İlaç Keşfinde Verimlilik ve Hassasiyeti Artırmaya Yönelik Yenilikçi Stratejiler

Atz, K., Cotos, L., Isert, C., et al. (2024). Prospective de Novo Drug Design with Deep Interactome Learning. Nature Communication, 22;15(1):3408. doi: 10.1038/s41467-024-47613-w

Barrow, A. S., Smedley, C. J., Zheng, Q., et al. (2019). The Growing Applications of SuFEx Click Chemistry. Chemical Society Reviews, 48(17):4731–58. doi:10.1039/C8CS00960K.

Bedard, Philippe L., Hyman, David M., Davids, Matthew S., et al. (2020). Small Molecules, Big Impact: 20 Years of Targeted Therapy in Oncology. The Lancet, 395(10229):1078–88. doi: 10.1016/S0140-6736(20)30164-1.

Bhardwaj, A., Kaur, J., Wuest, M et al. (2017). In Situ Click Chemistry Generation of Cyclooxygenase-2 Inhibitors. Nature Communications, 8(1):1–14. doi:10.1038/S41467-016-0009-6

Bonanno, Etienne, and Jean Paul Ebejer, J.P. (2020). Applying Machine Learning to Ultrafast Shape Recognition in Ligand-Based Virtual Screening. Frontiers in Pharmacology, 10:501024. doi.org/10.3389/fphar.2019.01675

Bondeson, Daniel P., Mares, A., Smith, Ian E. D. et al. (2015). Catalytic in Vivo Protein Knockdown by Small-Molecule PROTACs. Nature Chemical Biology, 11(8):611–17. doi:10.1038/nchembio.1858.

Brenner, S., and Lerner. R.A (1992). Encoded Combinatorial Chemistry. Proceedings of the National Academy of Sciences, 89(12):5381–83. doi:10.1073/PNAS.89.12.5381.

Cai, J. H., Zhu, X.Z, Guo, P.Y. et al. (2023). Recent Updates in Click and Computational Chemistry for Drug Discovery and Development. Frontiers in Chemistry, 11:1114970. doi: 10.3389/fchem.2023.1114970.

Campbell, I.B., Macdonald, S. J. F., and Procopiou, P.A. (2018). Medicinal Chemistry in Drug Discovery in Big Pharma: Past, Present and Future. Drug Discovery Today, 23(2):219–34. doi: 10.1016/j.drudis.2017.10.007.

Caselli, E., Romagnoli, C., Vahabi, R. et al. (2015). Click Chemistry in Lead Optimization of Boronic Acids as β-Lactamase Inhibitors. Journal of Medicinal Chemistry, 58(14):5445–58. doi: 10.1021/acs.jmedchem.5b00341

Castagnolo, D. (2023). Looking Back and Moving Forward in Medicinal Chemistry. Nature Communications, 14(1):4299. doi:10.1038/S41467-023-39949-6

Chen, Q., Li, Y., Lin, C., et al. (2022). Expanding the DNA-Encoded Library Toolbox: Identifying Small Molecules Targeting RNA. Nucleic Acids Research, 50(12):e67–e67. doi:10.1093/nar/gkac173.

Chen, Y. J., Wu, H., and Shen X. Z. (2016). The Ubiquitin–Proteasome System and Its Potential Application in Hepatocellular Carcinoma Therapy. Cancer Letters, 379(2):245–52. doi:10.1016/j.canlet.2015.06.023.

Cheng, T., Li, Q., Zhou, Z. et al. (2012). Structure-Based Virtual Screening for Drug Discovery: A Problem-Centric Review. AAPS Journal, 14(1):133–41. doi:10.1208/s12248-012-9322-0/metrıcs.

Clark, M. A., Acharya, R. A., Arico-Muendel, C. C. et al. (2009). Design, Synthesis and Selection of DNA-Encoded Small-Molecule Libraries. Nature Chemical Biology, 5(9):647–54. doi:10.1038/nchembıo.211;kwrd=chemıstry.

