11.014
4. AI for Chemistry Web Site. Retrieved December 20, 2020, from https://chemintelligence.com/ai-for-chemistry
5. McCarthy, J. ve Hayes, P. (1969). Some Philosophical Problems From the Standpoint of Artificial Intelligence. In Machine Intelligence; Edinburgh University Press: Edinburgh, Retrieved from https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.85.5082
6. Yang, X., Wang, Y., Byrne, R., Schneider, G., Yang, S. (2019). Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery. Chemical Reviews, 119(18), 10520–10594. https://doi.org/10.1021/acs.chemrev.8b00728
7. Barr, A.; Feigenbaum, E. A.; Cohen, P. R. (1982). Handbook of Artificial Intelligence; Addison-Wesley Longman: Boston, MA, USA.
8. Popovic, D. ve Bhatkar, V. P. (1994). Methods and Tools for Applied Artificial Intelligence; Marcel Dekker: New York.
9. Bobrow, D. G. (1964). Natural Language Input for a Computer Problem Solving System. In Semantic Information Processing; MIT Press: Cambridge.
10. Weizenbaum, J. (1966). ELIZA---a computer program for the study of natural language communication between man and machine. Communications of the ACM, 9(1), 36–45. https://doi.org/10.1145/365153.365168
11. Baum, E. B. (1988). On the capabilities of multilayer perceptrons. Journal of Complexity, 4(3), 193–215. https://doi.org/10.1016/0885-064X(88)90020-9
12. Rumelhart, D. E., Hinton, G. E., Williams, R. J. (1986). Learning internal representations by error propagation. In Parallel Distributed Processing: Explorations in the Microstructure of Cognition. MIT Press: Cambridge.
13. Qian, N. ve Sejnowski, T. J. (1988). Predicting the secondary structure of globular proteins using neural network models. Journal of Molecular Biology, 202(4), 865–884. https://doi.org/10.1016/0022-2836(88)90564-5
14. Hammett, L. P. (1937). The Effect of Structure upon the Reactions of Organic Compounds. Benzene Derivatives. Journal of the American Chemical Society, 59(1), 96–103. https://doi.org/10.1021/ja01280a022
15. Hansch, C. ve Fujita, T. (1964). ρ-σ-π Analysis. A Method for the Correlation of Biological Activity and Chemical Structure. Journal of the American Chemical Society, 86(8), 1616–1626. https://doi.org/10.1021/ja01062a035
16. Miller, E. ve Hansch, C. (1967). Structure-Activity Analysis of Tetrahydrofolate Analogs Using Substituent Constants and Regression Analysis. Journal of Pharmaceutical Sciences, 56(1), 92−97. https://doi.org/10.1002/jps.2600560119
17. Kopecký, J., Boček, K., Vlachová, D. (1965). Chemical Structure and Biological Activity on m-and p-Disubstituted Derivatives of Benzene. Nature, 207(5000), 981–981. https://doi.org/10.1038/207981a0
18. Wessel, M. D., Jurs, P. C., Tolan, J. W., Muskal, S. M. (1998). Prediction of human intestinal absorption of drug compounds from molecular structure. Journal of Chemical Information and Computer Sciences, 38(4), 726–735. https://doi.org/10.1021/ci980029a
