Epigenetic target identification strategy based on multi-feature learning

References

• Al-Shar I, N. A., & Musleh, S. (2020). Identification of CHK1 kinase inhibitors using structure-based pharmacophore modeling and molecular docking. Indian Journal of Pharmaceutical Sciences, 82(3), 472-482. https://doi.org/10.36468/pharmaceutical-sciences.670
   Web of Science ®Google Scholar
• Altman, N. S. (1992). An introduction to kernel and nearest-neighbor nonparametric regression. American Statistician, 46(3), 175. https://doi.org/10.2307/2685209
   Web of Science ®Google Scholar
• Biswas, S., & Rao, C. M. (2018). Epigenetic tools (The Writers, The Readers and The Erasers) and their implications in cancer therapy. European Journal of Pharmacology, 837, 8–24. https://doi.org/10.1016/j.ejphar.2018.08.021
   PubMed Web of Science ®Google Scholar
• Burgess, R. J., & Zhang, Z. (2013). Histone chaperones in nucleosome assembly and human disease. Nature Structural & Molecular Biology, 20(1), 14–22. https://doi.org/10.1038/nsmb.2461
   PubMed Web of Science ®Google Scholar
• Chadha, N., & Silakari, O. (2016). Active site fingerprinting and pharmacophore screening strategies for the identification of dual inhibitors of protein kinase C (RhoKappaCbeta) and poly (ADP-ribose) polymerase-1 (PARP-1). Molecular Diversity, 20(3), 747–761. https://doi.org/10.1007/s11030-016-9676-9
   PubMed Web of Science ®Google Scholar
• Chang, X., Zhang, T., Wang, Q., Rathore, M. G., Reddy, K., Chen, H., Shin, S. H., Ma, W. Y., Bode, A. M., & Dong, Z. (2020). HI-511 overcomes melanoma drug resistance via targeting AURKB and BRAF V600E. Theranostics, 10(21), 9721–9740. https://doi.org/10.7150/thno.44342
   PubMed Web of Science ®Google Scholar
• Chen, X., Liu, H., Xie, W., Yang, Y., Wang, Y., Fan, Y., Hua, Y., Zhu, L., Zhao, J., Lu, T., Chen, Y., & Zhang, Y. (2019). Investigation of crystal structures in structure-based virtual screening for protein kinase inhibitors. Journal of Chemical Information and Modeling, 59(12), 5244–5262. https://doi.org/10.1021/acs.jcim.9b00684
   PubMed Web of Science ®Google Scholar
• Chen, J., Li, Y., Zhang, J., Zhang, M., Wei, A., Liu, H., Xie, Z., Ren, W., Duan, W., Zhang, Z., Shen, A., & Hu, Y. (2021). Discovery of selective HDAC/BRD4 dual inhibitors as epigenetic probes. European Journal of Medicinal Chemistry, 209, 112868. https://doi.org/10.1016/j.ejmech.2020.112868
   PubMed Web of Science ®Google Scholar
• Chen, S., Wang, Y., Zhou, W., Li, S., Peng, J., Shi, Z., Hu, J., Liu, Y. C., Ding, H., Lin, Y., Li, L., Cheng, S., Liu, J., Lu, T., Jiang, H., Liu, B., Zheng, M., & Luo, C. (2014). Identifying novel selective non-nucleoside DNA methyltransferase 1 inhibitors through docking-based virtual screening. Journal of Medicinal Chemistry, 57(21), 9028–9041. https://doi.org/10.1021/jm501134e
   PubMed Web of Science ®Google Scholar
• Chollet, F. A. O. (2018). Keras: The Python deep learning library.