Cochrane, W. G., Fitzgerald, P. R., and Paegel., B. M (2021). Antibacterial Discovery via Phenotypic DNA-Encoded Library Screening. ACS Chemical Biology, 16(12):2752–56. doi.org/10.1021/acschembio.1c00714

Daina, A., Michielin, O., Zoete, V. Scientific reports, and undefined 2017. (2017). SwissADME: A Free Web Tool to Evaluate Pharmacokinetics, Drug-Likeness and Medicinal Chemistry Friendliness of Small Molecules. Scientific Reports, 7:42717 | DOI: 10.1038/srep42717

Davis, J. S., Ferreira, D., Paige, E. et al. (2020). Infectious Complications of Biological and Small Molecule Targeted Immunomodulatory Therapies. Clinical Microbiology Reviews, 33(3):1–117. doi: 10.1128/CMR.00035-19.

Defelipe, L.A., Arcon, J. P., Modenutti, C. P. et al. (2018). Solvents to Fragments to Drugs: MD Applications in Drug Design. Molecules, 23(12):3269. doi:10.3390/molecules23123269.

Ding, Y., Fei, Y., and Lu, B. (2020). Emerging New Concepts of Degrader Technologies. Trends in Pharmacological Sciences, 41(7):464-74. doi: 10.1016/j.tips.2020.04.005

van Drie, J. H. (2005). Pharmacophore Discovery - Lessons Learned. Current Pharmaceutical Design, 9(20):1649–64. doi:10.2174/1381612033454568.

Le Droumaguet, B., Guerrouache, M., and Carbonnier, B. (2022). Contribution of the “Click Chemistry” Toolbox for the Design, Synthesis, and Resulting Applications of Innovative and Efficient Separative Supports: Time for Assessment. Macromolecular Rapid Communications, 43(19):2200210. doi.org/10.1002/marc.202200210

Eldridge, A.G., and O’Brien, T. (2010). Therapeutic Strategies within the Ubiquitin Proteasome System. Cell Death and Differentiation, 17(1):4-13. doi: 10.1038/cdd.2009.82.

Ellis, R. John. (2001). Macromolecular Crowding: Obvious but Underappreciated. Trends in Biochemical Sciences, 26(10):597-604. doi:10.1016/S0968-0004(01)01938-7.

Ferreira, LLG., Andricopulo, A.D. (2019). ADMET Modeling Approaches in Drug Discovery. Drug Discovery Today, 24(5):1157-1165. doi.org/10.1016/j.drudis.2019.03.015

Forli, S., Huey, R., Pique, M. E. et al. (2016). Computational Protein–Ligand Docking and Virtual Drug Screening with the AutoDock Suite. Nature Protocols, 11(5):905-19. doi:10.1038/nprot.2016.051.

Fu, L., Shi, S., Yi, J. et al. (2024). ADMETlab 3.0: An Updated Comprehensive Online ADMET Prediction Platform Enhanced with Broader Coverage, Improved Performance, API Functionality and Decision. Nucleic Acids Research, 52:422-31. doi:10.1093/nar/gkae236.

Gartner, Z. J., and Liu., D. R. (2001). The Generality of DNA-Templated Synthesis as a Basis for Evolving Non-Natural Small Molecules. Journal of the American Chemical Society 123(28):6961–63. doi: 10.1021/ja015873n.

Glassford, I., Teijaro, C. N., Daher, S. S. et al. (2016). Ribosome-Templated Azide-Alkyne Cycloadditions: Synthesis of Potent Macrolide Antibiotics by in Situ Click Chemistry. Journal of the American Chemical Society, 138(9):3136–44. doi: 10.1021/jacs.5b13008.

