TY - JOUR T1 - İLAÇ KEŞFİ VE GELİŞTİRİLMESİNDE YAPAY ZEKÂ TT - ARTIFICIAL INTELLIGENCE ON DRUG DISCOVERY AND DEVELOPMENT AU - Çelik, İrem Nur AU - Arslan, Firdevs Kübra AU - Tunç, Ramazan AU - Yıldız, İlkay PY - 2021 DA - May Y2 - 2021 DO - 10.33483/jfpau.878041 JF - Journal of Faculty of Pharmacy of Ankara University JO - J. Fac. Pharm. Ankara PB - Ankara Üniversitesi WT - DergiPark SN - 1015-3918 SP - 400 EP - 427 VL - 45 IS - 2 LA - tr AB - Amaç: Makine zekâsı olarak da bilinen Yapay Zekâ’nın ilaç keşfi ve geliştirilme sürecindeki yeri ve öneminin ortaya konması amaçlanmıştır.Sonuç ve Tartışma: İlaç keşfi ve geliştirme aşamaları, insan sağlığına ve refahına katkıda bulunan en önemli çeviri bilim etkinlikleri arasındadır. Bununla birlikte, yeni bir ilacın geliştirilmesi oldukça karmaşık, pahalı ve oldukça uzun bir süreçtir. Maliyetlerin nasıl azaltılacağı ve yeni ilaç keşfinin nasıl hızlandırılacağı endüstride zorlu ve ivedi ile çözülmesi gereken bir soru haline gelmiştir. Yapay zekânın (AI) yeni deneysel teknolojilerle bir araya gelmesi, yeni ilaç arayışını daha hızlı, daha ucuz ve daha etkili hale getirmesi beklenmektedir. Bu derlemede, ilaç keşif sürecini hızlandırmak için ortaya çıkan yapay zekâ uygulamaları ele alınmıştır. KW - Bilgisayar-destekli ilaç keşfi KW - makine öğrenmesi KW - yapay zekâ N2 - Objective: It is aimed to reveal the place and importance of Artificial Intelligence, also known as machine intelligence, in drug discovery and development.Result and Discussion: The drug discovery and development stages are among the most important science activities contributing to human health and well-being. However, the development of a new drug is a complex, expensive, and lengthy process. How to reduce costs and accelerate the discovery of new drugs has become a challenging and urgent question in the industry. The combination of artificial intelligence (AI) with new experimental technologies is expected to make the search for new drugs faster, cheaper and more effective. In this review, emerging artificial intelligence applications to speed up the drug discovery process are discussed. CR - 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 CR - 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 CR - 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 CR - AI for Chemistry Web Site. Retrieved December 20, 2020, from https://chemintelligence.com/ai-for-chemistry CR - 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 CR - 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 CR - Barr, A.; Feigenbaum, E. A.; Cohen, P. R. (1982). Handbook of Artificial Intelligence; Addison-Wesley Longman: Boston, MA, USA. CR - Popovic, D. ve Bhatkar, V. P. (1994). Methods and Tools for Applied Artificial Intelligence; Marcel Dekker: New York. CR - Bobrow, D. G. (1964). Natural Language Input for a Computer Problem Solving System. In Semantic Information Processing; MIT Press: Cambridge. CR - 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 CR - 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 CR - 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. CR - 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 CR - 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 CR - 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 CR - 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 CR - 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 CR - 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 CR - Martin Y. C. (2010). Quantitative Drug Design: A Critical Introduction. Boca Raton, FL: CRC Press. 2nd ed. CR - 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 CR - 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 CR - 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 CR - 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/ CR - 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 CR - 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 CR - 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 CR - 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 CR - Civelek, Ö. (2003). Bulanık Mantık Nedir Yapay Zekâ Nedir. Türkiye Mühendislik Haberleri Dergisi, 423(1), 40–50. CR - 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 CR - 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 CR - 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 CR - 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 CR - 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 CR - 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 CR - 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 CR - 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 CR - 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 CR - 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 CR - 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 CR - 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 CR - 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 CR - 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 CR - 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 CR - 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 CR - 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 CR - 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 CR - 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 CR - Hessler, G. ve Baringhaus, K. H. (2018). Artificial intelligence in drug design. Molecules, 23(10), 2520. https://doi.org/10.3390/molecules23102520 CR - 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 CR - 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 CR - 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 CR - 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 CR - 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 CR - 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 CR - 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 CR - 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 CR - 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 CR - 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 CR - 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 CR - 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 CR - 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 CR - 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 CR - 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 CR - 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 CR - 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 CR - 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 CR - 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 CR - 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 CR - 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 CR - 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 CR - Scott, A. (2018). A CRISPR path to drug discovery. Nature, 555, 10–11. https://doi.org/10.1038/d41586-018-02477-1 CR - 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 CR - 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 CR - 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 CR - 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 CR - 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 CR - 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 CR - 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 CR - 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 CR - 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 CR - 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 CR - 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 CR - 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 CR - 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 CR - 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. CR - Voosen, P. (2017). The AI detectives. Science, 357(6346), 22–27. https://doi.org/10.1126/science.357.6346.22 CR - 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 CR - 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 CR - 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 CR - 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 CR - 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 CR - Lecun, Y., Bengio, Y., Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539 UR - https://doi.org/10.33483/jfpau.878041 L1 - https://dergipark.org.tr/tr/download/article-file/1569045 ER -