İLAÇ TASARIMINDA YAPAY ZEKÂ UYGULAMALARI

Öz

Amaç: Yapay zekâ üzerindeki çalışmaların giderek artması, her alanda olduğu gibi ilaç endüstrisinin de bu çalışmalardan faydalanmasına sebep olmaktadır. Bu çalışmada, yapay zeka uygulamalarının ilaç tasarımı ve geliştirilmesi üzerinde nasıl bir rol aldığının incelenmesi amaçlanmıştır. Sonuç ve Tartışma: Yeni biyolojik olarak aktif bileşiklere ihtiyacın giderek arttığı günümüzde, yapay zekada sürekli yeni algoritmaların ortaya çıkması, güçlü hesaplama yeteneği, elde edilen kimyasal ve biyolojik verilerin birikmesi, ilaç tasarımında yapay zekâ kullanımına olanak sunmaktadır. İlaç tasarım aşamalarının neredeyse tüm basamaklarında uygulanabilen yapay zekâ yöntemleriyle, yeni ilaç geliştirilmesindeki uzun zaman gereksinimi ve yüksek maliyet gibi zorluklar azaltılmaya çalışılmaktadır. Bu çalışma sonucunda, yapay zekâ teknolojisinin ilaç tasarım sürecindeki uygulamaları ve geleneksel yöntemlere göre avantajları kapsamlı bir şekilde analiz edilerek karşılaştırılmıştır.

Anahtar Kelimeler

• Derin öğrenme
• ilaç tasarımı
• makine öğrenimi
• yapay zekâ

Destekleyen Kurum

Bu çalışma herhangi bir proje desteği olmadan Çukurova Üniversitesi Eczacılık Fakültesi bünyesinde gerçekleştirilmiştir.

ARTIFICIAL INTELLIGENCE APPLICATIONS IN DRUG DESIGN

Öz

Objective: The increasing number of studies on artificial intelligence causes the pharmaceutical industry to benefit from these studies, as in every other field. This study is aimed at examining how artificial intelligence applications play a role in drug design and development. Result and Discussion: In today’s world, where the need for new biologically active compounds is increasing, the continuous emergence of new algorithms in artificial intelligence, strong computational ability, and accumulation of obtained chemical and biological data allow the use of artificial intelligence in drug design. With artificial intelligence methods that can be applied at almost all stages of drug design, difficulties such as long time requirements and high costs in developing new drugs are tried to be reduced. As a result of this study, the applications of artificial intelligence technology in the drug design process and its advantages over traditional methods have been extensively analyzed and compared.

Anahtar Kelimeler

• Artificial intelligence
• deep learning
• drug design
• machine learning

Kaynakça

1. 1. Lo, Y.C., Ren, G., Honda, H.L., Davis, K. (2020). Artificial intelligence-based drug design and discovery. Cheminformatics and Its Applications. [CrossRef]
2. 2. Mandal, S., Moudgil, M., Mandal, S.K. (2009). Rational drug design. European Journal of Pharmacology, 625(1-3), 90-100. [CrossRef]
3. 3. 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. [CrossRef]
4. 4. Gertrudes, J.C., Maltarollo, V.G., Silva, R.A., Oliveira, P.R., Honorio, K.M., da Silva, A.B.F. (2012). Machine learning techniques and drug design. Current Medicinal Chemistry, 19(25), 4289-4297. [CrossRef]
5. 5. Hessler, G., Baringhaus, K.H. (2018). Artificial intelligence in drug design. Molecules, 23(10), 2520. [CrossRef]
6. 6. Michie, D., Spiegelhalter, D.J., Taylor, C.C., Campbell, J. (Eds.) (1994). Machine Learning, Neural and Statistical Classification ABD: Ellis Horwood.
7. 7. Kaul, V., Enslin, S., Gross, S.A. (2020). History of artificial intelligence in medicine. Gastrointestinal Endoscopy, 92(4), 807-812. [CrossRef]
8. 8. Intelligent drug discovery powered by AI, A report from the Deloitte Centre for Health Solutions. Retrieved 14.07.2023, from: https://www2.deloitte.com/content/dam/Deloitte/my/Documents/risk/my-risk-sdg3-intelligent-drug-discovery.pdf.

9. 9. Greenhill, A.T., Edmunds, B.R. (2020). A primer of artificial intelligence in medicine. Techniques and Innovations in Gastrointestinal Endoscopy, 22(2), 85-89. [CrossRef]
10. 10. Hoogenboom, S.A., Bagci, U., Wallace, M.B. (2020). Artificial intelligence in gastroenterology. The current state of play and the potential. How will it affect our practice and when? Techniques and Innovations in Gastrointestinal Endoscopy, 22(2), 42-47. [CrossRef]
11. 11. Le Berre, C., Sandborn, W.J., Aridhi, S., Devignes, M.D., Fournier, L., Smaïl-Tabbone, M., Danese, S., Peyrin-Biroulet, L. (2020). Application of artificial intelligence to gastroenterology and hepatology. Gastroenterology, 158(1), 76-94.e2. [CrossRef]
12. 12. LeCun, Y., Bengio, Y., Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. [CrossRef]
13. 13. Sarkar, C., Das, B., Rawat, V S., Wahlang, J.B., Nongpiur, A., Tiewsoh, I., Lyngdoh, N.M., Das, D., Bidarolli, M., Sony, H.T. (2023). Artificial intelligence and machine learning technology driven modern drug discovery and development. International Journal of Molecular Sciences, 24(3), 2026. [CrossRef]
14. 14. 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. [CrossRef]
15. 15. Ippolito, M., Ferguson, J., Jenson, F. (2021). Improving facies prediction by combining supervised and unsupervised learning methods. Journal of Petroleum Science and Engineering, 200, 108300. [CrossRef]
16. 16. Linton-Reid, K. (2020). Introduction: An overview of AI in oncology drug discovery and development. In Artificial Intelligence in Oncology Drug Discovery and Development. [CrossRef]
17. 17. Bohr, H. (2020). Drug discovery and molecular modeling using artificial intelligence. In Artificial Intelligence in Healthcare (pp. 61-83). Elsevier. [CrossRef]
18. 18. 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. [CrossRef]
19. 19. 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. The AAPS Journal, 20(3), 58. [CrossRef]