19. Martin Y. C. (2010). Quantitative Drug Design: A Critical Introduction. Boca Raton, FL: CRC Press. 2nd ed.
20. Basile, A. O., Yahi, A., Tatonetti, N. P. (2019). Artificial Intelligence for Drug Toxicity and Safety. Trends in Pharmacological Sciences, 40(9), 624–635. https://doi.org/10.1016/j.tips.2019.07.005
21. Zhu, H. (2020). Big data and artificial intelligence modeling for drug discovery. Annual Review of Pharmacology and Toxicology, 60(1), 573–589. https://doi.org/10.1146/annurev-pharmtox-010919-023324
22. Bunney, P. E., Zink, A. N., Holm, A. A., Billington, C. J., Kotz, C. M. (2017). Orexin activation counteracts decreases in nonexercise activity thermogenesis (NEAT) caused by high-fat diet. Physiology & Behavior, 176(1), 139–148. https://doi.org/10.1016/j.physbeh.2017.03.040
23. Properzi, F., Taylor, K., Steedman, M. (2019). Accelerating drug discovery. Intelligent drug discovery powered by AI. 2-7. Retrieved from https://blogs.deloitte.co.uk/health/
24. Panteleev, J., Gao, H., Jia, L. (2018). Recent applications of machine learning in medicinal chemistry. Bioorganic and Medicinal Chemistry Letters, 28(17), 2807–2815. https://doi.org/10.1016/j.bmcl.2018.06.046
25. D’Souza, S., Prema, K. V., Balaji, S. (2020). Machine learning models for drug–target interactions: current knowledge and future directions. Drug Discovery Today, 25(4), 748–756. https://doi.org/10.1016/j.drudis.2020.03.003
26. Linton-Reid, K. (2020). Introduction: An Overview of AI in Oncology Drug Discovery and Development. Artificial Intelligence in Oncology Drug Discovery and Development, (Ml), 1–13. https://doi.org/10.5772/intechopen.92799
27. Ippolito, M., Ferguson, J., Jenson, F. (2020). Improving facies prediction by combining supervised and unsupervised learning methods. Journal of Petroleum Science and Engineering, 200, 108300. https://doi.org/10.1016/j.petrol.2020.108300
28. Civelek, Ö. (2003). Bulanık Mantık Nedir Yapay Zekâ Nedir. Türkiye Mühendislik Haberleri Dergisi, 423(1), 40–50.
29. Bohr, H. (2020). Drug discovery and molecular modeling using artificial intelligence. In Artificial Intelligence in Healthcare, pp. https://doi.org/10.1016/b978-0-12-818438-7.00003-4
30. Paul, D., Sanap, G., Shenoy, S., Kalyane, D., Kalia, K., Tekade, R. K. (2020). Artificial intelligence in drug discovery and development. Drug Discovery Today, 26(1), 80–93. https://doi.org/10.1016/j.drudis.2020.10.010
31. Chen, H., Engkvist, O., Wang, Y., Olivecrona, M., Blaschke, T. (2018). The rise of deep learning in drug discovery. Drug Discovery Today, 23(6), 1241–1250. https://doi.org/10.1016/j.drudis.2018.01.039
32. Jing, Y., Bian, Y., Hu, Z., Wang, L., Xie, X. Q. S. (2018). Deep Learning for Drug Design: an Artificial Intelligence Paradigm for Drug Discovery in the Big Data Era. AAPS Journal, 20(3), 58. https://doi.org/10.1208/s12248-018-0210-0
33. Zhavoronkov, A. (2018). Artificial Intelligence for Drug Discovery, Biomarker Development, and Generation of Novel Chemistry. Molecular Pharmaceutics, 15(10), 4311–4313. https://doi.org/10.1021/acs.molpharmaceut.8b00930
34. Gunavathi, C., Sivasubramanian, K., Keerthika, P., Paramasivam, C. (2020). A review on convolutional neural network based deep learning methods in gene expression data for disease diagnosis. Materials Today: Proceedings. https://doi.org/10.1016/j.matpr.2020.10.263
35. Hubel, D. H. ve Wiesel, T. N. (1962). Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. The Journal of Physiology, 160(1), 106–154. https://doi.org/10.1113/jphysiol.1962.sp006837
36. Hubel, D. H. ve Wiesel, T. N. (1959). Receptive fields of single neurones in the cat’s striate cortex. The Journal of Physiology, 148(3), 574–591. https://doi.org/10.1113/jphysiol.1959.sp006308
