Bypassing the Synthesis Bottleneck: A Resource-Stratified Framework for Advancing Cancer Drug Discovery
Main Article Content
Abstract
Despite the proliferation of validated oncogenic targets, the translation of bioactive natural products and high-throughput screening hits into clinical candidates is frequently stalled by the "Synthesis Bottleneck"—the prohibitive cost and technical difficulty of optimizing complex chemical scaffolds. This literature-based review addresses this critical impasse by proposing a paradigm shift from rigid structure-replication to function-mimicry, exemplified by the evolution of the complex marine natural product Halichondrin B to the simplified clinical drug Eribulin. It presents a resource-stratified framework that empowers researchers across the funding spectrum to navigate synthetic intractability. For resource-constrained academic laboratories, it highlights the emergence and revolution of ultra-large "make-on-demand" virtual libraries and pharmacophore hopping. For mid-tier biotechnology firms, it explores fragment-based drug discovery and free energy perturbation modeling to de-risk synthesis. For well-resourced pharmaceutical entities, it discusses the closed-loop integration of generative AI with autonomous robotic synthesis. Finally, it examines modality switching—specifically Proteolysis-Targeting Chimeras (PROTACs) and covalent inhibitors—as a strategic "escape hatch" for targets that remain refractory to traditional small-molecule optimization. By matching specific computational and experimental tools to available resources, this framework aims to democratize the discovery of developable cancer therapies and rescue promising biological hypotheses from the graveyard of intractable chemistry.
Keywords:
Synthetic accessibility; Function-oriented synthesis; Oncology drug discovery; Make-on-demand libraries; PROTACs; Generative AI; Lead optimization; Eribulin.
Article Details
How to Cite
MOSOH, Dexter Achu.
Bypassing the Synthesis Bottleneck: A Resource-Stratified Framework for Advancing Cancer Drug Discovery.
Medical Research Archives, [S.l.], v. 14, n. 2, feb. 2026.
ISSN 2375-1924.
Available at: <https://esmed.org/MRA/mra/article/view/7220>. Date accessed: 02 mar. 2026.
doi: https://doi.org/10.18103/mra.v14i2.7220.
Section
Review Articles
The Medical Research Archives grants authors the right to publish and reproduce the unrevised contribution in whole or in part at any time and in any form for any scholarly non-commercial purpose with the condition that all publications of the contribution include a full citation to the journal as published by the Medical Research Archives.
References
1. Kuo Y, Barrett JS. Consideration of the Root Causes in Candidate Attrition During Oncology Drug Development. Clin Pharmacol Drug Dev. 2024; 13(9):952-960.
2. Walker I, Newell H. Do molecularly targeted agents in oncology have reduced attrition rates? Nat Rev Drug Discov. 2009;8(1):15.
3. Cragg GM, Pezzuto JM. Natural products as a vital source for the discovery of cancer chemotherapeutic and chemopreventive agents. Med Princ Pract. 2016; 25(Suppl. 2):41-59.
4. Mann J. Natural products in cancer chemotherapy: past, present and future. Nat Rev Cancer. 2002;2 (2):143-148.
5. Showell GA, Mills JS. Chemistry challenges in lead optimization: silicon isosteres in drug discovery. Drug Discov Today. 2003;8(12):551-556.
6. Wender PA, Verma VA, Paxton TJ, Pillow TH. Function-oriented synthesis, step economy, and drug design. Acc Chem Res. 2008;41(1):40-49.
7. Bellmann L, Penner P, Gastreich M, Rarey M. Comparison of Combinatorial Fragment Spaces and Its Application to Ultralarge Make-on-Demand Compound Catalogs. J Chem Inf Model. 2022;62 (3):553-566. doi:10.1021/acs.jcim.1c01378
8. Doveston R, Marsden S, Nelson A. Towards the realisation of lead-oriented synthesis. Drug Discov Today. 2014;19(7):813-819.
9. Dai T, Vijayakrishnan S, Szczypiński FT, et al. Autonomous mobile robots for exploratory synthetic chemistry. Nature. 2024;635(8040):890-897. doi:10.1038/s41586-024-08173-7
10. Neklesa TK, Winkler JD, Crews CM. Targeted protein degradation by PROTACs. Pharmacol Ther. 2017;174:138-144. doi:10.1016/j.pharmthera.201 7.02.027
11. Mosoh DA. Widely-targeted in silico and in vitro evaluation of veratrum alkaloid analogs as FAK inhibitors and dual targeting of FAK and Hh/SMO pathways for cancer therapy: A critical analysis. Int J Biol Macromol. Published online October 4, 2024:136201. doi:10.1016/j.ijbiomac.2024.136201 12. Mosoh DA. Recent Advances in Phytochemical Research for Cancer Treatment. IntechOpen; 2024. doi:10.5772/intechopen.1007200
13. Xiao Z, Morris‐Natschke SL, Lee K. Strategies for the optimization of natural leads to anticancer drugs or drug candidates. Med Res Rev. 2016;36 (1):32-91.
14. Rodrigues T, Reker D, Schneider P, Schneider G. Counting on natural products for drug design.
Nat Chem. 2016;8(6):531-541.
15. Huang M, Lu JJ, Ding J. Natural products in cancer therapy: Past, present and future. Nat Prod Bioprospecting. 2021;11(1):5-13.