20. 20. Gunavathi, C., Sivasubramanian, K., Keerthika, P., Paramasivam, C. (2021). A review on convolutional neural network based deep learning methods in gene expression data for disease diagnosis. Materials Today: Proceedings, 45, 2282-2285. [CrossRef]
21. 21. 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. [CrossRef]
22. 22. 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. [CrossRef]
23. 23. Ramesh, A., Kambhampati, C., Monson, J., Drew, P. (2004). Artificial intelligence in medicine. Annals of The Royal College of Surgeons of England, 86(5), 334-338. [CrossRef]
24. 24. Amisha, Malik, P., Pathania, M., Rathaur, V. (2019). Overview of artificial intelligence in medicine. Journal of Family Medicine and Primary Care, 8(7), 2328. [CrossRef]
25. 25. Hamet, P., Tremblay, J. (2017). Artificial intelligence in medicine. Metabolism, 69, S36-S40. [CrossRef]
26. 26. Moran, M.E. (2007). Evolution of robotic arms. Journal of Robotic Surgery, 1(2), 103-111. [CrossRef]
27. 27. 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. [CrossRef]
28. 28. Kuipers, B., Feigenbaum, E.A., Hart, P.E., Nilsson, N.J. (2017). Shakey: From conception to history. AI Magazine, 38(1), 88-103. [CrossRef]
29. 29. Kulikowski, C.A. (2015). An opening chapter of the first generation of artificial intelligence in medicine: the first rutgers aim workshop, june 1975. Yearbook of Medical Informatics, 24(01), 227-233. [CrossRef]
30. 30. Ferrucci, D., Levas, A., Bagchi, S., Gondek, D., Mueller, E.T. (2013). Watson: beyond jeopardy! Artificial Intelligence, 199-200, 93-105. [CrossRef]
31. 31. Comendador, B.E.V., Francisco, B.M.B., Medenilla, J.S., Nacion, S.M.T., Serac, T.B.E. (2015). Pharmabot: a pediatric generic medicine consultant chatbot. Journal of Automation and Control Engineering, 3(2), 137-140. [CrossRef]
32. 32. Ni, L., Lu, C., Liu, N., Liu, J. (2017). MANDY: Towards a smart primary care chatbot application. In: Chen, J., Theeramunkong, T., Supnithi, T., Tang, X. (eds) Knowledge and Systems Sciences. Communications in Computer and Information Science, vol 780. Springer, Singapore. [CrossRef]
33. 33. Artificial intelligence: Google’s AlphaGo beats Go master Lee Sedol. In: Technology. BBC NEWS. 12 March 2016. from: http://www.bbc.com/news/technology-35785875# Erişim Tarihi: 14.07.2023
34. 34. Silver, D., Huang, A., Maddison, C.J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman, S., Grewe, D., Nham, J., Kalchbrenner, N., Sutskever, I., Lillicrap, T., Leach, M., Kavukcuoglu, K., Graepel, T., Hassabis, D. (2016). Mastering the game of go with deep neural networks and tree search. Nature, 529(7587), 484-489. [CrossRef]
35. 35. Chang, A.C. (2020). History of artificial intelligence in medicine. Intelligence-Based Medicine, (pp. 29-42). Academic Press. [CrossRef]
36. 36. Precision Medicine World Conference (PMWC) 2018 Silicon Valley PMWC Precision Medicine World Conferen. Retrieved 01.06.2023, from: https://past.pmwcintl.com/fabien-beckers-2018sv/. Erişim Tarihi: 14.07.2023.
37. 37. Chat GPT. Retrieved 01.06.2023, from: https://chat-gpt.org/tr. Erişim Tarihi: 14.07.2023.
38. 38. Chat GPT. Retrieved 01.06.2023, from: https://openai.com/research/gpt-4. Erişim Tarihi: 14.07.2023.
39. 39. Drug Statics. Retrieved 01.06.2023, from: https://go.drugbank.com/stats. Erişim Tarihi: 14.07.2023.
40. 40. Dalkara, S., Saraç S. 2016, s.148-187 Farmasötik Kimya 1. 4. Baskı, Ankara:Hacettepe Üniversitesi.
41. 41. Tripathi, N., Goshisht, M.K., Sahu, S.K., Arora, C. (2021). Applications of artificial intelligence to drug design and discovery in the big data era: a comprehensive review. Molecular Diversity, 25(3), 1643-1664. [CrossRef]
42. 42. Ashburn, T.T., Thor, K.B. (2004). Drug repositioning: identifying and developing new uses for existing drugs. Nature Reviews Drug Discovery, 3(8), 673-683. [CrossRef]
43. 43. DiMasi, J.A., Grabowski, H.G., Hansen, R.W. (2016). Innovation in the pharmaceutical industry: New estimates of R&D costs. Journal of Health Economics, 47, 20-33. [CrossRef]
44. 44. Domingos, P., Pazzani, M. (1997). On the optimality of the simple bayesian classifer under zero-one loss. Machine Learning, 29(2/3), 103-130. [CrossRef]
45. 45. Hou, T., Wang, J., Li, Y. (2007). Adme evaluation in drug discovery. 8. the prediction of human intestinal absorption by a support vector machine. Journal of Chemical Information and Modeling, 47(6), 2408-2415. [CrossRef]
46. 46. Svetnik, V., Liaw, A., Tong, C., Culberson, J.C., Sheridan, R.P., Feuston, B.P. (2003). Random forest: A classification and regression tool for compound classification and qsar modeling. Journal of Chemical Information and Computer Sciences, 43(6), 1947-1958. [CrossRef]
47. 47. Rayhan, F., Ahmed, S., Shatabda, S., Farid, D.M., Mousavian, Z., Dehzangi, A., Rahman, M.S. (2017). İdti-esboost: identification of drug target interaction using evolutionary and structural features with boosting. Scientific Reports, 7(1), 17731. [CrossRef]
48. 48. Cao, D.S., Xu, Q.S., Liang, Y.Z., Chen, X., Li, H.D. (2010). Automatic feature subset selection for decision tree-based ensemble methods in the prediction of bioactivity. Chemometrics and Intelligent Laboratory Systems, 103(2), 129-136. [CrossRef]
49. 49. Lavecchia, A., Giovanni, C. (2013). Virtual screening strategies in drug discovery: A critical review. Current Medicinal Chemistry, 20(23), 2839-2860. [CrossRef]
50. 50. Hansch, C., Fujita, T. (1964). p -σ-π analysis. a method for the correlation of biological activity and chemical structure. Journal of the American Chemical Society, 86(8), 1616-1626. [CrossRef]
51. 51. Zefirov, N.S., Palyulin, V.A. (2002). Fragmental approach in qsar. Journal of Chemical Information and Computer Sciences, 42(5), 1112-1122. [CrossRef]