37. Bilski, J., Rutkowski, L., Smoląg, J., Tao, D. (2021). A novel method for speed training acceleration of recurrent neural networks. Information Sciences, 553, 266–279. https://doi.org/10.1016/j.ins.2020.10.025
38. Big pharma is using AI and machine learning in drug discovery and development to save lives Web Site. Retrieved December 20, 2020, from https://www.businessinsider.com/ai-machine-learning-in-drug-discovery-development-2020
39. Chan, H. C. S., Shan, H., Dahoun, T., Vogel, H., Yuan, S. (2019). Advancing Drug Discovery via Artificial Intelligence. Trends in Pharmacological Sciences, 40(8), 592–604. https://doi.org/10.1016/j.tips.2019.06.004
40. Rubio, D. M. G., Schoenbaum, E. E., Lee, L. S., Schteingart, D. E., Marantz, P. R., Anderson, K. E. Esposito, K. (2010). Defining translational research: Implications for training. Academic Medicine, 85(3), 470–475. https://doi.org/10.1097/ACM.0b013e3181ccd618
41. Donner, Y., Kazmierczak, S., Fortney, K. (2018). Drug Repurposing Using Deep Embeddings of Gene Expression Profiles. Molecular Pharmaceutics, 15(10), 4314–4325. https://doi.org/10.1021/acs.molpharmaceut.8b00284
42. Xie, L., He, S., Song, X., Bo, X., Zhang, Z. (2018). Deep learning-based transcriptome data classification for drug-target interaction prediction. BMC Genomics, 19(7), 93-102. https://doi.org/10.1186/s12864-018-5031-0
43. Vanhaelen, Q., Mamoshina, P., Aliper, A. M., Artemov, A., Lezhnina, K., Ozerov, I. Zhavoronkov, A. (2017). Design of efficient computational workflows for in silico drug repurposing. Drug Discovery Today, 22(2), 210–222. https://doi.org/10.1016/j.drudis.2016.09.019
44. Aliper, A., Jellen, L., Cortese, F., Artemov, A., Semper, D. K., Moskalev, A. Zhavoronkov, A. (2017). Towards natural mimetics of metformin and rapamycin. Aging, 9(11), 2245–2268. https://doi.org/10.18632/aging.101319
45. Gayvert, K. M., Madhukar, N. S., Elemento, O. (2016). A Data-Driven Approach to Predicting Successes and Failures of Clinical Trials. Cell Chemical Biology, 23(10), 1294–1301. https://doi.org/10.1016/j.chembiol.2016.07.023
46. Mayr, A., Klambauer, G., Unterthiner, T., Hochreiter, S. (2016). DeepTox: Toxicity prediction using deep learning. Frontiers in Environmental Science, 3(80). https://doi.org/10.3389/fenvs.2015.00080
47. Zhavoronkov, A., Ivanenkov, Y. A., Aliper, A., Veselov, M. S., Aladinskiy, V. A., Aladinskaya, A. V. Aspuru-Guzik, A. (2019). Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nature Biotechnology, 37(9), 1038–1040. https://doi.org/10.1038/s41587-019-0224-x
48. Hessler, G. ve Baringhaus, K. H. (2018). Artificial intelligence in drug design. Molecules, 23(10), 2520. https://doi.org/10.3390/molecules23102520
49. Riddick, G., Song, H., Ahn, S., Walling, J., Borges-Rivera, D., Zhang, W. Fine, H. A. (2011). Predicting in vitro drug sensitivity using random forests. Bioinformatics, 27(2), 220–224. https://doi.org/10.1093/bioinformatics/btq628
50. Iorio, F., Knijnenburg, T. A., Vis, D. J., Bignell, G. R., Menden, M. P., Schubert, M. Garnett, M. J. (2016). A Landscape of Pharmacogenomic Interactions in Cancer. Cell, 166(3), 740–754. https://doi.org/10.1016/j.cell.2016.06.017
51. Cortés-Ciriano, I., Van Westen, G. J. P., Bouvier, G., Nilges, M., Overington, J. P., Bender, A. Malliavin, T. E. (2016). Improved large-scale prediction of growth inhibition patterns using the NCI60 cancer cell line panel. Bioinformatics, 32(1), 85–95. https://doi.org/10.1093/bioinformatics/btv529
52. Tetko, I. V. ve Bruneau, P. (2004). Application of ALOGPS to predict 1-octanol/water distribution coefficients, logP, and logD, of AstraZeneca in-house database. Journal of Pharmaceutical Sciences, 93(12), 3103–3110. https://doi.org/10.1002/jps.20217
53. Lusci, A., Pollastri, G., Baldi, P. (2013). Deep architectures and deep learning in chemoinformatics: The prediction of aqueous solubility for drug-like molecules. Journal of Chemical Information and Modeling, 53(7), 1563–1575. https://doi.org/10.1021/ci400187y