Giordano, D., Biancaniello, C., Argenio, M. A., and Facchiano, A. (2022). Drug design by pharmacophore andvirtual screening approach. Pharmacy (Basel) 15: 646. doi:10.3390/ph15050646

Harris, P. A., Berger, S. C., Jeong, J. U. et al. (2017). Discovery of a First-in-Class Receptor Interacting Protein 1 (RIP1) Kinase Specific Clinical Candidate (GSK2982772) for the Treatment of Inflammatory Diseases. Journal of Medicinal Chemistry, 60(4):1247-61. doi.org/10.1021/acs.jmedchem.6b01751.

Haupt, VJ, Schroeder, M. (2011). Old Friends in New Guise: Repositioning of Known Drugs with Structural Bioinformatics. Briefings in Bioinformatics, 12(4):312-26. doi: 10.1093/bib/bbr011.

He, H., Duo, H., Hao, Y. et al. (2023). Computational Drug Repurposing by Exploiting Large-Scale Gene Expression Data: Strategy, Methods and Applications. Computers in Biology and Medicine, 155:106671. doi: 10.1016/j.compbiomed.2023.106671.

Hsieh, J. C., Giannakoulias, S., Petersson, J. E. et al. (2023). Computational Chemistry for the Identification of Lead Compounds for Radiotracer Development. Pharmaceuticals, 16(2):317. doi:10.3390/ph16020317/s1.

Hu, X., and Manetsch, R. (2010). Kinetic Target-Guided Synthesis. Chemical Society Reviews, 39(4):1316-24. doi:10.1039/B904092G.

Keiser, M. J, Setola, V., Irwin, J. J. et al. C. (2009). Predicting New Molecular Targets for Known Drugs, Nature,12;462(7270):175-81. doi: 10.1038/nature08506

Keserü, G. M., and Makara, G. M. (2009). The Influence of Lead Discovery Strategies on the Properties of Drug Candidates. Nature Reviews Drug Discovery, 8(3):203–12. doi:10.1038/nrd2796

Keutzer, L., You, H., Farnoud, A., et al. (2022). Machine Learning and Pharmacometrics for Prediction of Pharmacokinetic Data: Differences, Similarities and Challenges Illustrated with Rifampicin. Pharmaceutics, 14(8), 1530. 10.3390/pharmaceutics14081530

Kim, E., and Koo, H. (2019). Biomedical Applications of Copper-Free Click Chemistry: In Vitro, in Vivo, and Ex Vivo. Chemical Science, 10(34):7835–51. doi:10.1039/c9sc03368h.

Kolb, H. C., Finn, M. G., and Sharpless, K. B. (2001). Click Chemistry: Diverse Chemical Function from a Few Good Reactions. Angewandte Chemie International Edition, 40(11):2004–21. doi:10.1002/1521-3773(20010601)40:11<2004::aıd-anıe2004>3.0.co;2-5.

Kugler, M., Hadzima, M., Dzijak, R. et al. (2023). Identification of Specific Carbonic Anhydrase Inhibitors via in Situ Click Chemistry, Phage-Display and Synthetic Peptide Libraries: Comparison of the Methods and Structural Study. RSC Medicinal Chemistry, 14(1):144–53. doi:10.1039/D2MD00330A.

Kuzmanic, A., Bowman, G. R., Juarez-Jimenez, J. et al. (2020). Investigating Cryptic Binding Sites by Molecular Dynamics Simulations. Accounts of Chemical Research, 53(3):654–61. doi: 10.1021/acs.accounts.9b00613

Lee, C.H., Huang, H. C., and Juan, H. F. (2011). Reviewing Ligand-Based Rational Drug Design: The Search for an ATP Synthase Inhibitor. International Journal of Molecular Sciences, 12(8):5304-18. doi:10.3390/IJMS12085304.

Lima, A. N., Philot, E. A., Trossini, G. H. G. et al. (2016). Use of Machine Learning Approaches for Novel Drug Discovery. Expert Opinion on Drug Discovery, 11(3):225-39. doi:10.1517/17460441.2016.1146250.