16. Eastgate MD, Schmidt MA, Fandrick KR. On the design of complex drug candidate syntheses in the pharmaceutical industry. Nat Rev Chem. 2017;1(2): 0016.
17. Truax NJ, Romo D. Bridging the gap between natural product synthesis and drug discovery. Nat Prod Rep. 2020;37(11):1436-1453.
18. Liu J, Liu X, Wu J, Li CC. Total synthesis of natural products containing a bridgehead double bond. Chem. 2020;6(3):579-615.
19. Yang W, Gadgil P, Krishnamurthy VR, et al. The evolving druggability and developability space: chemically modified new modalities and emerging small molecules. AAPS J. 2020;22(2):21.
20. Surade S, Blundell TL. Structural biology and drug discovery of difficult targets: the limits of ligandability. Chem Biol. 2012;19(1):42-50.
21. Skoraczyński G, Kitlas M, Miasojedow B, Gambin A. Critical assessment of synthetic accessibility scores in computer-assisted synthesis planning. J Cheminformatics. 2023;15(1):6.
22. Zhang Y, Zhang Z, Wen L, Liu Y. Synthetic accessibility scoring and its application to the high-throughput design of energetic molecules. J Mater Chem A. 2025;13(22):16330-16344.
23. Yu J, Wang J, Zhao H, et al. Organic compound synthetic accessibility prediction based on the graph attention mechanism. J Chem Inf Model. 2022;62(12):2973-2986.
24. Moreno L, Pearson AD. How can attrition rates be reduced in cancer drug discovery? Expert Opin Drug Discov. 2013;8(4):363-368.
25. Unibest Industrial. Scaling the Everest of Pharma Synthesis: A Deep Dive into Eribulin's Journey - Unibest Industrial Co., Ltd. 2024. Accessed February 5, 2026. https://www.unibestpharm.com/Scaling-the-Everest-of-Pharma-Synthesis-A-Deep-Dive-into-Eribulin-s-Journey-id48528486.html
26. Baran PS. Natural product total synthesis: as exciting as ever and here to stay. J Am Chem Soc. 2018;140(14):4751-4755.
27. Tamura T, Kawano M, Hamachi I. Targeted covalent modification strategies for drugging the undruggable targets. Chem Rev. 2025;125(2):1191-1253.
28. Chen FE, Huang J. Reserpine: a challenge for total synthesis of natural products. Chem Rev. 2005; 105(12):4671-4706.
29. Morrill LA, Susick RB, Chari JV, Garg NK. Total Synthesis as a Vehicle for Collaboration. J Am Chem Soc. 2019;141(32):12423-12443.
30. Reisman SE, Maimone TJ. Total synthesis of complex natural products: more than a race for molecular summits. Acc Chem Res. 2021;54(8):18 15-1816.
31. Rishton GM. Natural products as a robust source of new drugs and drug leads: past successes and present day issues. Am J Cardiol. 2008;101(10):S43-S49.
32. Thomford NE, Senthebane DA, Rowe A, et al. Natural products for drug discovery in the 21st century: innovations for novel drug discovery. Int J Mol Sci. 2018;19(6):1578.
33. Paterson I, Lam NYS. Challenges and discoveries in the total synthesis of complex polyketide natural products. J Antibiot (Tokyo). 2018;71(2):215-233.
34. Blakemore DC, Castro L, Churcher I, et al. Organic synthesis provides opportunities to transform drug discovery. Nat Chem. 2018;10(4):383-394.
35. Chen K, Wu F, Lei X. Function‐oriented natural product synthesis. Chin J Chem. 2021;39(4):838-854.
36. Levin MD, Sarpong R, Wendlandt AE. What is Editing? Acc Chem Res. 2025;58(11):1725-1726.
37. Ma C, Lindsley CW, Chang J, Yu B. Rational molecular editing: a new paradigm in drug discovery. J Med Chem. 2024;67(14):11459-11466.
38. Morrison KC, Hergenrother PJ. Natural products as starting points for the synthesis of complex and diverse compounds. Nat Prod Rep. 2013;31(1):6-14.
39. Melvin JY, Zheng W, Seletsky BM, Littlefield BA, Kishi Y. Case history: discovery of eribulin (HalavenTM), a halichondrin B analogue that prolongs overall survival in patients with metastatic breast cancer. In: Annual Reports in Medicinal Chemistry. Vol 46. Elsevier; 2011:227-241.