52. 52. McGregor, M.J., Muskal, S.M. (1999). Pharmacophore fingerprinting. 1. application to qsar and focused library design. Journal of Chemical Information and Computer Sciences, 39(3), 569-574. [CrossRef]
53. 53. Gozalbes, R., Doucet, J., Derouin, F. (2002). Application of topological descriptors in qsar and drug design: history and new trends. Current Drug Target -Infectious Disorders, 2(1), 93-102. [CrossRef]
54. 54. Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., Kingsbury, B. (2012). Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Processing Magazine, 29(6), 82-97. [CrossRef]
55. 55. Aoyama, T., Suzuki, Y., Ichikawa, H. (1989). Neural network applied to pearmaceutical problems. I. method and application to decision making. Chemical and Pharmaceutical Bulletin, 37(9), 2558-2560. [CrossRef]
56. 56. Tetko, I.V., Villa, A.E.P., Aksenova, T.I., Zielinski, W.L., Brower, J., Collantes, E.R., Welsh, W.J. (1998). Application of a pruning algorithm to optimize artificial neural networks for pharmaceutical fingerprinting. Journal of Chemical Information and Computer Sciences, 38(4), 660-668. [CrossRef]
57. 57. Tetko, I.V., Villa, A.E.P., Livingstone, D.J. (1996). Neural network studies. 2. variable selection. Journal of Chemical Information and Computer Sciences, 36(4), 794-803. [CrossRef]
58. 58. Agatonovic-Kustrin, S., Beresford, R. (2000). Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. Journal of Pharmaceutical and Biomedical Analysis, 22(5), 717-727. [CrossRef]
59. 59. Zhu, H. (2020). Big Data and artificial intelligence modeling for drug discovery. Annual Review of Pharmacology and Toxicology, 60(1), 573-589. [CrossRef]
60. 60. Gawehn, E., Hiss, J.A., Schneider, G. (2016). Deep learning in drug discovery. Molecular Informatics, 35(1), 3-14. [CrossRef]
61. 61. Ma, J., Sheridan, R.P., Liaw, A., Dahl, G.E., Svetnik, V. (2015). Deep neural nets as a method for quantitative structure-activity relationships. Journal of Chemical Information and Modeling, 55(2), 263-274. [CrossRef]
62. 62. Mayr, A., Klambauer, G., Unterthiner, T., Hochreiter, S. (2016). Deeptox: Toxicity prediction using deep learning. Frontiers in Environmental Science, 3. [CrossRef]
63. 63. Wu, Z., Ramsundar, B., Feinberg, E.N., Gomes, J., Geniesse, C., Pappu, A.S., Leswing, K., Pande, V. (2018). Moleculenet: A benchmark for molecular machine learning. Chemical Science, 9(2), 513-530. [CrossRef]
64. 64. Minnich, A.J., McLoughlin, K., Tse, M., Deng, J., Weber, A., Murad, N., Madej, B.D., Ramsundar, B., Rush, T., Calad-Thomson, S., Brase, J., Allen, J.E. (2020). Ampl: a data-driven modeling pipeline for drug discovery. Journal of Chemical Information and Modeling, 60(4), 1955-1968. [CrossRef]
65. 65. Mayr, A., Klambauer, G., Unterthiner, T., Steijaert, M., Wegner, J.K., Ceulemans, H., Clevert, D.-A., Hochreiter, S. (2018). Large-scale comparison of machine learning methods for drug target prediction on Chembl. Chemical Science, 9(24), 5441-5451. [CrossRef]
66. 66. Sheridan, R.P. (2013). Time-split cross-validation as a method for estimating the goodness of prospective prediction. Journal of Chemical Information and Modeling, 53(4), 783-790. [CrossRef]
67. 67. Heffernan, R., Paliwal, K., Lyons, J., Dehzangi, A., Sharma, A., Wang, J., Sattar, A., Yang, Y., Zhou, Y. (2015). Improving prediction of secondary structure, local backbone angles and solvent accessible surface area of proteins by iterative deep learning. Scientific Reports, 5(1), 11476. [CrossRef]
68. 68. 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. [CrossRef]
69. 69. Qi, Y., Oja, M., Weston, J., Noble, W.S. (2012). A unified multitask architecture for predicting local potein properties. PloS One, 7(3), e32235. [CrossRef]
70. 70. Spencer, M., Eickholt, J., Cheng, J. (2015). A deep learning network approach to ab initio protein secondary structure prediction. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 12(1), 103-112. [CrossRef]
71. 71. Wang, S., Peng, J., Ma, J., Xu, J. (2016). Protein secondary structure prediction using deep convolutional neural fields. Scientific Reports, 6(1), 18962. [CrossRef]
72. 72. Jo, T., Hou, J., Eickholt, J., Cheng, J. (2015). Improving protein fold recognition by deep learning networks. Scientific Reports, 5(1), 17573. [CrossRef]
73. 73. Dill, K.A., Ozkan, S.B., Shell, M.S., Weikl, T.R. (2008). The protein folding problem. Annual Review of Biophysics, 37(1), 289-316. [CrossRef]
74. 74. Dill, K.A., MacCallum, J.L. (2012). The protein-folding problem, 50 years on. Science, 338(6110), 1042-1046. [CrossRef]
75. 75. Wang, L., Ding, J., Pan, L., Cao, D., Jiang, H., Ding, X. (2019). Artificial intelligence facilitates drug design in the big data era. Chemometrics and Intelligent Laboratory Systems, 194, 103850. [CrossRef]
76. 76. Senior, A.W., Evans, R., Jumper, J., Kirkpatrick, J., Sifre, L., Green, T., Qin, C., Žídek, A., Nelson, A.W. R., Bridgland, A., Penedones, H., Petersen, S., Simonyan, K., Crossan, S., Kohli, P., Jones, D. T., Silver, D., Kavukcuoglu, K., Hassabis, D. (2020). Improved protein structure prediction using potentials from deep learning. Nature, 577(7792), 706-710. [CrossRef]
77. 77. Tunyasuvunakool, K., Adler, J., Wu, Z., Green, T., Zielinski, M., Žídek, A., Bridgland, A., Cowie, A., Meyer, C., Laydon, A., Velankar, S., Kleywegt, G.J., Bateman, A., Evans, R., Pritzel, A., Figurnov, M., Ronneberger, O., Bates, R., Kohl, S.A.A., Hassabis, D. (2021). Highly accurate protein structure prediction for the human proteome. Nature, 596(7873), 590-596. [CrossRef]