   Google Scholar
• Çınaroğlu, S. S., & Timuçin, E. (2020). Comprehensive evaluation of the MM-GBSA method on bromodomain-inhibitor sets. Briefings in Bioinformatics, 21(6), 2112–2125. https://doi.org/10.1093/bib/bbz143
   PubMed Web of Science ®Google Scholar
• Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297. https://doi.org/10.1007/BF00994018
   Web of Science ®Google Scholar
• Daina, A., Michielin, O., & Zoete, V. (2019). SwissTargetPrediction: Updated data and new features for efficient prediction of protein targets of small molecules. Nucleic Acids Research, 47(W1), W357–W364. https://doi.org/10.1093/nar/gkz382
   PubMed Web of Science ®Google Scholar
• Deans, C., & Maggert, K. A. (2015). What do you mean, "epigenetic"? Genetics, 199(4), 887–896. https://doi.org/10.1534/genetics.114.173492
   PubMed Web of Science ®Google Scholar
• Dittrich, J., Schmidt, D., Pfleger, C., & Gohlke, H. (2019). Converging a knowledge-based scoring function: DrugScore(2018). Journal of Chemical Information and Modeling, 59(1), 509–521. https://doi.org/10.1021/acs.jcim.8b00582
   PubMed Web of Science ®Google Scholar
• Durant, J. L., Leland, B. A., Henry, D. R., & Nourse, J. G. (2002). Reoptimization of MDL keys for use in drug discovery. Journal of Chemical Information and Computer Sciences, 42(6), 1273–1280. https://doi.org/10.1021/ci010132r
   PubMedGoogle Scholar
• Elmezayen, A. D., & Kemal, Y. (2021). Structure-based virtual screening for novel potential selective inhibitors of class IIa histone deacetylases for cancer treatment. Computational Biology and Chemistry, 92, 107491. https://doi.org/10.1016/j.compbiolchem.2021.107491
   PubMed Web of Science ®Google Scholar
• Fearn, T. (2014). Probabilistic principal component analysis. NIR News, 25(3), 23–23. https://doi.org/10.1255/nirn.1439
   Google Scholar
• Franci, G., Sarno, F., Nebbioso, A., & Altucci, L. (2017). Identification and characterization of PKF118-310 as a KDM4A inhibitor. Epigenetics, 12(3), 198–205. https://doi.org/10.1080/15592294.2016.1249089
   PubMed Web of Science ®Google Scholar
• Ganesan, A., Arimondo, P. B., Rots, M. G., Jeronimo, C., & Berdasco, M. (2019). The timeline of epigenetic drug discovery: From reality to dreams. Clinical Epigenetics, 11(1), 174. https://doi.org/10.1186/s13148-019-0776-0
   PubMed Web of Science ®Google Scholar
• Golbraikh, A., Muratov, E., Fourches, D., & Tropsha, A. (2014). Data set modelability by QSAR. Journal of Chemical Information and Modeling, 54(1), 1–4. https://doi.org/10.1021/ci400572x
   PubMed Web of Science ®Google Scholar
• Hameduh, T., Haddad, Y., Adam, V., & Heger, Z. (2020). Homology modeling in the time of collective and artificial intelligence. Computational and Structural Biotechnology Journal, 18, 3494–3506. https://doi.org/10.1016/j.csbj.2020.11.007
   PubMed Web of Science ®Google Scholar
• Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences of the United States of America, 79(8), 2554–2558. https://doi.org/10.1073/pnas.79.8.2554
   PubMed Web of Science ®Google Scholar
• Hoy, S. M. (2020). Tazemetostat: First approval. Drugs, 80(5), 513–521. https://doi.org/10.1007/s40265-020-01288-x
   PubMed Web of Science ®Google Scholar
• Huang, S. H., Cao, R., Lin, Q. W., Wu, S. Q., Gao, L. L., Sun, Q., Zhu, Q. H., Zou, Y., Xu, Y. G., & Wang, S. P. (2022). Design, synthesis and mechanism studies of novel dual PARP1/BRD4 inhibitors against pancreatic cancer. European Journal of Medicinal Chemistry, 230, 114116. https://doi.org/10.1016/j.ejmech.2022.114116
   PubMed Web of Science ®Google Scholar
• Huang, Z., Jiang, H., Liu, X., Chen, Y., Wong, J., Wang, Q., Huang, W., Shi, T., & Zhang, J. (2012). HEMD: An integrated tool of human epigenetic enzymes and chemical modulators for therapeutics. PloS One, 7(6), e39917. https://doi.org/10.1371/journal.pone.0039917
   PubMed Web of Science ®Google Scholar
• Kam, T. (1998). The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(8), 832–844.
