Cragg GM, Newman DJ. Natural products: a continuing source of novel drug leads. Biochem Biophys Acta. 2013;1830:3670–95.

Article  CAS  PubMed  Google Scholar 

Baker DD, Chu M, Oza U, Rajgarhia V. The value of natural products to future pharmaceutical discovery. Nat Prod Rep. 2007;24:1225–44.

Article  CAS  PubMed  Google Scholar 

Newman DJ, Cragg GM. Natural products as sources of new drugs over the nearly four decades from 01/1981 to 09/2019. J Nat Prod. 2020;83:770–803.

Article  CAS  PubMed  Google Scholar 

Brown DG, Lister T, May-Dracka TL. New natural products as new leads for antibacterial drug discovery. Bioorg Med Chem Let. 2014;24:413–8.

Article  CAS  Google Scholar 

Harvey AL, Edrada-Ebel R, Quinn RJ. The re-emergence of natural products for drug discovery in the genomics era. Nat Rev Drug Discov. 2015;14:111–29.

Article  CAS  PubMed  Google Scholar 

Hai Q-X, Hu K, Chen S-P, Fu Y-Y, Li X-N, Sun H-D, He H-P, Puno P-T. Silvaticusins A–D: ent-kaurane diterpenoids and a cyclobutane-containing ent-kaurane dimer from Isodon silvaticus. Nat Prod Bioprospect. 2024;14:1–8.

Article  Google Scholar 

Rashad M, Sampò S, Cataldi A, Zara S. Biological activities of gastropods secretions: snail and slug slime. Nat Prod Bioprospect. 2023;13:1–9.

Article  Google Scholar 

Banerjee S, Cabrera-Barjas G, Tapia J, Fabi JP, Delattre C, Banerjee A. Characterization of Chilean hot spring-origin Staphylococcus sp. BSP3 produced exopolysaccharide as biological additive. Nat Prod Bioprospect. 2024;14:1–16.

Article  Google Scholar 

Eberhardt L, Kumar K, Waldmann H. Exploring and exploiting biologically relevant chemical space. Curr Drug Targets. 2011;12:1531–46.

Article  CAS  PubMed  Google Scholar 

Chen J-C, Li W-L, Yao H-Q, Xu J-Y. Insights into drug discovery from natural products through structural modification. Fitoterapia. 2015;103:231–41.

Article  CAS  PubMed  Google Scholar 

Rodrigues T, Reker D, Schneider P, Schneider G. Counting on natural products for drug design. Nat Chem. 2016;8:531–41.

Article  CAS  PubMed  Google Scholar 

Yao H, Liu J-K, Xu S-T, Zhu Z-Y, Xu J-Y. The structural modification of natural products for novel drug discovery. Expert Opin Drug Discovery. 2017;12:121–40.

Article  CAS  Google Scholar 

Haynes RK, Fugmann B, Stetter J, Rieckmann K, Heilmann H-D, Chan H-W, Cheung M-K, Lam W-L, Wong H-N, Croft SL, Vivas L, Rattray L, Stewart L, Peters W, Robinson BL, Edstein MD, Kotecka B, Kyle DE, Beckermann B, Gerisch M, Radtke M, Schmuck G, Steinke W, Wollborn U, Schmeer K, Römer A. Artemisone—a highly active antimalarial drug of the artemisinin class. Angew Chem Int Ed. 2006;45:2082–8.

Article  CAS  Google Scholar 

Pereira AR, Strangman WK, Marion F, Feldberg L, Roll D, Mallon R, Hollander I, Andersen RJ. Synthesis of phosphatidylinositol 3-kinase (PI3K) inhibitory analogues of the sponge meroterpenoid liphagal. J Med Chem. 2010;53:8523–33.

Article  CAS  PubMed  Google Scholar 

Wang S-Z, Dong G-Q, Sheng C-Q. Structural simplification of natural products. Chem Rev. 2019;119:4180–220.

Article  CAS  PubMed  Google Scholar 

Gromiha MM, Harini K. Protein-nucleic acid complexes: docking and binding affinity. Curr Opin Struct Biol. 2025;90:1–9.

Article  Google Scholar 

Panigrahi D, Sahu SK, Panigrahi D, Sahu SK. Computational approaches: atom-based 3D-QSAR, molecular docking, ADME-Tox, MD simulation and DFT to find novel multi-targeted anti-tubercular agents. BMC Chem. 2025;19:1–28.

Article  Google Scholar 

Zhavoronkov A, Ivanenkov YA, Aliper A, Veselov MS, Aladinskiy VA, Aladinskaya AV, Terentiev VA, Polykovskiy DA, Kuznetsov MD, Asadulaev A, Volkov Y, Zholus A, Shayakhmetov RR, Zhebrak A, Minaeva LI, Zagribelnyy BA, Lee LH, Soll R, Madge D, Xing L, Guo T, Aspuru-Guzik A. Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nat Biotechnol. 2019;37:1038–40.

