Duarte Y, Márquez-Miranda V, Miossec MJ, González-Nilo F. Integration of target discovery, drug discovery and drug delivery: a review on computational strategies. Wiley Interdiscip Rev Nanomed Nanobiotechnol. 2019;11(4):e1554. https://doi.org/10.1002/wnan.1554.

Article  Google Scholar 

Gashaw I, Ellinghaus P, Sommer A, Asadullah K. What makes a good drug target? Drug Discov Today. 2011;16(23–24):1037–43. https://doi.org/10.1016/j.drudis.2011.09.007.

Article  Google Scholar 

Csermely P, Agoston V, Pongor S. The efficiency of multi-target drugs: the network approach might help drug design. Trends Pharmacol Sci. 2005;26(4):178–82. https://doi.org/10.1016/j.tips.2005.02.007.

Article  Google Scholar 

Fan K, Cheng L, Li L. Artificial intelligence and machine learning methods in predicting anti-cancer drug combination effects. Briefings Bioinf. 2021;22(6):bbab271. https://doi.org/10.1093/bib/bbab271.

Article  Google Scholar 

Mak K-K, Pichika MR. Artificial intelligence in drug development: present status and future prospects. Drug Discov Today. 2019;24(3):773–80. https://doi.org/10.1016/j.drudis.2018.11.014.

Article  Google Scholar 

Li G, Lin P, Wang K, Gu C-C, Kusari S. Artificial intelligence-guided discovery of anticancer lead compounds from plants and associated microorganisms. Trends Cancer. 2022;8(1):65–80. https://doi.org/10.1016/j.trecan.2021.10.002.

Article  Google Scholar 

Tolios A, De Las Rivas J, Hovig E, Trouillas P, Scorilas A, Mohr T. Computational approaches in cancer multidrug resistance research: identification of potential biomarkers, drug targets and drug-target interactions. Drug Res Updat: Rev Comment Antimicrob Anticancer Chemother. 2020;48:100662. https://doi.org/10.1016/j.drup.2019.100662.

Article  Google Scholar 

Zhou Y, Zhang Y, Lian X, Li F, Wang C, Zhu F, Qiu Y, Chen Y. Therapeutic target database update 2022: facilitating drug discovery with enriched comparative data of targeted agents. Nucleic Acids Res. 2022;50(D1):D1398–407. https://doi.org/10.1093/nar/gkab953.

Article  Google Scholar 

Wang L, Ding J, Pan L, Cao D, Jiang H, Ding X. Artificial intelligence facilitates drug design in the big data era. Chemom Intell Lab Syst. 2019;194:103850. https://doi.org/10.1016/j.chemolab.2019.103850.

Article  Google Scholar 

Liu G, Xie Y, Sun Y, Zhang K, Ma J, Huang Y. Drug research and development opportunities in low- and middle-income countries: accelerating traditional medicine through systematic utilization and comprehensive synergy. Infect Dis Poverty. 2022;11(1):27. https://doi.org/10.1186/s40249-022-00954-4.

Article  Google Scholar 

Paul D, Sanap G, Shenoy S, Kalyane D, Kalia K, Tekade RK. Artificial intelligence in drug discovery and development. Drug Discov Today. 2021;26(1):80–93. https://doi.org/10.1016/j.drudis.2020.10.010.

Article  Google Scholar 

Sun D, Gao W, Hu H, Zhou S. Why 90% of clinical drug development fails and how to improve it? Acta Pharmaceutica Sinica B. 2022;12(7):3049–62. https://doi.org/10.1016/j.apsb.2022.02.002.

Article  Google Scholar 

Takebe T, Imai R, Ono S. The current status of drug discovery and development as originated in united states academia: the influence of industrial and academic collaboration on drug discovery and development. Clin Transl Sci. 2018;11(6):597–606. https://doi.org/10.1111/cts.12577.

Article  Google Scholar 

Cook D, Brown D, Alexander R, March R, Morgan P, Satterthwaite G, Pangalos MN. Lessons learned from the fate of AstraZeneca’s drug pipeline: a five-dimensional framework. Nat Rev Drug Discov. 2014;13(6):419–31. https://doi.org/10.1038/nrd4309.