Lionta, E., Spyrou, G., Vassilatis, D. K. et al. 2014. Structure-Based Virtual Screening for Drug Discovery: Principles, Applications and Recent Advances. Current Topics in Medicinal Chemistry, 14(16):1923-38. doi:10.2174/1568026614666140929124445.

Liu, G., Catacutan, D. B., Rathod, K. et al. (2023). Deep Learning-Guided Discovery of an Antibiotic Targeting Acinetobacter Baumannii. Nature Chemical Biology, 19(11):1342–50. doi:10.1038/s41589-023-01349-8.

Liu, X., Shi, D., Zhou, S., et al. (2018). Molecular Dynamics Simulations and Novel Drug Discovery. Expert Opinion on Drug Discovery, 13(1):23-37. doi: 10.1080/17460441.2018.1403419.

Macalino, S. J. Y., Gosu, V., Hong, S. et al. (2015). Role of Computer-Aided Drug Design in Modern Drug Discovery. Archives of Pharmacal Research, 2015 38:9 38(9):1686-1701. doi:10.1007/S12272-015-0640-5.

Maia, E. H. B., Assis, L.C., Oliveira, T. A., et al. (2020). Structure-Based Virtual Screening: From Classical to Artificial Intelligence. Frontiers in Chemistry, 8:481382. doi:10.3389/fchem.2020.00343/xml/nlm.

Matsuo, B., Granados, A., Levitre, G., et al. (2023). Photochemical Methods Applied to DNA Encoded Library (DEL) Synthesis. Accounts of Chemical Research, 56(3):385-401. doi: 10.1021/acs.accounts.2c00778

McNair, D. (2023). Artificial Intelligence and Machine Learning for Lead-to-Candidate Decision-Making and Beyond. Annual Review of Pharmacology and Toxicology, 63:77–97. doi:10.1146/annurev-pharmtox-051921-023255

Medina-Franco, J.L (2021). Grand Challenges of Computer-Aided Drug Design: The Road Ahead. Frontiers in Drug Discovery, 1: 728551. doi:10.3389/fddsv.2021.728551

van Montfort, R. L. M., and Workman, P. (2009). Structure-Based Design of Molecular Cancer Therapeutics. Trends in Biotechnology, 27(5):315-28. doi: 10.1016/j.tibtech.2009.02.003

Morgan, P., Van Der Graaf, P.H., Arrowsmith, J. (2012). Can the Flow of Medicines Be Improved? Fundamental Pharmacokinetic and Pharmacological Principles toward Improving Phase II Survival. Drug Discovery Today, 17(9-10):419-24. doi: 10.1016/j.drudis.2011.12.020

Neri, D., and Lerner, R.A. (2018). DNA-Encoded Chemical Libraries: A Selection System Based on Endowing Organic Compounds with Amplifiable Information. Annual Review of Biochemistry, 87(Volume 87, 2018):479–502. doi: 10.1146/annurev-biochem-062917-012550

Parra Sánchez, A.R., Voskuyl, A. E., and van Vollenhoven, R. F. (2022). Treat-to-Target in Systemic Lupus Erythematosus: Advancing towards Its Implementation. Nature Reviews Rheumatology, 18(3):146-57. doi:10.1038/S41584-021-00739-3

Petersen, L.K., Christensen, A.B., Andersen, J., et al. (2021). Screening of DNA-Encoded Small Molecule Libraries inside a Living Cell. Journal of the American Chemical Society, 143(7):2751–56. doi: 10.1021/jacs.0c09213

Peterson, A. A., and Liu, D. R. (2023). Small-molecule discovery through DNA encoded libraries. Nature Reviews Drug Discovery, 22: 699-722. doi:10.1038/s41573-023-00713-6

Protein Data Bank (http://rcsb.org)

Rusinko, A., Rezaei, M., Friedrich, L. et al. (2024). AIDDISON: Empowering Drug Discovery with AI/ML and CADD Tools in a Secure, Web-Based SaaS Platform. Journal of Chemical Information and Modeling, 64(1):3-8. doi: 10.1021/acs.jcim.3c01016

Rzuczek, S. G., Park, H., and Disney., M.D. (2014). A Toxic RNA Catalyzes the In Cellulo Synthesis of Its Own Inhibitor. Angewandte Chemie International Edition, 53(41):10956–59. doi:10.1002/anıe.201406465.