   Web of Science ®Google Scholar
• Kharkar, P. S., Warrier, S., & Gaud, R. S. (2014). Reverse docking: A powerful tool for drug repositioning and drug rescue. Future Medicinal Chemistry, 6(3), 333–342. https://doi.org/10.4155/fmc.13.207
   PubMed Web of Science ®Google Scholar
• Klopman, G., & Kalos, A. N. (1985). Causality in structure activity studies. Journal of Computational Chemistry, 6(5), 492–506. https://doi.org/10.1002/jcc.540060520
   Web of Science ®Google Scholar
• Kong, Y., Bender, A., & Yan, A. (2018). Identification of novel aurora kinase A (AURKA) inhibitors via hierarchical ligand-based virtual screening. Journal of Chemical Information and Modeling, 58(1), 36–47. https://doi.org/10.1021/acs.jcim.7b00300
   PubMed Web of Science ®Google Scholar
• Kozakov, D., Grove, L. E., Hall, D. R., Bohnuud, T., Mottarella, S. E., Luo, L., Xia, B., Beglov, D., & Vajda, S. (2015). The FTMap family of web servers for determining and characterizing ligand-binding hot spots of proteins. Nature Protocols, 10(5), 733–755. https://doi.org/10.1038/nprot.2015.043
   PubMed Web of Science ®Google Scholar
• Kumar, V., Parate, S., Thakur, G., Lee, G., Ro, H. S., Kim, Y., Kim, H. J., Kim, M. O., & Lee, K. W. (2021). Identification of CDK7 inhibitors from natural sources using pharmacoinformatics and molecular dynamics simulations. Biomedicines, 9(9), 1197. https://doi.org/10.3390/biomedicines9091197
   PubMed Web of Science ®Google Scholar
• Landrum, G. (2019). RDKit: Open-source cheminformatics from machine learning to chemical registration. Abstracts of Papers of the American Chemical Society, 258.
   Web of Science ®Google Scholar
• Li, J., Fu, A., & Zhang, L. (2019). An overview of scoring functions used for protein-ligand interactions in molecular docking. Interdisciplinary Sciences, Computational Life Sciences, 11(2), 320–328. https://doi.org/10.1007/s12539-019-00327-w
   PubMed Web of Science ®Google Scholar
• Lionta, E., Spyrou, G., Vassilatis, D., & Cournia, Z. (2014). Structure-based virtual screening for drug discovery: Principles, applications and recent advances. Current Topics in Medicinal Chemistry, 14(16), 1923–1938. https://doi.org/10.2174/1568026614666140929124445
   PubMed Web of Science ®Google Scholar
• Liu, H., Hwang, J., Li, W., Choi, T. W., Liu, K., Huang, Z., Jang, J. H., Thimmegowda, N. R., Lee, K. W., Ryoo, I. J., Ahn, J. S., Bode, A. M., Zhou, X., Yang, Y., Erikson, R. L., Kim, B. Y., & Dong, Z. (2014). A derivative of chrysin suppresses two-stage skin carcinogenesis by inhibiting mitogen- and stress-activated kinase 1. Cancer Prevention Research (Philadelphia, Pa.), 7(1), 74–85. https://doi.org/10.1158/1940-6207.CAPR-13-0133
   PubMed Web of Science ®Google Scholar
• Liu, S., Zhou, X., Li, M., Zhao, W., Zhou, S., Cheng, K., Xu, Q., Chen, C., Wen, X., Sun, H., & Yuan, H. (2020). Discovery of ubiquitin-specific protease 7 (USP7) inhibitors with novel scaffold structures by virtual screening, molecular dynamics simulation, and biological evaluation. Journal of Chemical Information and Modeling, 60(6), 3255–3264. https://doi.org/10.1021/acs.jcim.0c00154
   PubMed Web of Science ®Google Scholar
• Loharch, S., Bhutani, I., Jain, K., Gupta, P., Sahoo, D. K., & Parkesh, R. (2015). EpiDBase: A manually curated database for small molecule modulators of epigenetic landscape. Database: The Journal of Biological Databases and Curation, 2015, 1-10. https://doi.org/10.1093/database/bav013
   Google Scholar
• Lou, Q., Pan, C., McGuinness, J., Horvath, A., Naeemi, A., Niemier, M., & Hu, X. S. (2019). A mixed signal architecture for convolutional neural networks. ACM Journal on Emerging Technologies in Computing Systems, 15(2), 1–26. https://doi.org/10.1145/3304110
   Web of Science ®Google Scholar
• Manal, M., Chandrasekar, M. J., Gomathi Priya, J., & Nanjan, M. J. (2016). Inhibitors of histone deacetylase as antitumor agents: A critical review. Bioorganic Chemistry, 67, 18–42. https://doi.org/10.1016/j.bioorg.2016.05.005
   PubMed Web of Science ®Google Scholar
• Maria Khan, Z. (2020). Combinatorial library designing and virtual screening of cryptolepine derivatives against topoisomerase ii by molecular docking.