Article  Google Scholar 

Meyers RM, Bryan JG, McFarland JM, Weir BA, Sizemore AE, Xu H, Dharia NV, Montgomery PG, Cowley GS, Pantel S, Goodale A, Lee Y, Ali LD, Jiang G, Lubonja R, Harrington WF, Strickland M, Wu T, Hawes DC, Zhivich VA, Wyatt MR, Kalani Z, Chang JJ, Okamoto M, Stegmaier K, Golub TR, Boehm JS, Vazquez F, Root DE, Hahn WC, Tsherniak A. Computational correction of copy number effect improves specificity of CRISPR-Cas9 essentiality screens in cancer cells. Nat Genet. 2017;49(12):1779–84. https://doi.org/10.1038/ng.3984.

Article  Google Scholar 

Pinu FR, Beale DJ, Paten AM, Kouremenos K, Swarup S, Schirra HJ, Wishart D. Systems biology and multi-omics integration: viewpoints from the metabolomics research community. Metabolites. 2019;9(4):76. https://doi.org/10.3390/metabo9040076.

Article  Google Scholar 

Galluzzi L, Vitale I, Michels J, Brenner C, Szabadkai G, Harel-Bellan A, Castedo M, Kroemer G. Systems biology of cisplatin resistance: past, present and future. Cell Death Dis. 2014;5(5):e1257–e1257. https://doi.org/10.1038/cddis.2013.428.

Article  Google Scholar 

Keseler IM, Mackie A, Peralta-Gil M, Santos-Zavaleta A, Gama-Castro S, Bonavides-Martínez C, Fulcher C, Huerta AM, Kothari A, Krummenacker M, Latendresse M, Muñiz-Rascado L, Ong Q, Paley S, Schröder I, Shearer AG, Subhraveti P, Travers M, Weerasinghe D, Weiss V, Collado-Vides J, Gunsalus RP, Paulsen I, Karp PD. EcoCyc: fusing model organism databases with systems biology. Nucleic Acids Res. 2013;41(D1):D605–12. https://doi.org/10.1093/nar/gks1027.

Article  Google Scholar 

Gogleva A, Polychronopoulos D, Pfeifer M, Poroshin V, Ughetto M, Martin MJ, Thorpe H, Bornot A, Smith PD, Sidders B, Dry JR, Ahdesmäki M, McDermott U, Papa E, Bulusu KC. Knowledge graph-based recommendation framework identifies drivers of resistance in EGFR mutant non-small cell lung cancer. Nat Commun. 2022;13(1):1667. https://doi.org/10.1038/s41467-022-29292-7.

Article  Google Scholar 

Chandak P, Huang K, Zitnik M. Building a knowledge graph to enable precision medicine. Sci Data. 2023;10(1):1–16. https://doi.org/10.1038/s41597-023-01960-3.

Article  Google Scholar 

Zador A, Escola S, Richards B, Ölveczky B, Bengio Y, Boahen K, Botvinick M, Chklovskii D, Churchland A, Clopath C, DiCarlo J, Ganguli S, Hawkins J, Körding K, Koulakov A, LeCun Y, Lillicrap T, Marblestone A, Olshausen B, Pouget A, Savin C, Sejnowski T, Simoncelli E, Solla S, Sussillo D, Tolias AS, Tsao D. Catalyzing next-generation artificial intelligence through NeuroAI. Nat Commun. 2023;14(1):1597.

Article  Google Scholar 

Gupta RR. Application of artificial intelligence and machine learning in drug discovery. In: Heifetz A, editor. Artificial intelligence in drug design. New York: Springer; 2022. p. 113–24.

Chapter  Google Scholar 

Sarkar C, Das B, Rawat VS, Wahlang JB, Nongpiur A, Tiewsoh I, Lyngdoh NM, Das D, Bidarolli M, Sony HT. Artificial intelligence and machine learning technology driven modern drug discovery and development. Int J Mol Sci. 2023;24(3):2026. https://doi.org/10.3390/ijms24032026.