40. Irwin JJ, Tang KG, Young J, et al. ZINC20—A Free Ultralarge-Scale Chemical Database for Ligand Discovery. J Chem Inf Model. 2020;60(12):6065-6073. doi:10.1021/acs.jcim.0c00675
41. Lyu J, Irwin JJ, Shoichet BK. Modeling the expansion of virtual screening libraries. Nat Chem Biol. 2023;19(6):712-718. doi:10.1038/s41589-022-01234-w
42. Bui L, Djikic-Stojsic T, Bret G, Bihel F, Kellenberger E. Scaffold-Based Libraries Versus Make-on-Demand Space: A Comparative Assessment of Chemical Content. ChemMedChem. 2025;20 (24):e202500518. doi:10.1002/cmdc.202500518
43. Popov KI, Wellnitz J, Maxfield T, Tropsha A. HIt Discovery using docking ENriched by GEnerative Modeling (HIDDEN GEM): A novel computational workflow for accelerated virtual screening of ultra-large chemical libraries. Mol Inform. 2024;43(1): e202300207. doi:10.1002/minf.202300207
44. Kuan J, Radaeva M, Avenido A, Cherkasov A, Gentile F. Keeping pace with the explosive growth of chemical libraries with structure-based virtual screening. WIREs Comput Mol Sci. 2023;13(6): e1678. doi:10.1002/wcms.1678
45. Festel G. Outsourcing chemical synthesis in the drug discovery process. Drug Discov Today. 2011; 16(5):237-243. doi:10.1016/j.drudis.2011.01.002
46. Jackson V, Jordan L, Burgin RN, McGaw OJS, Muir CW, Ceban V. Application of Molecular-Modeling, Scaffold-Hopping, and Bioisosteric Approaches to the Discovery of New Heterocyclic Picolinamides. J Agric Food Chem. 2022;70(36): 11031-11041. doi:10.1021/acs.jafc.2c03755
47. Muegge I, Bentzien J, Ge Y. Perspectives on current approaches to virtual screening in drug discovery. Expert Opin Drug Discov. 2024;19(10): 1173-1183. doi:10.1080/17460441.2024.2390511
48. Mishra A, Thakur A, Sharma R, et al. Scaffold hopping approaches for dual-target antitumor drug discovery: opportunities and challenges. Expert Opin Drug Discov. 2024;19(11):1355-1381. doi:10.108 0/17460441.2024.2409674
49. AlKharboush DF, Kozielski F, Wells G, Porta EOJ. Fragment-based drug discovery: A graphical review. Curr Res Pharmacol Drug Discov. 2025;9: 100233. doi:10.1016/j.crphar.2025.100233
50. Khedkar NR, Sindkhedkar M, Joseph A. Fragment-Based Drug Discovery: Small Fragments, Big Impact − Success Stories of Approved Oncology Therapeutics. Bioorganic Chem. 2025;156:108197. doi:10.1016/j.bioorg.2025.108197
51. Bon M, Bilsland A, Bower J, McAulay K. Fragment-based drug discovery—the importance of high-quality molecule libraries. Mol Oncol. 2022; 16(21):3761-3777. doi:10.1002/1878-0261.13277
52. Fesik SW. Drugging Challenging Cancer Targets Using Fragment-Based Methods. Chem Rev. 2025; 125(6):3586-3594. doi:10.1021/acs.chemrev.4c00892
53. Peterson AA, Liu DR. Small-molecule discovery through DNA-encoded libraries. Nat Rev Drug Discov. 2023;22(9):699-722. doi:10.1038/s41573-023-00713-6
54. Song M, Hwang GT. DNA-Encoded Library Screening as Core Platform Technology in Drug Discovery: Its Synthetic Method Development and Applications in DEL Synthesis. J Med Chem. 2020;63(13):6578-6599. doi:10.1021/acs.jmedche m.9b01782
55. Poupart J. DNA-Encoded Libraries in Cancer Research: Recent Landmarks and Future Promises. Published online October 16, 2025. doi:10.114 6/annurev-cancerbio-071124-011719
56. Gao Y, Liu J, Huang S, Du N, Zhang G, Li Y. Recent advances in DNA-encoded libraries. Chem Commun. 2025;61(59):10952-10968. doi:10.1039/D 5CC02904J
57. Wang X, Li L, Shen X, Lu X. Rational Design Strategies in DNA-Encoded Libraries for Drug Discovery. Angew Chem Int Ed. 2025;64(34):e2 02511839. doi:10.1002/anie.202511839
58. Abel R, Wang L, Harder ED, Berne BJ, Friesner RA. Advancing Drug Discovery through Enhanced Free Energy Calculations. Acc Chem Res. 2017;50 (7):1625-1632. doi:10.1021/acs.accounts.7b00083
59. de Oliveira C, Leswing K, Feng S, Kanters R, Abel R, Bhat S. FEP Protocol Builder: Optimization of Free Energy Perturbation Protocols Using Active Learning. J Chem Inf Model. 2023;63(17):5592-5603. doi:10.1021/acs.jcim.3c00681
60. Moraca F, Negri A, de Oliveira C, Abel R. Application of Free Energy Perturbation (FEP+) to Understanding Ligand Selectivity: A Case Study to Assess Selectivity Between Pairs of Phosphodiesterases (PDE's). J Chem Inf Model. 2019;59(6):2729-2740. doi:10.1021/acs.jcim.9b00106
61. Loeffler HH, He J, Tibo A, et al. Reinvent 4: Modern AI–driven generative molecule design. J Cheminformatics. 2024;16(1):20. doi:10.1186/s13321-024-00812-5
62. Moon SW, Ahn SH, Ahn JH, Kim HW. R2GB-GA: A Reaction-Regulated Graph-Based Genetic Algorithm for Exploring Synthesizable Chemical Space. Published online 2025.
63. Parrot M, Tajmouati H, da Silva VBR, et al. Integrating synthetic accessibility with AI-based generative drug design. J Cheminformatics. 2023; 15(1):83. doi:10.1186/s13321-023-00742-8
64. Zhang X, Liu J, Xu B, et al. SynFrag: Synthetic Accessibility Predictor based on Fragment Assembly Generation in Drug Discovery. Published online 2025.