   Google Scholar
• Mayran, A., & Drouin, J. (2018). Pioneer transcription factors shape the epigenetic landscape. Journal of Biological Chemistry, 293(36), 13795–13804. https://doi.org/10.1074/jbc.R117.001232
   PubMed Web of Science ®Google Scholar
• Medvedeva, Y. A., Lennartsson, A., Ehsani, R., Kulakovskiy, I. V., Vorontsov, I. E., Panahandeh, P., Khimulya, G., Kasukawa, T., Consortium, F., & Drablos, F, FANTOM Consortium. (2015). EpiFactors: A comprehensive database of human epigenetic factors and complexes. Database: The Journal of Biological Databases and Curation, 2015, bav067. https://doi.org/10.1093/database/bav067
   PubMedGoogle Scholar
• Mendez, D., Gaulton, A., Bento, A. P., Chambers, J., De Veij, M., Felix, E., Magarinos, M. P., Mosquera, J. F., Mutowo, P., Nowotka, M., Gordillo-Maranon, M., Hunter, F., Junco, L., Mugumbate, G., Rodriguez-Lopez, M., Atkinson, F., Bosc, N., Radoux, C. J., Segura-Cabrera, A., Hersey, A., & Leach, A. R. (2019). ChEMBL: Towards direct deposition of bioassay data. Nucleic Acids Research, 47(D1), D930–D940. https://doi.org/10.1093/nar/gky1075
   PubMed Web of Science ®Google Scholar
• Miranda Furtado, C. L., Dos Santos Luciano, M. C., Silva Santos, R. D., Furtado, G. P., Moraes, M. O., & Pessoa, C. (2019). Epidrugs: Targeting epigenetic marks in cancer treatment. Epigenetics, 14(12), 1164–1176. https://doi.org/10.1080/15592294.2019.1640546
   PubMed Web of Science ®Google Scholar
• Mohammed, M. Z., Vyjayanti, V. N., Laughton, C. A., Dekker, L. V., Fischer, P. M., Wilson, D. M., 3rd., Abbotts, R., Shah, S., Patel, P. M., Hickson, I. D., & Madhusudan, S. (2011). Development and evaluation of human AP endonuclease inhibitors in melanoma and glioma cell lines. British Journal of Cancer, 104(4), 653–663. https://doi.org/10.1038/sj.bjc.6606058
   PubMed Web of Science ®Google Scholar
• Morgan, H. L. (1965). The generation of a unique machine description for chemical structures – A technique developed at chemical abstracts service. Journal of Chemical Documentation, 5(2), 107–113. https://doi.org/10.1021/c160017a018
   Google Scholar
• Naveja, J. J., & Medina-Franco, J. L. (2018). Insights from pharmacological similarity of epigenetic targets in epipolypharmacology. Drug Discovery Today, 23(1), 141–150. https://doi.org/10.1016/j.drudis.2017.10.006
   PubMed Web of Science ®Google Scholar
• Nickel, J., Gohlke, B. O., Erehman, J., Banerjee, P., Rong, W. W., Goede, A., Dunkel, M., & Preissner, R. (2014). SuperPred: Update on drug classification and target prediction. Nucleic Acids Research, 42(Web Server issue), W26–31. https://doi.org/10.1093/nar/gku477
   PubMed Web of Science ®Google Scholar
• Nogueira, M. S., & Koch, O. (2019). The development of target-specific machine learning models as scoring functions for docking-based target prediction. Journal of Chemical Information and Modeling, 59(3), 1238–1252. https://doi.org/10.1021/acs.jcim.8b00773
   PubMed Web of Science ®Google Scholar
• Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, B., Thirion, O., Grisel, M., Blondel, P., Prettenhofer, R., Weiss, V., Dubourg, J., Vanderplas, A., Passos, D., Cournapeau, M., Brucher, M., Perrot., & Duchesnay, É (2011). Scikit-learn: Machine learning in Python.