Article  Google Scholar 

Rashid MBMA. Artificial intelligence effecting a paradigm shift in drug development. SLAS Technol. 2021;26(1):3–15. https://doi.org/10.1177/2472630320956931.

Article  Google Scholar 

Recanatini M, Cabrelle C. Drug research meets network science: where are we? J Med Chem. 2020;63(16):8653–66. https://doi.org/10.1021/acs.jmedchem.9b01989.

Article  Google Scholar 

Li D, Hu J, Zhang L, Li L, Yin Q, Shi J, Guo H, Zhang Y, Zhuang P. Deep learning and machine intelligence: new computational modeling techniques for discovery of the combination rules and pharmacodynamic characteristics of traditional Chinese medicine. Eur J Pharmacol. 2022;933:175260. https://doi.org/10.1016/j.ejphar.2022.175260.

Article  Google Scholar 

She S, Chen H, Ji W, Sun M, Cheng J, Rui M, Feng C. Deep learning-based multi-drug synergy prediction model for individually tailored anti-cancer therapies. Front Pharmacol. 2022;13:1032875. https://doi.org/10.3389/fphar.2022.1032875.

Article  Google Scholar 

Adams SA, Petersen C. Precision medicine: opportunities, possibilities, and challenges for patients and providers. J Am Med Inf Assoc. 2016;23(4):787–90. https://doi.org/10.1093/jamia/ocv215.

Article  Google Scholar 

Fukushima K. Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol Cybern. 1980;36(4):193–202. https://doi.org/10.1007/BF00344251.

Article  Google Scholar 

Lipton Z. A critical review of recurrent neural networks for sequence learning. 2015.

Hinton GE, Osindero S, Teh Y-W. A fast learning algorithm for deep belief nets. Neural Comput. 2006;18(7):1527–54. https://doi.org/10.1162/neco.2006.18.7.1527.

Article  MathSciNet  Google Scholar 

Gerstein M. ENCODE leads the way on big data. Nature. 2012;489(7415):208–208. https://doi.org/10.1038/489208b.

Article  Google Scholar 

Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y. Generative adversarial nets. In: Proceedings of the 27th International Conference on Neural Information Processing Systems—Volume 2, MIT Press: Montreal, Canada, 2014; pp 2672–2680.

Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J., Devin M, Ghemawat S, Irving G, Isard M, Kudlur M, Levenberg J, Monga R, Moore S, Murray DG, Steiner B, Tucker PA, Vasudevan V, Warden P, Wicke M, Yu Y, Zhang X. In: TensorFlow: a system for large-scale machine learning, USENIX Symposium on Operating Systems Design and Implementation. 2016.

Ii B. The power of deep learning to ligand-based novel drug discovery. Expert Opin Drug Discov. 2020. https://doi.org/10.1080/17460441.2020.1745183.

Article  Google Scholar 

Bonner S, Barrett IP, Ye C, Swiers R, Engkvist O, Hoyt CT, Hamilton WL. Understanding the performance of knowledge graph embeddings in drug discovery. Artif Intell Life Sci. 2022;2:100036. https://doi.org/10.1016/j.ailsci.2022.100036.

Article  Google Scholar 

Acosta JN, Falcone GJ, Rajpurkar P, Topol EJ. Multimodal biomedical AI. Nat Med. 2022;28(9):1773–84. https://doi.org/10.1038/s41591-022-01981-2.

Article  Google Scholar 

Kırboğa KK, Abbasi S, Küçüksille EU. Explainability and white box in drug discovery. Chem Biol Drug Des. 2023;102(1):217–33. https://doi.org/10.1111/cbdd.14262.

Article  Google Scholar 

Zongsheng W, Xue R, Shao M. Knowledge graph analysis and visualization of AI technology applied in COVID-19. Environ Sci Pollut Res Int. 2022. https://doi.org/10.1007/s11356-021-17800-z.

Art