___

• 1. Schneider, P., Walters, W. P., Plowright, A. T., Sieroka, N., Listgarten, J., Goodnow, R. A., Fisher, J., Jansen, J. M., Duca, J. S., Rush, T. S., Zentgraf, M., Hill, J. E., Krutoholow, E., Kohler, M., Blaney, J., Funatsu, K., Luebkemann, C., 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., 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., 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; AddisonWesley Longman: Boston, MA, USA.
• 8. Popovic, D., 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., 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., 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., 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 mand 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-pharmtox010919-023324
• 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., 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., 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-machinelearning-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., Platt, L. D., Baez, A., Esposito, K. (2010). Defining translational research: Implications for training. Academic Medicine, 85(3), 470–475. https://doi.org/10.1097/ACM.0b013e3181ccd618
• 41. Merck Molecular Activity Challenge. Retrieved December 20, 2020, from https://www.kaggle.com/c/MerckActivity 42. DeepCodex: a deep code for gene expression data. Retrieved December 20, 2020, from http://deepcodex.org
• 43. 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
• 44. 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
• 45. Vanhaelen, Q., Mamoshina, P., Aliper, A. M., Artemov, A., Lezhnina, K., Ozerov, I., Labat, 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
• 46. Aliper, A., Jellen, L., Cortese, F., Artemov, A., Semper, D. K., Moskalev, A., Swick, A. G., Zhavoronkov, A. (2017). Towards natural mimetics of metformin and rapamycin. Aging, 9(11), 2245–2268.https://doi.org/10.18632/aging.101319
• 47. 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
• 48. 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
• 49. Zhavoronkov, A., Ivanenkov, Y. A., Aliper, A., Veselov, M. S., Aladinskiy, V. A., Aladinskaya, A. V., Terentiev, V. A., Polykovskiy, D. A., Kuznetsov, M. D., Asadulaev, A., Volkov, Y., Zholus, A., Shayakhmetov, R. R., Zhebrak, A., Minaeva, L. I., Zagribelnyy, B. A., Lee, L. H., Soll, R., Madge, D., Xing, L., Guo, T., 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
• 50. Hessler, G., Baringhaus, K. H. (2018). Artificial intelligence in drug design. Molecules, 23(10), 2520.https://doi.org/10.3390/molecules23102520
• 51. 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
• 52. Iorio, F., Knijnenburg, T. A., Vis, D. J., Bignell, G. R., Menden, M. P., Schubert, M., Aben, N., Gonçalves, E., Barthorpe, S., Lightfoot, H., Cokelaer, T., Greninger, P., Syk, E. V., Chang, H., Silva, H. D., Heyn, H., Deng, X., Egan, R.K., Liu, Q., Mironenko, T., Mitropoulos, X., Richardson, L., Wang, J., Zhang, T., Moran, S., Sayols, S., Soleimani, M., Tamborero, D., Lopez-Bigas, N., Ross-Macdonald, P., Esteller, M., Gray, N. S., Haber, D. A., Stratton, M. R., Benes, C. H., Wessels, L. F. A., Saez-Rodriguez, J., McDermott, U., 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
• 53. 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
• 54. Tetko, I. V., 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
• 55. 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
• 56. Koscielny, G., An, P., Carvalho-Silva, D., Cham, J. A., Fumis, L., Gasparyan, R., Hasan, S., Karamanis, N., Maguire, M., Papa, E., Pierleoni, A., Pignatelli, M., Platt, T., Rowland, W., Wankar, P., Pento, A. P., Burdett, T., Fabregat, A., Forbes, S., Gaulton, A., Gonzalez, C. Y., Hermjakob, H., Hersey, A., Jupe, S., Kafkas, Ş., Keays, M., Leroy, C., Lopez, F. J., Magarinos, M. P., Malone, J., McEntyre, J., Fuentes, A. M. P., O’Donovan, C., Papatheodorou, I., Parkinson, H., Palka, B., Paschall, J., Petryszak, R., Pratanwanich, N., Sarntivijal, S., Saunders, G., Sidiropoulos, K., Smith, T., Sondka, Z., Stegle, O., Tang, Y. A., Turner, E., Vaughan, B., Vrousgou, O., Watkins, X., Martin, M. J., Sanseau, P., Vamathevan, J., Birney, E., Barrett, J., 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
• 57. 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