   Google Scholar
• Rodriguez-Paredes, M., & Esteller, M. (2011). Cancer epigenetics reaches mainstream oncology. Nature Medicine, 17(3), 330–339. https://doi.org/10.1038/nm.2305
   PubMed Web of Science ®Google Scholar
• Rognan, D. (2010). Structure-based approaches to target fishing and ligand profiling. Molecular Informatics, 29(3), 176–187. https://doi.org/10.1002/minf.200900081
   PubMed Web of Science ®Google Scholar
• Romanelli, A., Stazi, G., Fioravanti, R., Zwergel, C., Di Bello, E., Pomella, S., Perrone, C., Battistelli, C., Strippoli, R., Tripodi, M., Del Bufalo, D., Rota, R., Trisciuoglio, D., Mai, A., & Valente, S. (2020). Design of first-in-class dual EZH2/HDAC inhibitor: Biochemical activity and biological evaluation in cancer cells. ACS Medicinal Chemistry Letters, 11(5), 977–983. https://doi.org/10.1021/acsmedchemlett.0c00014
   PubMed Web of Science ®Google Scholar
• Salo, H. S., Laitinen, T., Poso, A., Jarho, E., & Lahtela-Kakkonen, M. (2013). Identification of novel SIRT3 inhibitor scaffolds by virtual screening. Bioorganic & Medicinal Chemistry Letters, 23(10), 2990–2995. https://doi.org/10.1016/j.bmcl.2013.03.033
   PubMed Web of Science ®Google Scholar
• Sanchez-Cruz, N., & Medina-Franco, J. L. (2021). Epigenetic target profiler: A web server to predict epigenetic targets of small molecules. Journal of Chemical Information and Modeling, 61(4), 1550–1554. https://doi.org/10.1021/acs.jcim.1c00045
   PubMed Web of Science ®Google Scholar
• Sanchez-Cruz, N., & Medina-Franco, J. L. (2021). Epigenetic target fishing with accurate machine learning models. Journal of Medicinal Chemistry, 64(12), 8208–8220. https://doi.org/10.1021/acs.jmedchem.1c00020
   PubMed Web of Science ®Google Scholar
• Sargsyan, K., Grauffel, C., & Lim, C. (2017). How molecular size impacts RMSD applications in molecular dynamics simulations. Journal of Chemical Theory and Computation, 13(4), 1518–1524. https://doi.org/10.1021/acs.jctc.7b00028
   PubMed Web of Science ®Google Scholar
• Schrodinger, L. (2011). Schrodinger software suite.