Article  CAS  PubMed  Google Scholar 

Jiang D, Hsieh C-Y, Wu Z-X, Kang Y, Wang J, Wang E, Liao B, Shen C, Xu L, Wu J, Cao D-S, Hou T-J. Interactiongraphnet: a novel and efficient deep graph representation learning framework for accurate protein–ligand interaction predictions. J Med Chem. 2021;64:18209–32.

Article  CAS  PubMed  Google Scholar 

Cao D-H, Chen M-A, Zhang R-Z, Wang Z-K, Huang M-L, Yu J, Jiang X-Y, Fan Z-H, Zhang W, Zhou H, Li X-T, Fu Z-Y, Zhang S-L, Zheng M-Y. SurfDock is a surface-informed diffusion generative model for reliable and accurate protein–ligand complex prediction. Nat Methods. 2024;22:310–22.

Article  PubMed  Google Scholar 

Zhang O, Lin H-T, Zhang H, Zhao H-F, Huang Y-F, Hsieh C-Y, Pan P-C, Hou T-J. Deep lead optimization: leveraging generative AI for structural modification. J Am Chem Soc. 2024;146:31357–70.

Article  CAS  PubMed  Google Scholar 

Acharya A, Nagpure M, Roy N, Gupta V, Patranabis S, Guchhait SK. How to nurture natural products to create new therapeutics: strategic innovations and molecule-to-medicinal insights into therapeutic advancements. Drug Discov Today. 2024;29:1–19.

Article  Google Scholar 

Cross S, Cruciani G. FragExplorer: GRID-based fragment growing and replacement. J Chem Inf Model. 2022;62:1224–35.

Article  CAS  PubMed  Google Scholar 

Schneider G, Neidhart W, Giller T, Schmid G. “Scaffold-Hopping” by topological pharmacophore search: a contribution to virtual screening. Angew Chem Int Ed. 1999;38:2894–6.

Article  CAS  Google Scholar 

Erlanson DA, McDowell RS, O’Brien T. Fragment-based drug discovery. J Med Chem. 2004;47:3463–82.

Article  CAS  PubMed  Google Scholar 

Jhoti H, Williams G, Rees DC, Murray CW, Jhoti H, Williams G, Rees DC, Murray CW. The “rule of three” for fragment-based drug discovery: where are we now? Nat Rev Drug Discov. 2013;12:1–2.

Article  Google Scholar 

Gagare S, Patil P, Jain A. Natural product-inspired strategies towards the discovery of novel bioactive molecules. Future J Pharm Sci. 2024;10:1–23.

Google Scholar 

Chen X, Varghese S, Zhang Z-Y, Du J-C, Ruan B-F, Baell JB, Liu X-H. Drug discovery and optimization based on the co-crystal structure of natural product with target. Eur J Med Chem. 2024;266:116–26.

Article  Google Scholar 

Sakano K, Furui K, Ohue M. NPGPT: natural product-like compound generation with GPT-based chemical language models. J Supercomput. 2024;81:1–16.

Google Scholar 

Welsch ME, Snyder SA, Stockwell BR. Privileged scaffolds for library design and drug discovery. Curr Opin Chem Biol. 2010;14:347–61.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Green H, Koes DR, Durrant JD. DeepFrag: a deep convolutional neural network for fragment-based lead optimization. Chem Sci. 2021;12:8036–47.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Yang S, Hwang D, Lee S, Ryu S, Hwang SJ. Hit and lead discovery with explorative RL and fragment-based molecule generation. arXiv preprint arXiv: 2110.01219 [Online], 2023.

Telepov A, Tsypin A, Khrabrov K, Yakukhnov S, Strashnov P, Zhilyaev P, Rumiantsev E, Ezhov D, Avetisian M, Popova O, Kadurin A. FREED++: improving RL agents for fragment-based molecule generation by thorough reproduction. arXiv preprint arXiv: 2401.09840 [Online], 2024.

Imrie F, Hadfield TE, Bradley AR, Deane CM. Deep generative design with 3D pharmacophoric constraints. Chem Sci. 2021;12:14577–89.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Hadfield TE, Imrie F, Merritt A, Birchall K, Deane CM. Incorporating target-specific pharmacophoric information into deep generative models for fragment elaboration. J Chem Inf Model. 2022;62:2280–92.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Eguida M, Schmitt-Valencia C, Hibert M, Villa P, Rognan D. Target-focused library design by pocket-applied computer vision and fragment deep generative linking. J Med Chem. 2022;65:13771–83.

Article