54. Koscielny, G., An, P., Carvalho-Silva, D., Cham, J. A., Fumis, L., Gasparyan, R. Dunham, I. (2017). Open Targets: A platform for therapeutic target identification and Validation. Nucleic Acids Research, 45(1), 985–994. https://doi.org/10.1093/nar/gkw1055
55. Ferrero, E., Dunham, I., Sanseau, P. (2017). In silico prediction of novel therapeutic targets using gene-disease association data. Journal of Translational Medicine, 15(1), 1–16. https://doi.org/10.1186/s12967-017-1285-6
56. Cavasotto, C. N. ve Di Filippo, J. I. (2021). Artificial intelligence in the early stages of drug discovery. Archives of Biochemistry and Biophysics, 698, 108730. https://doi.org/10.1016/j.abb.2020.108730
57. Plante, A., Shore, D. M., Morra, G., Khelashvili, G., Weinstein, H. (2019). A Machine Learning Approach for the Discovery of Ligand-Specific Functional Mechanisms of GPCRs. Molecules, 24(11), 2097. http://dx.doi.org/10.3390/molecules24112097
58. Díaz, Ó., Dalton, J. A. R., Giraldo, J. (2019). Artificial Intelligence: A Novel Approach for Drug Discovery. Trends in Pharmacological Sciences, 40(8), 550–551. https://doi.org/10.1016/j.tips.2019.06.005
59. Ferraro, M., Decherchi, S., De Simone, A., Recanatini, M., Cavalli, A., Bottegoni, G. (2020). Multi-target dopamine D3 receptor modulators: Actionable knowledge for drug design from molecular dynamics and machine learning. European Journal of Medicinal Chemistry, 188, 111975. https://doi.org/10.1016/j.ejmech.2019.111975
60. Beck, B. R., Shin, B., Choi, Y., Park, S., Kang, K. (2020). Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model. Computational and Structural Biotechnology Journal, 18, 784–790. https://doi.org/10.1016/j.csbj.2020.03.025
61. Green, D. V. S., Pickett, S., Luscombe, C., Senger, S., Marcus, D., Meslamani, J. Masson, J. (2020). BRADSHAW: a system for automated molecular design. Journal of Computer-Aided Molecular Design, 34(7), 747–765. https://doi.org/10.1007/s10822-019-00234-8
62. Camodeca, C., Nuti, E., Tepshi, L., Boero, S., Tuccinardi, T., Stura, E. A. Rossello, A. (2016). Discovery of a new selective inhibitor of A Disintegrin and Metalloprotease 10 (ADAM-10) able to reduce the shedding of NKG2D ligands in Hodgkin’s lymphoma cell models. European Journal of Medicinal Chemistry, 111, 193–201. https://doi.org/10.1016/j.ejmech.2016.01.053
63. Healy, E. F., Romano, P., Mejia, M., Lindfors, G. (2010). Acetylenic inhibitors of ADAM10 and ADAM17: In silico analysis of potency and selectivity. Journal of Molecular Graphics and Modelling, 29(3), 436–442. https://doi.org/10.1016/j.jmgm.2010.08.006
64. Tippmann, F., Hundt, J., Schneider, A., Endres, K., Fahrenholz, F. (2009). Up‐regulation of the α‐secretase ADAM10 by retinoic acid receptors and acitretin. The FASEB Journal, 23(6), 1643–1654. https://doi.org/10.1096/fj.08-121392
65. Altmeppen, H. C., Prox, J., Krasemann, S., Puig, B., Kruszewski, K., Dohler, F. Glatze, M. (2015). The sheddase ADAM10 is a potent modulator of prion disease. ELife, 2015(4), 1–50. https://doi.org/10.7554/eLife.04260
66. Kohutek, Z. A., DiPierro, C. G., Redpath, G. T., Hussaini, I. M. (2009). ADAM-10-Mediated N-Cadherin Cleavage Is Protein Kinase C-α Dependent and Promotes Glioblastoma Cell Migration. Journal of Neuroscience, 29(14), 4605–4615. https://doi.org/10.1523/JNEUROSCI.5126-08.2009
67. Woods, N., Trevino, J., Coppola, D., Chellappan, S., Yang, S., Padmanabhan, J. (2015). Fendiline inhibits proliferation and invasion of pancreatic cancer cells by interfering with ADAM10 activation and β-catenin signaling. Oncotarget, 6(34), 35931–35948. https://doi.org/10.18632/oncotarget.5933