   Google Scholar
• Schwede, T., Kopp, J., Guex, N., & Peitsch, M. C. (2003). SWISS-MODEL: An automated protein homology-modeling server. Nucleic Acids Research, 31(13), 3381–3385. https://doi.org/10.1093/nar/gkg520
   PubMed Web of Science ®Google Scholar
• Sessions, Z., Sanchez-Cruz, N., Prieto-Martinez, F. D., Alves, V. M., Santos, H. P., Jr., Muratov, E., Tropsha, A., & Medina-Franco, J. L. (2020). Recent progress on cheminformatics approaches to epigenetic drug discovery. Drug Discovery Today, 25(12), 2268–2276. https://doi.org/10.1016/j.drudis.2020.09.021
   PubMed Web of Science ®Google Scholar
• Singh Nanda, J., Kumar, R., & Raghava, G. P. (2016). dbEM: A database of epigenetic modifiers curated from cancerous and normal genomes. Scientific Reports, 6(1), 19340. https://doi.org/10.1038/srep19340
   PubMedGoogle Scholar
• Stresemann, C., & Lyko, F. (2008). Modes of action of the DNA methyltransferase inhibitors azacytidine and decitabine. International Journal of Cancer, 123(1), 8–13. https://doi.org/10.1002/ijc.23607
   PubMed Web of Science ®Google Scholar
• Tariq, A., Rehman, H. M., Mateen, R. M., Ali, M., Mutahir, Z., Afzal, M. S., Sajjad, M., Gul, R., & Saleem, M. (2022). A computer aided drug discovery based discovery of lead-like compounds against KDM5A for cancers using pharmacophore modeling and high-throughput virtual screening. Proteins, 90(3), 645–657. https://doi.org/10.1002/prot.26262
   PubMed Web of Science ®Google Scholar
• Teif, V. B., & Rippe, K. (2009). Predicting nucleosome positions on the DNA: Combining intrinsic sequence preferences and remodeler activities. Nucleic Acids Research, 37(17), 5641–5655. https://doi.org/10.1093/nar/gkp610
   PubMed Web of Science ®Google Scholar
• Tyagi, M., Imam, N., Verma, K., & Patel, A. K. (2016). Chromatin remodelers: We are the drivers!. Nucleus (Austin, Tex.), 7(4), 388–404. https://doi.org/10.1080/19491034.2016.1211217
   PubMed Web of Science ®Google Scholar
• Vidler, L. R., Filippakopoulos, P., Fedorov, O., Picaud, S., Martin, S., Tomsett, M., Woodward, H., Brown, N., Knapp, S., & Hoelder, S. (2013). Discovery of novel small-molecule inhibitors of BRD4 using structure-based virtual screening. Journal of Medicinal Chemistry, 56(20), 8073–8088. https://doi.org/10.1021/jm4011302
   PubMed Web of Science ®Google Scholar
• Vilar, S., Cozza, G., & Moro, S. (2008). Medicinal chemistry and the molecular operating environment (MOE): Application of QSAR and molecular docking to drug discovery. Current Topics in Medicinal Chemistry, 8(18), 1555–1572. https://doi.org/10.2174/156802608786786624
   PubMed Web of Science ®Google Scholar
• Wang, D. D., Chan, M. T., & Yan, H. (2021). Structure-based protein-ligand interaction fingerprints for binding affinity prediction. Computational and Structural Biotechnology Journal, 19, 6291–6300. https://doi.org/10.1016/j.csbj.2021.11.018
   PubMed Web of Science ®Google Scholar
• Wang, Y., Zhang, S., Li, F., Zhou, Y., Zhang, Y., Wang, Z., Zhang, R., Zhu, J., Ren, Y., Tan, Y., Qin, C., Li, Y., Li, X., Chen, Y., & Zhu, F. (2020). Therapeutic target database 2020: Enriched resource for facilitating research and early development of targeted therapeutics. Nucleic Acids Research, 48(D1), D1031–D1041.