78. 78. Goshisht, M.K., Moudgil, L., Rani, M., Khullar, P., Singh, G., Kumar, H., Singh, N., Kaur, G., Bakshi, M. S. (2014). Lysozyme complexes for the synthesis of functionalized biomaterials to understand protein-protein interactions and their biological applications. The Journal of Physical Chemistry C, 118(48), 28207-28219. [CrossRef]
79. 79. Goshisht, M.K., Moudgil, L., Khullar, P., Singh, G., Kaura, A., Kumar, H., Kaur, G., Bakshi, M.S. (2015). Surface adsorption and molecular modeling of biofunctional gold nanoparticles for systemic circulation and biological sustainability. ACS Sustainable Chemistry & Engineering, 3(12), 3175-3187. [CrossRef]
80. 80. Khullar, P., Goshisht, M.K., Moudgil, L., Singh, G., Mandial, D., Kumar, H., Ahluwalia, G.K., Bakshi, M. S. (2017). Mode of protein complexes on gold nanoparticles surface: Synthesis and characterization of biomaterials for hemocompatibility and preferential dna complexation. ACS Sustainable Chemistry & Engineering, 5(1), 1082-1093. [CrossRef]
81. 81. Mahal, A., Goshisht, M. K., Khullar, P., Kumar, H., Singh, N., Kaur, G., Bakshi, M.S. (2014). Protein mixtures of environmentally friendly zein to understand protein–protein interactions through biomaterials synthesis, hemolysis, and their antimicrobial activities. Phys. Chem. Chem. Phys., 16(27), 14257-14270. [CrossRef]
82. 82. Scott, D.E., Bayly, A.R., Abell, C., Skidmore, J. (2016). Small molecules, big targets: drug discovery faces the protein-protein interaction challenge. Nature Reviews Drug Discovery, 15(8), 533-550. [CrossRef]
83. 83. Azzarito, V., Long, K., Murphy, N.S., Wilson, A.J. (2013). Inhibition of α-helix-mediated protein-protein interactions using designed molecules. Nature Chemistry, 5(3), 161-173. [CrossRef]
84. 84. Rao, V.S., Srinivas, K., Sujini, G.N., Kumar, G.N.S. (2014). Protein-protein interaction detection: methods and analysis. International Journal of Proteomics, 2014, 1-12. [CrossRef]
85. 85. Du, T., Liao, L., Wu, C.H., Sun, B. (2016). Prediction of residue-residue contact matrix for protein-protein interaction with fisher score features and deep learning. Methods, 110, 97-105. [CrossRef]
86. 86. Shin, W.H., Christoffer, C.W., Kihara, D. (2017). In silico structure-based approaches to discover protein-protein interaction-targeting drugs. Methods, 131, 22-32. [CrossRef]
87. 87. Maheshwari, S., Brylinski, M. (2016). Template-based identification of protein-protein interfaces using eFindSitePPI. Methods, 93, 64-71. [CrossRef]
88. 88. Vakser, I.A. (2014). Protein-protein docking: From interaction to interactome. Biophysical Journal, 107(8), 1785-1793. [CrossRef]
89. 89. Mosca, R., Céol, A., Aloy, P. (2013). Interactome3D: Adding structural details to protein networks. Nature Methods, 10(1), 47-53. [CrossRef]
90. 90. Du, X., Sun, S., Hu, C., Yao, Y., Yan, Y., Zhang, Y. (2017). Deepppi: Boosting prediction of protein-protein interactions with deep neural networks. Journal of Chemical Information and Modeling, 57(6), 1499-1510. [CrossRef]
91. 91. Zeng, H., Wang, S., Zhou, T., Zhao, F., Li, X., Wu, Q., Xu, J. (2018). Complexcontact: A web server for inter-protein contact prediction using deep learning. Nucleic Acids Research, 46(W1), W432-W437. [CrossRef]
92. 92. Xie, Z., Deng, X., Shu, K. (2020). Prediction of protein-protein interaction sites using convolutional neural network and improved data sets. International Journal of Molecular Sciences, 21(2), 467. [CrossRef]
93. 93. Rester, U. (2008). From virtuality to reality-virtual screening in lead discovery and lead optimization: A medicinal chemistry perspective. Current Opinion in Drug Discovery and Development, 11(4), 559-568.
94. 94. Walters, W.P., Stahl, M.T., Murcko, M.A. (1998). Virtual screening-an overview. Drug Discovery Today, 3(4), 160-178. [CrossRef]
95. 95. Gonczarek, A., Tomczak, J.M., Zaręba, S., Kaczmar, J., Dąbrowski, P., Walczak, M.J. (2018). Interaction prediction in structure-based virtual screening using deep learning. Computers in Biology and Medicine, 100, 253-258. [CrossRef]
96. 96. Plewczynski, D., Spieser, S., Koch, U. (2009). Performance of machine learning methods for ligand-based virtual screening. Combinatorial Chemistry & High Throughput Screening, 12(4), 358-368. [CrossRef]
97. 97. Bohacek, R.S., McMartin, C., Guida, W.C. (1996). The art and practice of structure-based drug design: A molecular modeling perspective. Medicinal Research Reviews, 16(1), 3-50. [CrossRef]
98. 98. Xiao, T., Qi, X., Chen, Y., Jiang, Y. (2018). Development of ligand-based big data deep neural network models for virtual screening of large compound libraries. Molecular Informatics, 37(11), 1800031. [CrossRef]
99. 99. Ferreira, L., dos Santos, R., Oliva, G., Andricopulo, A. (2015). Molecular docking and structure-based drug design strategies. Molecules, 20(7), 13384-13421. [CrossRef]
100. 100. Akbar, R., Jusoh, S.A., Amaro, R.E., Helms, V. (2017). Enri: A tool for selecting structure-based virtual screening target conformations. Chemical Biology and Drug Design, 89(5), 762-771. [CrossRef]
101. 101. Cheng, T., Li, Q., Zhou, Z., Wang, Y., Bryant, S.H. (2012). Strcture-based virtual screening for drug discovery: A problem-centric review. The AAPS Journal, 14(1), 133-141. [CrossRef]
102. 102. Bengio, Y., Courville, A., Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1798-1828. [CrossRef]
103. 103. Pereira, J.C., Caffarena, E.R., dos Santos, C.N. (2016). Boosting docking-based virtual screening with deep learning. Journal of Chemical Information and Modeling, 56(12), 2495-2506. [CrossRef]
104. 104. 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), 182. [CrossRef]
105. 105. DeepCodex: A deep code for gene expression data. Retrieved 01.06.2023, from: http://deepcodex.org Erişim Tarihi: 14.07.2023.