65. Katsuyama A, Tokodai Y, Hoshi K, Ichikawa S. Retrosynthetic Analysis-Aware Fragment Linking for Structure-Guided Ligand Extension. Published online 2025.
66. Khater T, Alkhatib SA, AlShehhi A, et al. Generative artificial intelligence based models optimization towards molecule design enhancement. J Cheminformatics. 2025;17(1):116. doi:10.1186/s 13321-025-01059-4
67. van den Broek RL, Patel S, van Westen GJP, Jespers W, Sherman W. In Search of Beautiful Molecules: A Perspective on Generative Modeling for Drug Design. J Chem Inf Model. 2025;65(18):
9383-9397. doi:10.1021/acs.jcim.5c01203
68. Meyers J, Fabian B, Brown N. De novo molecular design and generative models. Drug Discov Today. 2021;26(11):2707-2715. doi:10.1016/j.drudis.202 1.05.019
69. Basak S, Bandyopadhyay A. Toward Making Polymer Chemistry Autonomous. ACS Appl Eng Mater. 2024;2(5):1190-1208. doi:10.1021/acsaen m.4c00214
70. Struble TJ, Alvarez JC, Brown SP, et al. Current and Future Roles of Artificial Intelligence in Medicinal Chemistry Synthesis. J Med Chem. 2020;63(16): 8667-8682. doi:10.1021/acs.jmedchem.9b02120
71. Wang G, Ang HT, Dubbaka SR, O'Neill P, Wu J. Multistep automated synthesis of pharmaceuticals. Trends Chem. 2023;5(6):432-445. doi:10.1016/j.tre chm.2023.03.008
72. Ali RSAE, Meng J, Jiang X. Synergy of Machine Learning and High-Throughput Experimentation: A Road Toward Autonomous Synthesis. Chem – Asian J. 2025;20(20):e00825. doi:10.1002/asia.202500825
73. Zhong L, Xu Y, Li X, Cheng P, Tao S. Production and evaluation of high-throughput reaction data from an automated chemical synthesis platform. Sci China Chem. 2025;68(10):5322-5331. doi:10.1007/s11426-025-2676-x
74. Eyke NS, Koscher BA, Jensen KF. Toward Machine Learning-Enhanced High-Throughput Experimentation. Trends Chem. 2021;3(2):120-132. doi:10.1016/j.trechm.2020.12.001
75. Mennen SM, Alhambra C, Allen CL, et al. The Evolution of High-Throughput Experimentation in Pharmaceutical Development and Perspectives on the Future. Org Process Res Dev. 2019;23(6):1213-1242. doi:10.1021/acs.oprd.9b00140
76. Xiao W, Su H, Ma Y, et al. Integration of Machine Learning and Automated Synthesis for Accelerated Drug and Material Research. ChemistrySelect. 2025;10(42):e04970. doi:10.1002/slct.202504970
77. Liang X, Ren H, Han F, Liang R, Zhao J, Liu H. The new direction of drug development: Degradation of undruggable targets through targeting chimera technology. Med Res Rev. 2024;44(2):632-685. doi:10.1002/med.21992
78. Baillie TA. Approaches to mitigate the risk of serious adverse reactions in covalent drug design. Expert Opin Drug Discov. 2021;16(3):275-287. doi:10.1080/17460441.2021.1832079
79. Fan G, Chen S, Zhang Q, et al. Proteolysis-Targeting Chimera (PROTAC): Current Applications and Future Directions. MedComm. 2025;6(10): e70401. doi:10.1002/mco2.70401
80. Vetma V, O'Connor S, Ciulli A. Development of PROTAC Degrader Drugs for Cancer. Annu Rev Cancer Biol. 2025;9(Volume 9, 2025):119-140. doi:10.1146/annurev-cancerbio-061824-105806
81. Sasso JM, Tenchov R, Wang D, Johnson LS, Wang X, Zhou QA. Molecular Glues: The Adhesive Connecting Targeted Protein Degradation to the Clinic. Biochemistry. 2023;62(3):601-623. doi:10.102 1/acs.biochem.2c00245
82. Jung H, Lee Y. Targeting the Undruggable: Recent Progress in PROTAC-Induced Transcription Factor Degradation. Cancers. 2025;17(11):1871. doi:10.3390/cancers17111871
83. Salama AKAA, Trkulja MV, Casanova E, Uras IZ. Targeted Protein Degradation: Clinical Advances in the Field of Oncology. Int J Mol Sci. 2022;23 (23):15440. doi:10.3390/ijms232315440
84. Baillie TA. Targeted Covalent Inhibitors for Drug Design. Angew Chem Int Ed. 2016;55(43): 13408-13421. doi:10.1002/anie.201601091
85. Péczka N, Orgován Z, Ábrányi-Balogh P, Keserű GM. Electrophilic warheads in covalent drug discovery: an overview. Expert Opin Drug Discov. 2022;17 (4):413-422. doi:10.1080/17460441.2022.2034783
86. Ray S, Murkin AS. New Electrophiles and Strategies for Mechanism-Based and Targeted Covalent Inhibitor Design. Biochemistry. 2019;58 (52):5234-5244. doi:10.1021/acs.biochem.9b00293
87. Dang CV, Reddy EP, Shokat KM, Soucek L. Drugging the "undruggable" cancer targets. Nat Rev Cancer. 2017;17(8):502-508. doi:10.1038/nrc.2017.36
88. Li Y, Han L, Zhang Z. Understanding the influence of AMG 510 on the structure of KRASG12C empowered by molecular dynamics simulation. Comput Struct Biotechnol J. 2022;20:1056-1067. doi:10.1016/j.csbj.2022.02.018
89. Solanki HS, Stewart PA, Welsh EA, et al. Abstract 3911: Proteomic and transcriptomic subtypes of KRASG12C mutant lung cancer. Cancer Res. 2022; 82(12_Supplement):3911. doi:10.1158/1538-744 5.AM2022-3911
90. You M, Liu H, Li C. The Current Toolbox for Covalent Inhibitors: From Hit Identification to Drug Discovery. JACS Au. 2025;5(12):5866-5887. doi:10.1021/jacsau.5c01134
91. Gehringer M, Laufer SA. Emerging and Re-Emerging Warheads for Targeted Covalent Inhibitors: Applications in Medicinal Chemistry and Chemical Biology. J Med Chem. 2019;62(12):5673-5724. doi:10.1021/acs.jmedchem.8b01153
92. Ruiz-Gonzalez A. AI-Driven Chemical Design: Transforming the Sustainability of the Pharmaceutical Industry. Future Pharmacol. 2025;5(2):24. doi:10.3390/futurepharmacol5020024
2. Walker I, Newell H. Do molecularly targeted agents in oncology have reduced attrition rates? Nat Rev Drug Discov. 2009;8(1):15.