   PubMed Web of Science ®Google Scholar
• Warr, W. A. (2012). Scientific workflow systems: Pipeline Pilot and KNIME. Journal of Computer-Aided Molecular Design, 26(7), 801–804. https://doi.org/10.1007/s10822-012-9577-7
   PubMed Web of Science ®Google Scholar
• Weininger, D. (1988). SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. Journal of Chemical Information and Computer Sciences, 28(1), 31–36. https://doi.org/10.1021/ci00057a005
   Google Scholar
• Wilson, J. E., Patel, G., Patel, C., Brucelle, F., Huhn, A., Gardberg, A. S., Poy, F., Cantone, N., Bommi-Reddy, A., Sims, R. J., 3rd., Cummings, R. T., & Levell, J. R. (2020). Discovery of CPI-1612: A potent, selective, and orally bioavailable EP300/CBP histone acetyltransferase inhibitor. ACS Medicinal Chemistry Letters, 11(6), 1324–1329. https://doi.org/10.1021/acsmedchemlett.0c00155
   PubMed Web of Science ®Google Scholar
• Wojcikowski, M., Zielenkiewicz, P., & Siedlecki, P. (2015). Open drug discovery toolkit (ODDT): A new open-source player in the drug discovery field. Journal of Cheminformatics, 7(1), 26. https://doi.org/10.1186/s13321-015-0078-2
   PubMedGoogle Scholar
• Xue, B., Chaddha, M., Elasbali, A. M., Zhu, Z., Jairajpuri, D. S., Alhumaydhi, F. A., Mohammad, T., Abdulmonem, W. A., Sharaf, S. E., & Hassan, M. I. (2022). Death-associated protein kinase 3 inhibitors identified by virtual screening for drug discovery in cancer and hypertension. Omics: A Journal of Integrative Biology, 26(7), 404–413. https://doi.org/10.1089/omi.2022.0044
   PubMed Web of Science ®Google Scholar
• Yagci, S., Gozelle, M., Kaya, S. G., Ozkan, Y., Aksel, A. B., Bakar-Ates, F., Dundar, Y., & Eren, G. (2021). Hit-to-lead optimization on aryloxybenzamide derivative virtual screening hit against SIRT. Bioorganic & Medicinal Chemistry, 30, 115961. https://doi.org/10.1016/j.bmc.2020.115961
   PubMed Web of Science ®Google Scholar
• Yan, Y., Zhou, X. E., Novick, S. J., Shaw, S. J., Li, Y., Brunzelle, J. S., Hitoshi, Y., Griffin, P. R., Xu, H. E., & Melcher, K. (2019). Structures of AMP-activated protein kinase bound to novel pharmacological activators in phosphorylated, non-phosphorylated, and nucleotide-free states. Journal of Biological Chemistry, 294(3), 953–967. https://doi.org/10.1074/jbc.RA118.004883
   PubMed Web of Science ®Google Scholar
• Yang, S. H., Khadka, D. B., Cho, S. H., Ju, H. K., Lee, K. Y., Han, H. J., Lee, K. T., & Cho, W. J. (2011). Virtual screening and synthesis of quinazolines as novel JAK2 inhibitors. Bioorganic & Medicinal Chemistry, 19(2), 968–977. https://doi.org/10.1016/j.bmc.2010.11.057
   PubMed Web of Science ®Google Scholar
• Zdrazil, B., Richter, L., Brown, N., & Guha, R. (2020). Moving targets in drug discovery. Scientific Reports, 10(1), 20213. https://doi.org/10.1038/s41598-020-77033-x
   PubMed Web of Science ®Google Scholar
• Zhang, Y., Yang, S., Jiao, Y., Liu, H., Yuan, H., Lu, S., Ran, T., Yao, S., Ke, Z., Xu, J., Xiong, X., Chen, Y., & Lu, T. (2013). An integrated virtual screening approach for VEGFR-2 inhibitors. Journal of Chemical Information and Modeling, 53(12), 3163–3177. https://doi.org/10.1021/ci400429g
   PubMed Web of Science ®Google Scholar
• Zhang, X., Yan, J., Wang, H., Wang, Y., Wang, J., & Zhao, D. (2021). Molecular docking, 3D-QSAR, and molecular dynamics simulations of thieno[3,2-b]pyrrole derivatives against anticancer targets of KDM1A/LSD1. Journal of Biomolecular Structure & Dynamics, 39(4), 1189–1202. https://doi.org/10.1080/07391102.2020.1726819
   PubMed Web of Science ®Google Scholar
• Zhenyu Yuan, Y. J., Li, J., & Huang, H. (2005). Hybrid-DNNs: Hybrid deep neural networks for mixed inputs.
   Google Scholar