106. 106. Donner, Y., Kazmierczak, S., Fortney, K. (2018). Drug repurposing using deep embeddings of gene expression profiles. Molecular Pharmaceutics, 15(10), 4314-4325. [CrossRef]
107. 107. Duan, Q., Flynn, C., Niepel, M., Hafner, M., Muhlich, J.L., Fernandez, N.F., Rouillard, A.D., Tan, C.M., Chen, E.Y., Golub, T.R., Sorger, P.K., Subramanian, A., Ma’ayan, A. (2014). Lincs canvas browser: Interactive web app to query, browse and interrogate lincs l1000 gene expression signatures. Nucleic Acids Research, 42(W1), W449-W460. [CrossRef]
108. 108. 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(S7), 667. [CrossRef]
109. 109. 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. [CrossRef]
110. 110. Skalic, M., Martínez-Rosell, G., Jiménez, J., De Fabritiis, G. (2019). Playmolecule bindscope: Large scale cnn-based virtual screening on the web. Bioinformatics, 35(7), 1237-1238. [CrossRef]
111. 111. Mendolia, I., Contino, S., Perricone, U., Ardizzone, E., Pirrone, R. (2020). Convolutional architectures for virtual screening. BMC Bioinformatics, 21(S8), 310. [CrossRef]
112. 112. Esposito, E.X., Hopfinger, A.J., Madura, J.D. (2004). Methods for applying the quantitative structure-activity relationship paradigm (pp. 131-213). [CrossRef]
113. 113. Myint, K.Z., Xie, X.Q. (2010). Recent advances in fragment-based qsar and multi-dimensional qsar methods. International Journal of Molecular Sciences, 11(10), 3846-3866. [CrossRef]
114. 114. Lei, T., Li, Y., Song, Y., Li, D., Sun, H., Hou, T. (2016). Admet evaluation in drug discovery: 15. accurate prediction of rat oral acute toxicity using relevance vector machine and consensus modeling. Journal of Cheminformatics, 8(1), 6. [CrossRef]
115. 115. Aoyama, T., Suzuki, Y., Ichikawa, H. (1990). Neural networks applied to pharmaceutical problems. III. Neural networks applied to quantitative structure-activity relationship (QSAR) analysis. Journal of Medicinal Chemistry, 33(9), 2583-2590. [CrossRef]
116. 116. Dobchev, D., Pillai, G., Karelson, M. (2014). In silico machine learning methods in drug development. Current Topics in Medicinal Chemistry, 14(16), 1913-1922. [CrossRef]
117. 117. Dong, J., Yao, Z.J., Zhu, M.F., Wang, N.N., Lu, B., Chen, A.F., Lu, A.P., Miao, H., Zeng, W.B., Cao, D.S. (2017). Chemsar: An online pipelining platform for molecular sar modeling. Journal of Cheminformatics, 9(1), 27. [CrossRef]
118. 118. Dahl, G.E., Jaitly, N., Salakhutdinov, R. (2014). Multi-task Neural Networks for QSAR Predictions.
119. 119. Tenorio-Borroto, E., Peñuelas Rivas, C.G., Vásquez Chagoyán, J.C., Castañedo, N., Prado-Prado, F.J., García-Mera, X., González-Díaz, H. (2012). Ann multiplexing model of drugs effect on macrophages; theoretical and flow cytometry study on the cytotoxicity of the anti-microbial drug gi in spleen. Bioorganic Medicinal Chemistry, 20(20), 6181-6194. [CrossRef]
120. 120. Tenorio-Borroto, E., Peñuelas-Rivas, C.G., Vásquez-Chagoyán, J.C., Castañedo, N., Prado-Prado, F.J., García-Mera, X., González-Díaz, H. (2014). Model for high-throughput screening of drug immunotoxicity-study of the anti-microbial gi over peritoneal macrophages using flow cytometry. European Journal of Medicinal Chemistry, 72, 206-220. [CrossRef]
121. 121. Speck-Planche, A., Cordeiro, M. (2013). Simultaneous modeling of antimycobacterial activities and admet profiles: a chemoinformatic approach to medicinal chemistry. Current Topics in Medicinal Chemistry, 13(14), 1656-1665. [CrossRef]
122. 122. Speck-Planche, A., Dias Soeiro Cordeiro, M.N. (2017). Speeding up early drug discovery in antiviral research: a fragment-based in silico approach for the design of virtual anti-hepatitis C leads. ACS Combinatorial Science, 19(8), 501-512. [CrossRef]
123. 123. Ramsundar, B., Kearnes, S., Riley, P., Webster, D., Konerding, D., Pande, V. (2015). Massively Multitask Networks for Drug Discovery.