3. Cragg GM, Pezzuto JM. Natural products as a vital source for the discovery of cancer chemotherapeutic and chemopreventive agents. Med Princ Pract. 2016; 25(Suppl. 2):41-59.
4. Mann J. Natural products in cancer chemotherapy: past, present and future. Nat Rev Cancer. 2002;2 (2):143-148.
5. Showell GA, Mills JS. Chemistry challenges in lead optimization: silicon isosteres in drug discovery. Drug Discov Today. 2003;8(12):551-556.
6. Wender PA, Verma VA, Paxton TJ, Pillow TH. Function-oriented synthesis, step economy, and drug design. Acc Chem Res. 2008;41(1):40-49.
7. Bellmann L, Penner P, Gastreich M, Rarey M. Comparison of Combinatorial Fragment Spaces and Its Application to Ultralarge Make-on-Demand Compound Catalogs. J Chem Inf Model. 2022;62 (3):553-566. doi:10.1021/acs.jcim.1c01378
8. Doveston R, Marsden S, Nelson A. Towards the realisation of lead-oriented synthesis. Drug Discov Today. 2014;19(7):813-819.
9. Dai T, Vijayakrishnan S, Szczypiński FT, et al. Autonomous mobile robots for exploratory synthetic chemistry. Nature. 2024;635(8040):890-897. doi:10.1038/s41586-024-08173-7
10. Neklesa TK, Winkler JD, Crews CM. Targeted protein degradation by PROTACs. Pharmacol Ther. 2017;174:138-144. doi:10.1016/j.pharmthera.201 7.02.027
11. Mosoh DA. Widely-targeted in silico and in vitro evaluation of veratrum alkaloid analogs as FAK inhibitors and dual targeting of FAK and Hh/SMO pathways for cancer therapy: A critical analysis. Int J Biol Macromol. Published online October 4, 2024:136201. doi:10.1016/j.ijbiomac.2024.136201 12. Mosoh DA. Recent Advances in Phytochemical Research for Cancer Treatment. IntechOpen; 2024. doi:10.5772/intechopen.1007200
13. Xiao Z, Morris‐Natschke SL, Lee K. Strategies for the optimization of natural leads to anticancer drugs or drug candidates. Med Res Rev. 2016;36 (1):32-91.
14. Rodrigues T, Reker D, Schneider P, Schneider G. Counting on natural products for drug design.
Nat Chem. 2016;8(6):531-541.
15. Huang M, Lu JJ, Ding J. Natural products in cancer therapy: Past, present and future. Nat Prod Bioprospecting. 2021;11(1):5-13.
16. Eastgate MD, Schmidt MA, Fandrick KR. On the design of complex drug candidate syntheses in the pharmaceutical industry. Nat Rev Chem. 2017;1(2): 0016.
17. Truax NJ, Romo D. Bridging the gap between natural product synthesis and drug discovery. Nat Prod Rep. 2020;37(11):1436-1453.
18. Liu J, Liu X, Wu J, Li CC. Total synthesis of natural products containing a bridgehead double bond. Chem. 2020;6(3):579-615.
19. Yang W, Gadgil P, Krishnamurthy VR, et al. The evolving druggability and developability space: chemically modified new modalities and emerging small molecules. AAPS J. 2020;22(2):21.
20. Surade S, Blundell TL. Structural biology and drug discovery of difficult targets: the limits of ligandability. Chem Biol. 2012;19(1):42-50.
21. Skoraczyński G, Kitlas M, Miasojedow B, Gambin A. Critical assessment of synthetic accessibility scores in computer-assisted synthesis planning. J Cheminformatics. 2023;15(1):6.
22. Zhang Y, Zhang Z, Wen L, Liu Y. Synthetic accessibility scoring and its application to the high-throughput design of energetic molecules. J Mater Chem A. 2025;13(22):16330-16344.