68. Shi, T., Huang, S., Chen, L., Heng, Y., Kuang, Z. Mei, H. (2020). A molecular generative model of ADAM10 inhibitors by using GRU-based deep neural network and transfer learning. Chemometrics and Intelligent Laboratory Systems, 205, 104122. https://doi.org/10.1016/j.chemolab.2020.104122
69. Segler, M. H. S., Kogej, T., Tyrchan, C., Waller, M. P. (2018). Generating focused molecule libraries for drug discovery with recurrent neural networks. ACS Central Science, 4(1), 120–131. https://doi.org/10.1021/acscentsci.7b00512
70. Luo, J. (2016). CRISPR/Cas9: From Genome Engineering to Cancer Drug Discovery. Trends in Cancer, 2(6), 313–324. https://doi.org/10.1016/j.trecan.2016.05.001
71. Scott, A. (2018). A CRISPR path to drug discovery. Nature, 555, 10–11. https://doi.org/10.1038/d41586-018-02477-1
72. Wallach, I., Dzamba, M., Heifets, A. (2015). AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery. 1–11. Retrieved from http://arxiv.org/abs/1510.02855
73. Spitzer, R. ve Jain, A. N. (2012). Surflex-Dock: Docking benchmarks and real-world application. Journal of Computer-Aided Molecular Design, 26(6), 687–699. https://doi.org/10.1007/s10822-011-9533-y
74. Allen, W. J., Balius, T. E., Mukherjee, S., Brozell, S. R., Moustakas, D. T., Lang, P. T. Rizzo, R. C. (2015). DOCK 6: Impact of new features and current docking performance. Journal of Computational Chemistry, 36(15), 1132–1156. https://doi.org/10.1002/jcc.23905
75. Kuenzi, B. M., Park, J., Fong, S. H., Sanchez, K. S., Lee, J., Kreisberg, J. F. Ideker, T. (2020). Predicting Drug Response and Synergy Using a Deep Learning Model of Human Cancer Cells. Cancer Cell, 38(5), 672-684. https://doi.org/10.1016/j.ccell.2020.09.014
76. Hasan Mahmud, S. M., Chen, W., Jahan, H., Dai, B., Din, S. U., Dzisoo, A. M. (2020). DeepACTION: A deep learning-based method for predicting novel drug-target interactions. Analytical Biochemistry, 610, 113978. https://doi.org/10.1016/j.ab.2020.113978
77. Wan, F., Zhu, Y., Hu, H., Dai, A., Cai, X., Chen, L. Zeng, J. (2019). DeepCPI: A Deep Learning-based Framework for Large-scale in silico Drug Screening. Genomics, Proteomics and Bioinformatics, 17(5), 478–495. https://doi.org/10.1016/j.gpb.2019.04.003
78. Lagunin, A., Zakharov, A., Filimonov, D., Poroikov, V. (2011). QSAR modelling of rat acute toxicity on the basis of PASS prediction. Molecular Informatics, 30(2–3), 241–250. https://doi.org/10.1002/minf.201000151
79. Soufan, O., Ba-Alawi, W., Afeef, M., Essack, M., Kalnis, P., Bajic, V. B. (2016). DRABAL: novel method to mine large high-throughput screening assays using Bayesian active learning. Journal of Cheminformatics, 8(1), 1–14. https://doi.org/10.1186/s13321-016-0177-8
80. Malandraki-Miller, S. ve Riley, P. R. (2021). Use of artificial intelligence to enhance phenotypic drug discovery. Drug Discovery Today. https://doi.org/10.1016/j.drudis.2021.01.013
81. Zhong, F., Xing, J., Li, X., Liu, X., Fu, Z., Xiong, Z. Jiang, H. (2018). Artificial intelligence in drug design. Science China Life Sciences, 61(10), 1191–1204. https://doi.org/10.1007/s11427-018-9342-2
82. Kalliokoski, T., Kramer, C., Vulpetti, A., Gedeck, P. (2013). Comparability of Mixed IC50 Data -A Statistical Analysis. PLoS ONE, 8(4), 1-11. https://doi.org/10.1371/journal.pone.0061007
83. Solomon, S. M. (2020). Genome editing in animals: why FDA regulation matters. Nature Biotechnology, 38(2), 142–143. https://doi.org/10.1038/s41587-020-0413-7
84. Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P. Greene, C. S. (2018). Opportunities and obstacles for deep learning in biology and medicine. Journal of The Royal Society Interface, 15(141). https://doi.org/10.1101/142760
85. Carlini, N., Liu, C., Erlingsson, Ú., Kos, J., Song, D. (2019). The secret Sharer: Evaluating and testing unintended memorization in neural networks. In Proceedings of the 28th USENIX Security Symposium, (pp. 267–284). Santa Clara, CA, USA.