124. 124. Xu, Y., Ma, J., Liaw, A., Sheridan, R.P., Svetnik, V. (2017). Demystifying multitask deep neural networks for quantitative structure-activity relationships. Journal of Chemical Information and Modeling, 57(10), 2490-2504. [CrossRef]
125. 125. Zhao, Z., Qin, J., Gou, Z., Zhang, Y., Yang, Y. (2020). Multi-task learning models for predicting active compounds. Journal of Biomedical Informatics, 108, 103484. [CrossRef]
126. 126. Kharkar, P. (2010). Two-dimensional (2D) in silico models for absorption, distribution, metabolism, excretion and toxicity (ADME/T) in drug discovery. Current Topics in Medicinal Chemistry, 10(1), 116-126. [CrossRef]
127. 127. Wang, Y., Xing, J., Xu, Y., Zhou, N., Peng, J., Xiong, Z., Liu, X., Luo, X., Luo, C., Chen, K., Zheng, M., Jiang, H. (2015). In silico ADME/T modelling for rational drug design. Quarterly Reviews of Biophysics, 48(4), 488-515. [CrossRef]
128. 128. Xue, H., Li, J., Xie, H., Wang, Y. (2018). Review of drug repositioning approaches and resources. International Journal of Biological Sciences, 14(10), 1232-1244. [CrossRef]
129. 129. Kennedy, T. (1997). Managing the drug discovery/development interface. Drug Discovery Today, 2(10), 436-444. [CrossRef]
130. 130. Merlot, C. (2010). Computational toxicology-a tool for early safety evaluation. Drug Discovery Today, 15(1-2), 16-22. [CrossRef]
131. 131. Khanna, I. (2012). Drug discovery in pharmaceutical industry: productivity challenges and trends. Drug Discovery Today, 17(19-20), 1088-1102. [CrossRef]
132. 132. Tan, J.J., Cong, X.J., Hu, L.M., Wang, C.X., Jia, L., Liang, X.J. (2010). Therapeutic strategies underpinning the development of novel techniques for the treatment of HIV infection. Drug Discovery Today, 15(5-6), 186-197. [CrossRef]
133. 133. 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. [CrossRef]
134. 134. Kortagere, S., Chekmarev, D., Welsh, W.J., Ekins, S. (2008). New predictive models for blood-brain barrier permeability of drug-like molecules. Pharmaceutical Research, 25(8), 1836-1845. [CrossRef]
135. 135. Obrezanova, O., Csányi, G., Gola, J.M.R., Segall, M.D. (2007). Gaussian processes: A method for automatic qsar modeling of adme properties. Journal of Chemical Information and Modeling, 47(5), 1847-1857. [CrossRef]
136. 136. Lombardo, F., Obach, R.S., DiCapua, F.M., Bakken, G.A., Lu, J., Potter, D.M., Gao, F., Miller, M.D., Zhang, Y. (2006). A hybrid mixture discriminant analysis-random forest computational model for the prediction of volume of distribution of drugs in human. Journal of Medicinal Chemistry, 49(7), 2262-2267. [CrossRef]
137. 137. Klon, A.E., Lowrie, J.F., Diller, D.J. (2006). Improved natïve bayesian modeling of numerical data for absorption, distribution, metabolism and excretion (ADME) property prediction. Journal of Chemical Information and Modeling, 46(5), 1945-1956. [CrossRef]
138. 138. 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. [CrossRef]
139. 139. Krewski, D., Acosta, D., Andersen, M., Anderson, H., Bailar, J.C., Boekelheide, K., Brent, R., Charnley, G., Cheung, V.G., Green, S., Kelsey, K.T., Kerkvliet, N.I., Li, A.A., McCray, L., Meyer, O., Patterson, R.D., Pennie, W., Scala, R.A., Solomon, G.M., Staff of Committee on Toxicity Test. (2010). Toxicity testing in the 21st century: A vision and a strategy. Journal of Toxicology and Environmental Health, Part B, 13(2-4), 51-138. [CrossRef]
140. 140. Clark, A.M., Dole, K., Coulon-Spektor, A., McNutt, A., Grass, G., Freundlich, J.S., Reynolds, R.C., Ekins, S. (2015). Open source bayesian models. 1. application to adme/tox and drug discovery datasets. Journal of Chemical Information and Modeling, 55(6), 1231-1245. [CrossRef]
141. 141. Wenlock, M.C., Carlsson, L.A. (2015). How experimental errors influence drug metabolism and pharmacokinetic qsar/qspr models. Journal of Chemical Information and Modeling, 55(1), 125-134. [CrossRef]
142. 142. Hughes, T.B., Miller, G.P., Swamidass, S.J. (2015). Modeling epoxidation of drug-like molecules with a deep machine learning network. ACS Central Science, 1(4), 168-180. [CrossRef]
143. 143. Xu, Y., Dai, Z., Chen, F., Gao, S., Pei, J., Lai, L. (2015). Deep learning for drug-induced liver injury. Journal of Chemical Information and Modeling, 55(10), 2085-2093. [CrossRef]
144. 144. 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., van Dyk, E., Chang, H., de Silva, H., Heyn, H., Deng, X., Egan, R.K., Liu, Q., Garnett, M.J. (2016). A landscape of pharmacogenomic interactions in cancer. Cell, 166(3), 740-754. [CrossRef]
145. 145. 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. [CrossRef]
146. 146. 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. [CrossRef]
147. 147. 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), 64. [CrossRef]
148. 148. Korotcov, A., Tkachenko, V., Russo, D.P., Ekins, S. (2017). Comparison of deep learning with multiple machine learning methods and metrics using diverse drug discovery data sets. Molecular Pharmaceutics, 14(12), 4462-4475. [CrossRef]
149. 149. Ramsundar, B., Liu, B., Wu, Z., Verras, A., Tudor, M., Sheridan, R.P., Pande, V. (2017). Is multitask deep learning practical for pharma? Journal of Chemical Information and Modeling, 57(8), 2068-2076. [CrossRef]
150. 150. Altae-Tran, H., Ramsundar, B., Pappu, A.S., Pande, V. (2017). Low data drug discovery with one-shot learning. ACS Central Science, 3(4), 283-293. [CrossRef]
151. 151. Li, X., Xu, Y., Lai, L., Pei, J. (2018). Prediction of human cytochrome P450 inhibition using a multitask deep autoencoder neural network. Molecular Pharmaceutics, 15(10), 4336-4345. [CrossRef]