23. Yu J, Wang J, Zhao H, et al. Organic compound synthetic accessibility prediction based on the graph attention mechanism. J Chem Inf Model. 2022;62(12):2973-2986.
24. Moreno L, Pearson AD. How can attrition rates be reduced in cancer drug discovery? Expert Opin Drug Discov. 2013;8(4):363-368.
25. Unibest Industrial. Scaling the Everest of Pharma Synthesis: A Deep Dive into Eribulin's Journey - Unibest Industrial Co., Ltd. 2024. Accessed February 5, 2026. https://www.unibestpharm.com/Scaling-the-Everest-of-Pharma-Synthesis-A-Deep-Dive-into-Eribulin-s-Journey-id48528486.html
26. Baran PS. Natural product total synthesis: as exciting as ever and here to stay. J Am Chem Soc. 2018;140(14):4751-4755.
27. Tamura T, Kawano M, Hamachi I. Targeted covalent modification strategies for drugging the undruggable targets. Chem Rev. 2025;125(2):1191-1253.
28. Chen FE, Huang J. Reserpine: a challenge for total synthesis of natural products. Chem Rev. 2005; 105(12):4671-4706.
29. Morrill LA, Susick RB, Chari JV, Garg NK. Total Synthesis as a Vehicle for Collaboration. J Am Chem Soc. 2019;141(32):12423-12443.
30. Reisman SE, Maimone TJ. Total synthesis of complex natural products: more than a race for molecular summits. Acc Chem Res. 2021;54(8):18 15-1816.
31. Rishton GM. Natural products as a robust source of new drugs and drug leads: past successes and present day issues. Am J Cardiol. 2008;101(10):S43-S49.
32. Thomford NE, Senthebane DA, Rowe A, et al. Natural products for drug discovery in the 21st century: innovations for novel drug discovery. Int J Mol Sci. 2018;19(6):1578.
33. Paterson I, Lam NYS. Challenges and discoveries in the total synthesis of complex polyketide natural products. J Antibiot (Tokyo). 2018;71(2):215-233.
34. Blakemore DC, Castro L, Churcher I, et al. Organic synthesis provides opportunities to transform drug discovery. Nat Chem. 2018;10(4):383-394.
35. Chen K, Wu F, Lei X. Function‐oriented natural product synthesis. Chin J Chem. 2021;39(4):838-854.
36. Levin MD, Sarpong R, Wendlandt AE. What is Editing? Acc Chem Res. 2025;58(11):1725-1726.
37. Ma C, Lindsley CW, Chang J, Yu B. Rational molecular editing: a new paradigm in drug discovery. J Med Chem. 2024;67(14):11459-11466.
38. Morrison KC, Hergenrother PJ. Natural products as starting points for the synthesis of complex and diverse compounds. Nat Prod Rep. 2013;31(1):6-14.
39. Melvin JY, Zheng W, Seletsky BM, Littlefield BA, Kishi Y. Case history: discovery of eribulin (HalavenTM), a halichondrin B analogue that prolongs overall survival in patients with metastatic breast cancer. In: Annual Reports in Medicinal Chemistry. Vol 46. Elsevier; 2011:227-241.
40. Irwin JJ, Tang KG, Young J, et al. ZINC20—A Free Ultralarge-Scale Chemical Database for Ligand Discovery. J Chem Inf Model. 2020;60(12):6065-6073. doi:10.1021/acs.jcim.0c00675
41. Lyu J, Irwin JJ, Shoichet BK. Modeling the expansion of virtual screening libraries. Nat Chem Biol. 2023;19(6):712-718. doi:10.1038/s41589-022-01234-w
42. Bui L, Djikic-Stojsic T, Bret G, Bihel F, Kellenberger E. Scaffold-Based Libraries Versus Make-on-Demand Space: A Comparative Assessment of Chemical Content. ChemMedChem. 2025;20 (24):e202500518. doi:10.1002/cmdc.202500518
43. Popov KI, Wellnitz J, Maxfield T, Tropsha A. HIt Discovery using docking ENriched by GEnerative Modeling (HIDDEN GEM): A novel computational workflow for accelerated virtual screening of ultra-large chemical libraries. Mol Inform. 2024;43(1): e202300207. doi:10.1002/minf.202300207
44. Kuan J, Radaeva M, Avenido A, Cherkasov A, Gentile F. Keeping pace with the explosive growth of chemical libraries with structure-based virtual screening. WIREs Comput Mol Sci. 2023;13(6): e1678. doi:10.1002/wcms.1678
45. Festel G. Outsourcing chemical synthesis in the drug discovery process. Drug Discov Today. 2011; 16(5):237-243. doi:10.1016/j.drudis.2011.01.002