86. Voosen, P. (2017). The AI detectives. Science, 357(6346), 22–27. https://doi.org/10.1126/science.357.6346.22
87. Tishby, N. ve Zaslavsky, N. (2015). Deep learning and the information bottleneck principle. In Proceedings of the 2015 IEEE Information Theory Workshop (ITW), (pp. 1-5). Jeju Island, Korea. https://doi.org/10.1109/ITW.2015.7133169
88. Merk, D., Friedrich, L., Grisoni, F., Schneider, G. (2018). De Novo Design of Bioactive Small Molecules by Artificial Intelligence. Molecular Informatics, 37(1-2). https://doi.org/10.1002/minf.201700153
89. Lake, F. (2019). Artificial intelligence in drug discovery: what is new, and what is next? Future Drug Discovery, 1(2). https://doi.org/10.4155/fdd-2019-0025
90. Hassan Baig, M., Ahmad, K., Roy, S., Mohammad Ashraf, J., Adil, M., Haris Siddiqui, M. Choi, I. (2016). Computer Aided Drug Design: Success and Limitations. Current Pharmaceutical Design, 22(5), 572–581. https://doi.org/10.2174/1381612822666151125000550
91. Zhou, Y., Wang, F., Tang, J., Nussinov, R., Cheng, F. (2020). Artificial intelligence in COVID-19 drug repurposing. The Lancet Digital Health, 2(12), 667–676. https://doi.org/10.1016/S2589-7500(20)30192-8
92. Lecun, Y., Bengio, Y., Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
93. Schneider, P., Walters, W. P., Plowright, A. T., Sieroka, N., Listgarten, J., Goodnow, R. A. Schneider, G. (2020). Rethinking Drug Design in The Artificial İntelligence Era. Nature Reviews Drug Discovery, 19(5), 353–364. https://doi.org/10.1038/s41573-019-0050-3
94. Rubio, D. M. G., Schoenbaum, E. E., Lee, L. S., Schteingart, D. E., Marantz, P. R., Anderson, K. E. Esposito, K. (2010). Defining translational research: Implications for training. Academic Medicine, 85(3), 470–475. https://doi.org/10.1097/ACM.0b013e3181ccd618
95. Vanhaelen, Q., Mamoshina, P., Aliper, A. M., Artemov, A., Lezhnina, K., Ozerov, I. Zhavoronkov, A. (2017). Design of efficient computational workflows for in silico drug repurposing. Drug Discovery Today, 22(2), 210–222. https://doi.org/10.1016/j.drudis.2016.09.019
96. Aliper, A., Jellen, L., Cortese, F., Artemov, A., Semper, D. K., Moskalev, A. Zhavoronkov, A. (2017). Towards natural mimetics of metformin and rapamycin. Aging, 9(11), 2245–2268. https://doi.org/10.18632/aging.101319
97. Zhavoronkov, A., Ivanenkov, Y. A., Aliper, A., Veselov, M. S., Aladinskiy, V. A., Aladinskaya, A. V. Aspuru-Guzik, A. (2019). Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nature Biotechnology, 37(9), 1038–1040. https://doi.org/10.1038/s41587-019-0224-x
98. Riddick, G., Song, H., Ahn, S., Walling, J., Borges-Rivera, D., Zhang, W. Fine, H. A. (2011). Predicting in vitro drug sensitivity using random forests. Bioinformatics, 27(2), 220–224. https://doi.org/10.1093/bioinformatics/btq628
99. Iorio, F., Knijnenburg, T. A., Vis, D. J., Bignell, G. R., Menden, M. P., Schubert, M. Garnett, M. J. (2016). A Landscape of Pharmacogenomic Interactions in Cancer. Cell, 166(3), 740–754. https://doi.org/10.1016/j.cell.2016.06.017