152. 152. Wenzel, J., Matter, H., Schmidt, F. (2019). Predictive multitask deep neural network models for adme-tox properties: Learning from large data sets. Journal of Chemical Information and Modeling, 59(3), 1253-1268. [CrossRef]
153. 153. Novac, N. (2013). Challenges and opportunities of drug repositioning. Trends in Pharmacological Sciences, 34(5), 267-272. [CrossRef]
154. 154. Chen, X., Yan, C.C., Zhang, X., Zhang, X., Dai, F., Yin, J., Zhang, Y. (2016). Drug-target interaction prediction: Databases, web servers and computational models. Briefings in Bioinformatics, 17(4), 696-712. [CrossRef]
155. 155. Durán, F., Alonso, N., Caamaño, O., García-Mera, X., Yañez, M., Prado-Prado, F., González-Díaz, H. (2014). Prediction of multi-target networks of neuroprotective compounds with entropy indices and synthesis, assay, and theoretical study of new asymmetric 1,2-rasagiline carbamates. International Journal of Molecular Sciences, 15(9), 17035-17064. [CrossRef]
156. 156. Kitchen, D.B., Decornez, H., Furr, J.R., Bajorath, J. (2004). Docking and scoring in virtual screening for drug discovery: Methods and applications. Nature Reviews Drug Discovery, 3(11), 935-949. [CrossRef]
157. 157. Cao, D.S., Liu, S., Xu, Q.S., Lu, H.M., Huang, J.H., Hu, Q.N., Liang, Y.Z. (2012). Large-scale prediction of drug-target interactions using protein sequences and drug topological structures. Analytica Chimica Acta, 752, 1-10. [CrossRef]
158. 158. Yao, Z.J., Dong, J., Che, Y.J., Zhu, M.F., Wen, M., Wang, N.N., Wang, S., Lu, A.P., Cao, D.S. (2016). Targetnet: A web service for predicting potential drug-target interaction profiling via multi-target sar models. Journal of Computer-Aided Molecular Design, 30(5), 413-424. [CrossRef]
159. 159. Ding, H., Takigawa, I., Mamitsuka, H., Zhu, S. (2014). Similarity-based machine learning methods for predicting drug-target interactions: A brief review. Briefings in Bioinformatics, 15(5), 734-747. [CrossRef]
160. 160. Cao, D.S., Zhang, L.X., Tan, G.S., Xiang, Z., Zeng, W.B., Xu, Q.S., Chen, A.F. (2014). Computational prediction of drug-target interactions using chemical, biological, and network features. Molecular Informatics, 33(10), 669-681. [CrossRef]
161. 161. Byvatov, E., Fechner, U., Sadowski, J., Schneider, G. (2003). Comparison of support vector machine and artificial neural network systems for drug/nondrug classification. Journal of Chemical Information and Computer Sciences, 43(6), 1882-1889. [CrossRef]
162. 162. Romero-Durán, F.J., Alonso, N., Yañez, M., Caamaño, O., García-Mera, X., González-Díaz, H. (2016). Brain-inspired cheminformatics of drug-target brain interactome, synthesis, and assay of TVP1022 derivatives. Neuropharmacology, 103, 270-278. [CrossRef]
163. 163. Wen, M., Zhang, Z., Niu, S., Sha, H., Yang, R., Yun, Y., Lu, H. (2017). Deep-learning-based drug-target interaction prediction. Journal of Proteome Research, 16(4), 1401-1409. [CrossRef]
164. 164. Luo, Y., Zhao, X., Zhou, J., Yang, J., Zhang, Y., Kuang, W., Peng, J., Chen, L., Zeng, J. (2017). A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information. Nature Communications, 8(1), 573. [CrossRef]
165. 165. Luo, J. (2016). Crisp/Cas9: From genome engineering to cancer drug discovery. Trends in Cancer, 2(6), 313-324. [CrossRef]
166. 166. Scott, A. (2018). How crispr is transforming drug discovery. Nature, 555(7695), S10-S11. [CrossRef]
167. 167. 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. [CrossRef]
168. 168. Schneider, G., Fechner, U. (2005). Computer-based de novo design of drug-like molecules. Nature Reviews Drug Discovery, 4(8), 649-663. [CrossRef]
169. 169. Bohm, H.J. (1992). The computer program ludi: A new method for the de novo design of enzyme inhibitors. Journal of Computer-Aided Molecular Design, 6(1), 61-78. [CrossRef]
170. 170. Schneider, G., Geppert, T., Hartenfeller, M., Reisen, F., Klenner, A., Reutlinger, M., Hähnke, V., Hiss, J. A., Zettl, H., Keppner, S., Spänkuch, B., Schneider, P. (2011). Reaction-driven de novo design, synthesis and testing of potential type II kinase inhibitors. Future Medicinal Chemistry, 3(4), 415-424. [CrossRef]
171. 171. Besnard, J., Ruda, G. F., Setola, V., Abecassis, K., Rodriguiz, R.M., Huang, X.P., Norval, S., Sassano, M. F., Shin, A.I., Webster, L.A., Simeons, F.R.C., Stojanovski, L., Prat, A., Seidah, N.G., Constam, D.B., Bickerton, G.R., Read, K.D., Wetsel, W.C., Gilbert, I.H., Hopkins, A.L. (2012). Automated design of ligands to polypharmacological profiles. Nature, 492(7428), 215-220. [CrossRef]
172. 172. Miyao, T., Kaneko, H., Funatsu, K. (2016). Inverse qspr/qsar analysis for chemical structure generation (from y to x). Journal of Chemical Information and Modeling, 56(2), 286-299. [CrossRef]
173. 173. Olivecrona, M., Blaschke, T., Engkvist, O., Chen, H. (2017). Molecular de-novo design through deep reinforcement learning. Journal of Cheminformatics, 9(1), 48. [CrossRef]
174. 174. Kadurin, A., Nikolenko, S., Khrabrov, K., Aliper, A., Zhavoronkov, A. (2017). druGAN: An advanced generative adversarial autoencoder model for de novo generation of new molecules with desired molecular properties in silico. Molecular Pharmaceutics, 14(9), 3098-3104. [CrossRef]
175. 175. Gómez-Bombarelli, R., Wei, J.N., Duvenaud, D., Hernández-Lobato, J.M., Sánchez-Lengeling, B., Sheberla, D., Aguilera-Iparraguirre, J., Hirzel, T.D., Adams, R.P., Aspuru-Guzik, A. (2018). Automatic chemical design using a data-driven continuous representation of molecules. ACS Central Science, 4(2), 268-276. [CrossRef]
176. 176. 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., Aspuru-Guzik, A. (2019). Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nature Biotechnology, 37(9), 1038-1040. [CrossRef]