46. Jackson V, Jordan L, Burgin RN, McGaw OJS, Muir CW, Ceban V. Application of Molecular-Modeling, Scaffold-Hopping, and Bioisosteric Approaches to the Discovery of New Heterocyclic Picolinamides. J Agric Food Chem. 2022;70(36): 11031-11041. doi:10.1021/acs.jafc.2c03755
47. Muegge I, Bentzien J, Ge Y. Perspectives on current approaches to virtual screening in drug discovery. Expert Opin Drug Discov. 2024;19(10): 1173-1183. doi:10.1080/17460441.2024.2390511
48. Mishra A, Thakur A, Sharma R, et al. Scaffold hopping approaches for dual-target antitumor drug discovery: opportunities and challenges. Expert Opin Drug Discov. 2024;19(11):1355-1381. doi:10.108 0/17460441.2024.2409674
49. AlKharboush DF, Kozielski F, Wells G, Porta EOJ. Fragment-based drug discovery: A graphical review. Curr Res Pharmacol Drug Discov. 2025;9: 100233. doi:10.1016/j.crphar.2025.100233
50. Khedkar NR, Sindkhedkar M, Joseph A. Fragment-Based Drug Discovery: Small Fragments, Big Impact − Success Stories of Approved Oncology Therapeutics. Bioorganic Chem. 2025;156:108197. doi:10.1016/j.bioorg.2025.108197
51. Bon M, Bilsland A, Bower J, McAulay K. Fragment-based drug discovery—the importance of high-quality molecule libraries. Mol Oncol. 2022; 16(21):3761-3777. doi:10.1002/1878-0261.13277
52. Fesik SW. Drugging Challenging Cancer Targets Using Fragment-Based Methods. Chem Rev. 2025; 125(6):3586-3594. doi:10.1021/acs.chemrev.4c00892
53. Peterson AA, Liu DR. Small-molecule discovery through DNA-encoded libraries. Nat Rev Drug Discov. 2023;22(9):699-722. doi:10.1038/s41573-023-00713-6
54. Song M, Hwang GT. DNA-Encoded Library Screening as Core Platform Technology in Drug Discovery: Its Synthetic Method Development and Applications in DEL Synthesis. J Med Chem. 2020;63(13):6578-6599. doi:10.1021/acs.jmedche m.9b01782
55. Poupart J. DNA-Encoded Libraries in Cancer Research: Recent Landmarks and Future Promises. Published online October 16, 2025. doi:10.114 6/annurev-cancerbio-071124-011719
56. Gao Y, Liu J, Huang S, Du N, Zhang G, Li Y. Recent advances in DNA-encoded libraries. Chem Commun. 2025;61(59):10952-10968. doi:10.1039/D 5CC02904J
57. Wang X, Li L, Shen X, Lu X. Rational Design Strategies in DNA-Encoded Libraries for Drug Discovery. Angew Chem Int Ed. 2025;64(34):e2 02511839. doi:10.1002/anie.202511839
58. Abel R, Wang L, Harder ED, Berne BJ, Friesner RA. Advancing Drug Discovery through Enhanced Free Energy Calculations. Acc Chem Res. 2017;50 (7):1625-1632. doi:10.1021/acs.accounts.7b00083
59. de Oliveira C, Leswing K, Feng S, Kanters R, Abel R, Bhat S. FEP Protocol Builder: Optimization of Free Energy Perturbation Protocols Using Active Learning. J Chem Inf Model. 2023;63(17):5592-5603. doi:10.1021/acs.jcim.3c00681
60. Moraca F, Negri A, de Oliveira C, Abel R. Application of Free Energy Perturbation (FEP+) to Understanding Ligand Selectivity: A Case Study to Assess Selectivity Between Pairs of Phosphodiesterases (PDE's). J Chem Inf Model. 2019;59(6):2729-2740. doi:10.1021/acs.jcim.9b00106
61. Loeffler HH, He J, Tibo A, et al. Reinvent 4: Modern AI–driven generative molecule design. J Cheminformatics. 2024;16(1):20. doi:10.1186/s13321-024-00812-5
62. Moon SW, Ahn SH, Ahn JH, Kim HW. R2GB-GA: A Reaction-Regulated Graph-Based Genetic Algorithm for Exploring Synthesizable Chemical Space. Published online 2025.
63. Parrot M, Tajmouati H, da Silva VBR, et al. Integrating synthetic accessibility with AI-based generative drug design. J Cheminformatics. 2023; 15(1):83. doi:10.1186/s13321-023-00742-8
64. Zhang X, Liu J, Xu B, et al. SynFrag: Synthetic Accessibility Predictor based on Fragment Assembly Generation in Drug Discovery. Published online 2025.
65. Katsuyama A, Tokodai Y, Hoshi K, Ichikawa S. Retrosynthetic Analysis-Aware Fragment Linking for Structure-Guided Ligand Extension. Published online 2025.