Sadybekov, A. V., and Katritch, V. (2023). Computational approaches streamlining drug discovery. Nature, 616: 673–685. doi:10.1038/s41586-023-05905-z

Saftig, P., and Klumperman, J. (2009). Lysosome Biogenesis and Lysosomal Membrane Proteins: Trafficking Meets Function. Nature Reviews Molecular Cell Biology, 2009 10:9 10(9):623–35. doi:10.1038/nrm2745.

Sainy, J., and Sharma, R. (2015). QSAR Analysis of Thiolactone Derivatives Using HQSAR, CoMFA and CoMSIA. SAR and QSAR in Environmental Research, 26(10):873-92. doi: 10.1080/1062936X.2015.1095238

Saldívar-González, F.I., and Medina-Franco, J.L. (2022). Approaches for Enhancing the Analysis of Chemical Space for Drug Discovery. Expert Opinion on Drug Discovery, 17(7):789-98. doi: 10.1080/17460441.2022.2084608

Scheuermann, J., Dumelin, C. E., Melkko, S. et al. (2006). DNA-Encoded Chemical Libraries. Journal of Biotechnology, 126(4):568–81. doi:10.1016/j.jbıotec.2006.05.018.

Schneider, G., Funatsu, K., Okuno, Y. et al. (2017). De Novo Drug Design – Ye Olde Scoring Problem Revisited. Molecular Informatics, 36(1-2). doi: 10.1002/minf.201681031

Schneider, G. (2014). Future de Novo Drug Design. Molecular Informatics, 33(6-7):397–402. doi:10.1002/mınf.201400034.

Shankaraiah, N., Sakla, A.P., Laxmikeshav, K., et al. (2020). Reliability of Click Chemistry on Drug Discovery: A Personal Account. The Chemical Record, 20(4):253-72. doi:10.1002/tcr.201900027.

Shen, C., Wang, Z., Yao, X. et.al. (2020). Comprehensive Assessment of Nine Docking Programs on Type II Kinase Inhibitors: Prediction Accuracy of Sampling Power, Scoring Power and Screening Power. Briefings in Bioinformatics, 21(1):282-97. doi:10.1093/bıb/bby103.

Sohlenius-Sternbeck, A.K., Terelius, Y. (2022). Evaluation of ADMET Predictor in Early Discovery Drug Metabolism and Pharmacokinetics Project Work. Drug Metabolism and Disposition, 50(2):95-104. doi: 10.1124/dmd.121.000552.

Tang, Y., Feng, B., Wang, Y., et. al. (2020). Structure-Based Discovery of CZL80, a Caspase-1 Inhibitor with Therapeutic Potential for Febrile Seizures and Later Enhanced Epileptogenic Susceptibility. British Journal of Pharmacology, 177(15):3519–34. doi:10.1111/bph.15076.

Thomas, M., Smith, R.T., O’Boyle, N.M. et al. (2021). Comparison of Structure-and Ligand-Based Scoring Functions for Deep Generative Models: A GPCR Case Study. Journal of Cheminformatics, 13(39). doi: 10.1186/s13321-021-00516-0

Vyas, V. K., Bhati, S., Patel, S. et al. (2022). Structure- and Ligand-Based Drug Design Methods for the Modeling of Antimalarial Agents: A Review of Updates from 2012 Onwards. Journal of Biomolecular Structure and Dynamics, 40(20):10481–506. doi:10.1080/07391102.2021.1932598

Wang, X., Huang, B., Liu, X. et al. (2016). Discovery of Bioactive Molecules from CuAAC Click-Chemistry-Based Combinatorial Libraries. Drug Discovery Today, 21(1):118-32. doi:10.1016/j.drudıs.2015.08.004.