• 58. Cavasotto, C. N., 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
• 59. 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
• 60. 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
• 61. 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
• 62. 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
• 63. Green, D. V. S., Pickett, S., Luscombe, C., Senger, S., Marcus, D., Meslamani, J., Brett, D., Powell, A., 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
• 64. Camodeca, C., Nuti, E., Tepshi, L., Boero, S., Tuccinardi, T., Stura, E. A., Poggi, A., Zocchi, M. R., 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
• 65. 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
• 66. 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
• 67. Altmeppen, H. C., Prox, J., Krasemann, S., Puig, B., Kruszewski, K., Dohler, F., Bernreuther, C., Hoxha, A., Linsenmeier, L., Sikorska, B., Liberski, P. P., Bartsch, U., Saftig, P., 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
• 68. Kohutek, Z. A., DiPierro, C. G., Redpath, G. T., Hussaini, I. M. (2009). ADAM-10-Mediated NCadherin 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
• 69. 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
• 70. Shi, T., Huang, S., Chen, L., Heng, Y., Kuang, Z., Xu, L., 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
• 71. 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
• 72. 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
• 73. Scott, A. (2018). A CRISPR path to drug discovery. Nature, 555, 10–11. https://doi.org/10.1038/d41586-018-02477-1
• 74. 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
• 75. Spitzer, R., 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
• 76. Allen, W. J., Balius, T. E., Mukherjee, S., Brozell, S. R., Moustakas, D. T., Lang, P. T., Case, D. A., Kuntz, I. D., 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
• 77. Kuenzi, B. M., Park, J., Fong, S. H., Sanchez, K. S., Lee, J., Kreisberg, J. F., Ma, J., 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
• 78. 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
• 79. Wan, F., Zhu, Y., Hu, H., Dai, A., Cai, X., Chen, L., Gong, H., Xia, T., Yang, D., Wang, M. W., 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
• 80. 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
• 81. 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
• 82. Malandraki-Miller, S., 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
• 83. Zhong, F., Xing, J., Li, X., Liu, X., Fu, Z., Xiong, Z., Lu, D., Wu, X., Zhao, J., Tan, X., Li, F., Luo, X., Li, Z., Chen, K., Zheng, M., 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
• 84. 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
• 85. 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
• 86. Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P., Ferrero, E., Agapow, P.M., Zietz, M., Hoffman, M. M., Xie, W., Rosen, G. L., Lengerich, B.J., Israeli, J., Lanchantin, J., Woloszynek, S., Carpenter, A. E., Shrikumar, A., Xu, J., Cofer, E.M. Lavender, C.A., Turaga, S. C., Alexandari, A. M., Lu, Z., Harris, D. J., DeCaprio, D., Qi, Y., Kundaje, A., Peng, Y., Wiley, L. K., Segler, M. H. S., Boca, S. M., Swamidass, S. J., Huang, A., Gitter, A., 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
• 87. 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.
• 88. Voosen, P. (2017). The AI detectives. Science, 357(6346), 22–27. https://doi.org/10.1126/science.357.6346.22
• 89. Tishby, N., 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
• 90. 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
• 91. 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
• 92. Hassan Baig, M., Ahmad, K., Roy, S., Mohammad Ashraf, J., Adil, M., Haris Siddiqui, M., Khan, S., Amjad Kamal, M., Provazník, I., Choi, I. (2016). Computer Aided Drug Design: Success and Limitations. Current Pharmaceutical Design, 22(5), 572–581. https://doi.org/10.2174/1381612822666151125000550
• 93. Zhou, Y., Wang, F., Tang, J., Nussinov, R., Cheng, F. (2020). Artificial intelligence in COVID19 drug repurposing. The Lancet Digital Health, 2(12), 667–676. https://doi.org/10.1016/S2589- 7500(20)30192-8
• 94. Lecun, Y., Bengio, Y., Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539