66. Khater T, Alkhatib SA, AlShehhi A, et al. Generative artificial intelligence based models optimization towards molecule design enhancement. J Cheminformatics. 2025;17(1):116. doi:10.1186/s 13321-025-01059-4
67. van den Broek RL, Patel S, van Westen GJP, Jespers W, Sherman W. In Search of Beautiful Molecules: A Perspective on Generative Modeling for Drug Design. J Chem Inf Model. 2025;65(18):
9383-9397. doi:10.1021/acs.jcim.5c01203
68. Meyers J, Fabian B, Brown N. De novo molecular design and generative models. Drug Discov Today. 2021;26(11):2707-2715. doi:10.1016/j.drudis.202 1.05.019
69. Basak S, Bandyopadhyay A. Toward Making Polymer Chemistry Autonomous. ACS Appl Eng Mater. 2024;2(5):1190-1208. doi:10.1021/acsaen m.4c00214
70. Struble TJ, Alvarez JC, Brown SP, et al. Current and Future Roles of Artificial Intelligence in Medicinal Chemistry Synthesis. J Med Chem. 2020;63(16): 8667-8682. doi:10.1021/acs.jmedchem.9b02120
71. Wang G, Ang HT, Dubbaka SR, O'Neill P, Wu J. Multistep automated synthesis of pharmaceuticals. Trends Chem. 2023;5(6):432-445. doi:10.1016/j.tre chm.2023.03.008
72. Ali RSAE, Meng J, Jiang X. Synergy of Machine Learning and High-Throughput Experimentation: A Road Toward Autonomous Synthesis. Chem – Asian J. 2025;20(20):e00825. doi:10.1002/asia.202500825
73. Zhong L, Xu Y, Li X, Cheng P, Tao S. Production and evaluation of high-throughput reaction data from an automated chemical synthesis platform. Sci China Chem. 2025;68(10):5322-5331. doi:10.1007/s11426-025-2676-x
74. Eyke NS, Koscher BA, Jensen KF. Toward Machine Learning-Enhanced High-Throughput Experimentation. Trends Chem. 2021;3(2):120-132. doi:10.1016/j.trechm.2020.12.001
75. Mennen SM, Alhambra C, Allen CL, et al. The Evolution of High-Throughput Experimentation in Pharmaceutical Development and Perspectives on the Future. Org Process Res Dev. 2019;23(6):1213-1242. doi:10.1021/acs.oprd.9b00140
76. Xiao W, Su H, Ma Y, et al. Integration of Machine Learning and Automated Synthesis for Accelerated Drug and Material Research. ChemistrySelect. 2025;10(42):e04970. doi:10.1002/slct.202504970
77. Liang X, Ren H, Han F, Liang R, Zhao J, Liu H. The new direction of drug development: Degradation of undruggable targets through targeting chimera technology. Med Res Rev. 2024;44(2):632-685. doi:10.1002/med.21992
78. Baillie TA. Approaches to mitigate the risk of serious adverse reactions in covalent drug design. Expert Opin Drug Discov. 2021;16(3):275-287. doi:10.1080/17460441.2021.1832079
79. Fan G, Chen S, Zhang Q, et al. Proteolysis-Targeting Chimera (PROTAC): Current Applications and Future Directions. MedComm. 2025;6(10): e70401. doi:10.1002/mco2.70401
80. Vetma V, O'Connor S, Ciulli A. Development of PROTAC Degrader Drugs for Cancer. Annu Rev Cancer Biol. 2025;9(Volume 9, 2025):119-140. doi:10.1146/annurev-cancerbio-061824-105806
81. Sasso JM, Tenchov R, Wang D, Johnson LS, Wang X, Zhou QA. Molecular Glues: The Adhesive Connecting Targeted Protein Degradation to the Clinic. Biochemistry. 2023;62(3):601-623. doi:10.102 1/acs.biochem.2c00245
82. Jung H, Lee Y. Targeting the Undruggable: Recent Progress in PROTAC-Induced Transcription Factor Degradation. Cancers. 2025;17(11):1871. doi:10.3390/cancers17111871
83. Salama AKAA, Trkulja MV, Casanova E, Uras IZ. Targeted Protein Degradation: Clinical Advances in the Field of Oncology. Int J Mol Sci. 2022;23 (23):15440. doi:10.3390/ijms232315440
84. Baillie TA. Targeted Covalent Inhibitors for Drug Design. Angew Chem Int Ed. 2016;55(43): 13408-13421. doi:10.1002/anie.201601091
85. Péczka N, Orgován Z, Ábrányi-Balogh P, Keserű GM. Electrophilic warheads in covalent drug discovery: an overview. Expert Opin Drug Discov. 2022;17 (4):413-422. doi:10.1080/17460441.2022.2034783
86. Ray S, Murkin AS. New Electrophiles and Strategies for Mechanism-Based and Targeted Covalent Inhibitor Design. Biochemistry. 2019;58 (52):5234-5244. doi:10.1021/acs.biochem.9b00293
87. Dang CV, Reddy EP, Shokat KM, Soucek L. Drugging the "undruggable" cancer targets. Nat Rev Cancer. 2017;17(8):502-508. doi:10.1038/nrc.2017.36
88. Li Y, Han L, Zhang Z. Understanding the influence of AMG 510 on the structure of KRASG12C empowered by molecular dynamics simulation. Comput Struct Biotechnol J. 2022;20:1056-1067. doi:10.1016/j.csbj.2022.02.018
89. Solanki HS, Stewart PA, Welsh EA, et al. Abstract 3911: Proteomic and transcriptomic subtypes of KRASG12C mutant lung cancer. Cancer Res. 2022; 82(12_Supplement):3911. doi:10.1158/1538-744 5.AM2022-3911
90. You M, Liu H, Li C. The Current Toolbox for Covalent Inhibitors: From Hit Identification to Drug Discovery. JACS Au. 2025;5(12):5866-5887. doi:10.1021/jacsau.5c01134
91. Gehringer M, Laufer SA. Emerging and Re-Emerging Warheads for Targeted Covalent Inhibitors: Applications in Medicinal Chemistry and Chemical Biology. J Med Chem. 2019;62(12):5673-5724. doi:10.1021/acs.jmedchem.8b01153
92. Ruiz-Gonzalez A. AI-Driven Chemical Design: Transforming the Sustainability of the Pharmaceutical Industry. Future Pharmacol. 2025;5(2):24. doi:10.3390/futurepharmacol5020024