100. Cortés-Ciriano, I., Van Westen, G. J. P., Bouvier, G., Nilges, M., Overington, J. P., Bender, A. Malliavin, T. E. (2016). Improved large-scale prediction of growth inhibition patterns using the NCI60 cancer cell line panel. Bioinformatics, 32(1), 85–95. https://doi.org/10.1093/bioinformatics/btv529
101. Koscielny, G., An, P., Carvalho-Silva, D., Cham, J. A., Fumis, L., Gasparyan, R. Dunham, I. (2017). Open Targets: A platform for therapeutic target identification and Validation. Nucleic Acids Research, 45(1), 985–994. https://doi.org/10.1093/nar/gkw1055
102. Green, D. V. S., Pickett, S., Luscombe, C., Senger, S., Marcus, D., Meslamani, J. Masson, J. (2020). BRADSHAW: a system for automated molecular design. Journal of Computer-Aided Molecular Design, 34(7), 747–765. https://doi.org/10.1007/s10822-019-00234-8
103. Camodeca, C., Nuti, E., Tepshi, L., Boero, S., Tuccinardi, T., Stura, E. A. Rossello, A. (2016). Discovery of a new selective inhibitor of A Disintegrin and Metalloprotease 10 (ADAM-10) able to reduce the shedding of NKG2D ligands in Hodgkin’s lymphoma cell models. European Journal of Medicinal Chemistry, 111, 193–201. https://doi.org/10.1016/j.ejmech.2016.01.053
104. Altmeppen, H. C., Prox, J., Krasemann, S., Puig, B., Kruszewski, K., Dohler, F. Glatze, M. (2015). The sheddase ADAM10 is a potent modulator of prion disease. ELife, 2015(4), 1–50. https://doi.org/10.7554/eLife.04260
105. Shi, T., Huang, S., Chen, L., Heng, Y., Kuang, Z. Mei, H. (2020). A molecular generative model of ADAM10 inhibitors by using GRU-based deep neural network and transfer learning. Chemometrics and Intelligent Laboratory Systems, 205, 104122. https://doi.org/10.1016/j.chemolab.2020.104122
106. Allen, W. J., Balius, T. E., Mukherjee, S., Brozell, S. R., Moustakas, D. T., Lang, P. T. Rizzo, R. C. (2015). DOCK 6: Impact of new features and current docking performance. Journal of Computational Chemistry, 36(15), 1132–1156. https://doi.org/10.1002/jcc.23905
107. Kuenzi, B. M., Park, J., Fong, S. H., Sanchez, K. S., Lee, J., Kreisberg, J. F. Ideker, T. (2020). Predicting Drug Response and Synergy Using a Deep Learning Model of Human Cancer Cells. Cancer Cell, 38(5), 672-684. https://doi.org/10.1016/j.ccell.2020.09.014
108. Wan, F., Zhu, Y., Hu, H., Dai, A., Cai, X., Chen, L. Zeng, J. (2019). DeepCPI: A Deep Learning-based Framework for Large-scale in silico Drug Screening. Genomics, Proteomics and Bioinformatics, 17(5), 478–495. https://doi.org/10.1016/j.gpb.2019.04.003
109. Zhong, F., Xing, J., Li, X., Liu, X., Fu, Z., Xiong, Z. Jiang, H. (2018). Artificial intelligence in drug design. Science China Life Sciences, 61(10), 1191–1204. https://doi.org/10.1007/s11427-018-9342-2
110. Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P. Greene, C. S. (2018). Opportunities and obstacles for deep learning in biology and medicine. Journal of The Royal Society Interface, 15(141). https://doi.org/10.1101/142760
111. Hassan Baig, M., Ahmad, K., Roy, S., Mohammad Ashraf, J., Adil, M., Haris Siddiqui, M. Choi, I. (2016). Computer Aided Drug Design: Success and Limitations. Current Pharmaceutical Design, 22(5), 572–581. https://doi.org/10.2174/1381612822666151125000550