177. 177. Skalic, M., Jiménez, J., Sabbadin, D., De Fabritiis, G. (2019). Shape-based generative modeling for de novo drug design. Journal of Chemical Information and Modeling, 59(3), 1205-1214. [CrossRef]
178. 178. 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. [CrossRef]
179. 179. 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. [CrossRef]
180. 180. 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. [CrossRef]
181. 181. 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., Glatzel, M. (2015). The sheddase ADAM10 is a potent modulator of prion disease. ELife, 4. [CrossRef]
182. 182. 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. The Journal of Neuroscience, 29(14), 4605-4615. [CrossRef]
183. 183. 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. [CrossRef]
184. 184. 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. [CrossRef]
185. 185. 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. [CrossRef]
186. 186. Szymkuć, S., Gajewska, E.P., Klucznik, T., Molga, K., Dittwald, P., Startek, M., Bajczyk, M., Grzybowski, B.A. (2016). Computer‐assisted synthetic planning: The end of the beginning. Angewandte Chemie International Edition, 55(20), 5904-5937. [CrossRef]
187. 187. Segler, M H.S., Preuss, M., Waller, M.P. (2018). Planning chemical syntheses with deep neural networks and symbolic ai. Nature, 555(7698), 604-610. [CrossRef]
188. 188. Coley, C.W., Green, W.H., Jensen, K.F. (2018). Machine learning in computer-aided synthesis planning. Accounts of Chemical Research, 51(5), 1281-1289. [CrossRef]
189. 189. Button, A., Merk, D., Hiss, J.A., Schneider, G. (2019). Automated de novo molecular design by hybrid machine intelligence and rule-driven chemical synthesis. Nature Machine Intelligence, 1(7), 307-315. [CrossRef]
190. 190. Yuan, W., Jiang, D., Nambiar, D.K., Liew, L.P., Hay, M.P., Bloomstein, J., Lu, P., Turner, B., Le, Q.T., Tibshirani, R., Khatri, P., Moloney, M.G., Koong, A.C. (2017). Chemical space mimicry for drug discovery. Journal of Chemical Information and Modeling, 57(4), 875-882. [CrossRef]
191. 191. 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. [CrossRef]
192. 192. Merk, D., Friedrich, L., Grisoni, F., Schneider, G. (2018). De novo design of bioactive small molecules by artificial intelligence. Molecular Informatics, 37(1-2), 1700153. [CrossRef]
193. 193. Papadatos, G., Gaulton, A., Hersey, A., Overington, J.P. (2015). Activity, assay and target data curation and quality in the Chembl database. Journal of Computer-Aided Molecular Design, 29(9), 885-896. [CrossRef]
194. 194. Olley, D. (ed.) Artificial intelligence: How knowledge is created, transferred, and used (Elsevier, 2019).
195. 195. Perron, Q. Deep learning for ligand-based de novo design in lead optimization: A real life case study. Presented at the XXV EFMC International Symposium on Medicinal Chemistry (2018).
196. 196. Rodrigues, T., Hauser, N., Reker, D., Reutlinger, M., Wunderlin, T., Hamon, J., Koch, G., Schneider, G. (2015). Multidimensional de novo design reveals 5-HT2B receptor-selective ligands. Angewandte Chemie International Edition, 54(5), 1551-1555. [CrossRef]
197. 197. Reutlinger, M., Rodrigues, T., Schneider, P., Schneider, G. (2014). Multi-objective molecular de novo design by adaptive fragment prioritization. Angewandte Chemie International Edition, 53(16), 4244-4248. [CrossRef]
198. 198. Gao, K., Nguyen, D.D., Tu, M., Wei, G.W. (2020). Generative network complex for the automated generation of drug-like molecules. Journal of Chemical Information and Modeling, 60(12), 5682-5698. [CrossRef]
199. 199. Trobe, M., Burke, M.D. (2018). The molecular industrial revolution: Automated synthesis of small molecules. Angewandte Chemie International Edition, 57(16), 4192-4214. [CrossRef]
200. 200. Baranczak, A., Tu, N.P., Marjanovic, J., Searle, P.A., Vasudevan, A., Djuric, S.W. (2017). Integrated platform for expedited synthesis-purification-testing of small molecule libraries. ACS Medicinal Chemistry Letters, 8(4), 461-465. [CrossRef]
201. 201. Cox, G., Sieron, A., King, A.M., De Pascale, G., Pawlowski, A.C., Koteva, K., Wright, G.D. (2017). A common platform for antibiotic dereplication and adjuvant discovery. Cell Chemical Biology, 24(1), 98-109. [CrossRef]
202. 202. Camacho, D.M., Collins, K.M., Powers, R.K., Costello, J.C., Collins, J.J. (2018). Next-generation machine learning for biological networks. Cell, 173(7), 1581-1592. [CrossRef]
203. 203. De, S.K., Stebbins, J.L., Chen, L.H., Riel-Mehan, M., Machleidt, T., Dahl, R., Yuan, H., Emdadi, A., Barile, E., Chen, V., Murphy, R., Pellecchia, M. (2009). Design, synthesis, and structure-activity relationship of substrate competitive, selective, and in vivo active triazole and thiadiazole inhibitors of the c-jun n-terminal kinase. Journal of Medicinal Chemistry, 52(7), 1943-1952. [CrossRef]
204. 204. Stokes, J.M., Yang, K., Swanson, K., Jin, W., Cubillos-Ruiz, A., Donghia, N.M., MacNair, C.R., French, S., Carfrae, L.A., Bloom-Ackermann, Z., Tran, V.M., Chiappino-Pepe, A., Badran, A.H., Andrews, I.W., Chory, E.J., Church, G.M., Brown, E.D., Jaakkola, T.S., Barzilay, R., Collins, J.J. (2020). A deep learning approach to antibiotic discovery. Cell, 180(4), 688-702.e13. [CrossRef]
205. 205. Malandraki-Miller, S., Riley, P.R. (2021). Use of artificial intelligence to enhance phenotypic drug discovery. Drug Discovery Today, 26(4), 887-901. [CrossRef]
206. 206. DSP-1181. Retrieved 14.07.2023, from: https://www.exscientia.ai/dsp-1181.
207. 207. Wills T. AI drug discovery: Assessing the first AI-designed drug candidates to go into human clinical trials, CAS, 2022. Retrieved 14.07.2023, from: https://www.cas.org/resources/cas-insights/drug-discovery/ai-designed-drug-candidates.