Wells, J. A., and Kumru, K. (2024). Extracellular targeted protein degradation: an emerging modality for drug discovery. Nature Reviews Drug Discovery, 23: 126-140. doi:10.1038/s41573-023-00833-z

Wichert M., Guasch, L., M. Franzini, R.M (2024). Challenges and Prospects of DNA-Encoded Library Data Interpretation. Chemical Reviews, 124 (22): 12551-12572. doi:10.1021/acs.chemrev.4c00284

Wu, H., Liu, J., Zhang, R. et al. (2024). A Review of Deep Learning Methods for Ligand Based Drug Virtual Screening. Fundamental Research, 4(4):715-37. doi:10.1016/j.fmre.2024.02.011.

Wurz, R. P., Dellamaggiore, K., Dou, H. et al. (2018). A “Click Chemistry Platform” for the Rapid Synthesis of Bispecific Molecules for Inducing Protein Degradation. Journal of Medicinal Chemistry, 61(2):453–61. doi: 10.1021/acs.jmedchem.6b01781

Yang, X., Wang, Y., Byrne, R. et al. (2019). Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery. Chemical Reviews 119(18):10520–94. doi: 10.1021/acs.chemrev.8b00728

Yang, Z.Y., Fu, L., Lu, A. P. et al. (2021). Semi-Automated Workflow for Molecular Pair Analysis and QSAR-Assisted Transformation Space Expansion. Journal of Cheminformatics 13(1):1–14. doi:10.1186/s13321-021-00564-6/tables/5.

Yelshanskaya, M.V., Singh, A. K., Narangoda, C. et al. (2022). Structural Basis of AMPA Receptor Inhibition by Trans-4-Butylcyclohexane Carboxylic Acid. British Journal of Pharmacology, 179(14):3628-44. doi:10.1111/bph.15254.

Yi, J., Shi, S., Fu, L. et al. (2024). OptADMET: A Web-Based Tool for Substructure Modifications to Improve ADMET Properties of Lead Compounds. Nature Protocols, 19(4):1105-1121. doi: 10.1038/s41596-023-00942-4

Zhang, C., Liu, Y., Li, G. et al. (2024). Targeting the Undruggables—the Power of Protein Degraders. Science Bulletin 69(11):1776-97. doi:10.1016/j.scıb.2024.03.056.

Zhang, X., Zhang, O., Shen, C. et al. (2023). Efficient and Accurate Large Library Ligand Docking with KarmaDock. Nature Computational Science, 3(9):789-804. doi: 10.1038/s43588-023-00511-5.

Zhao, L., Zhao, J., Zhong, K. et al. (2022). Targeted Protein Degradation: Mechanisms, Strategies and Application. Signal Transduction and Targeted Therapy, 2022 7:1 7(1):1-13. doi:10.1038/s41392-022-00966-4.

Zhao, R., Zhu, J., Jiang, X., and Bai, R. (2024). Click chemistry-aided drug discovery: a retrospective and prospective outlook. European Journal of Medicinal Chemistry, 264: 116037. doi:10.1016/j.ejmech.2023.116037

Zhong, L., Li, Y., Xiong, L. et al. (2021). Small Molecules in Targeted Cancer Therapy: Advances, Challenges, and Future Perspectives. Signal Transduction and Targeted Therapy, 2021 6:1 6(1):1-48. doi:10.1038/s41392-021-00572-w.

Zhu, L., Chen, X., Abola, E. E. et al. (2020). Serial Crystallography for Structure-Based Drug Discovery. Trends in Pharmacological Sciences, 41(11):830–39. doi: 10.1016/j.tips.2020.08.009