Page 45 - AIH-2-2

Artificial Intelligence in Health New drug discovery in the AI era

reactions: A systematic review and analysis. Crit Rev Toxicol. Artificial intelligence in drug discovery and development.
2016;46(6):477-489. Drug Discov Today. 2021;26(1):80-93.
doi: 10.3109/10408444.2016.1149452 doi: 10.1016/j.drudis.2020.10.010
10. Amacher DE. The primary role of hepatic metabolism in 21. Visan AI, Negut I. Integrating artificial intelligence for drug
idiosyncratic drug-induced liver injury. Expert Opin Drug discovery in the context of revolutionizing drug delivery.
Metab Toxicol. 2012;8(3):335-347. Life (Basel). 2024;14:233.
doi: 10.1517/17425255.2012.658041 doi: 10.3390/life14020233
11. Weaver RJ, Blomme EA, Chadwick AE, et al. Managing 22. Blanco-González A, Cabezón A, Seco-González A, et al. The
the challenge of drug-induced liver injury: A roadmap for role of AI in drug discovery: Challenges, opportunities, and
the development and deployment of preclinical predictive strategies. Pharmaceuticals (Basel). 2023;16(6):891.
models. Nat Rev Drug Discov. 2020;19(2):131-148.
doi: 10.3390/ph16060891
doi: 10.1038/s41573-019-0048-x
23. Hasselgren C, Oprea TI. Artificial intelligence for drug
12. Meunier L, Larrey D. Drug-induced liver injury: Biomarkers, discovery: Are we there yet? Annu Rev Pharmacol Toxicol.
requirements, candidates, and validation. Front Pharmacol. 2024;64:527-550.
2019;10:1482.
doi: 10.1146/annurev-pharmtox-040323-040828
doi: 10.3389/fphar.2019.01482
24. Qiu X, Li H, Steeg GV, Godzik A. Advances in AI for protein
13. Kotsampasakou E, Ecker GF. Predicting drug-induced structure prediction: Implications for cancer drug discovery
cholestasis with the help of hepatic transporters-an in silico and development. Biomolecules. 2024;14(3):339.
modelling approach. J Chem Inf Model. 2017;57(3):608-615.
doi: 10.3390/biom14030339
doi: 10.1021/acs.jcim.6b00518
25. Janet JP, Mervin L, Engkvist O. Artificial intelligence in
14. Aleo MD, Shah F, Allen S, et al. Moving beyond binary molecular de novo design: Integration with experiment.
predictions of human drug-induced liver injury (DILI) Curr Opin Struct Biol. 2023;80:102575.
toward contrasting relative risk potential. Chem Res Toxicol.
2020;33(1):223-328. doi: 10.1016/j.sbi.2023.102575
doi: 10.1021/acs.chemrestox.9b00262 26. Wang M, Wang Z, Sun H, et al. Deep learning approaches
for de novo drug design: An overview. Curr Opin Struct Biol.
15. Cook D, Brown D, Alexander R, et al. Lessons learned from 2022;72:135-144.
the fate of AstraZeneca’s drug pipeline: A five-dimensional
framework. Nat Rev Drug Discov. 2014;13:419-431. doi: 10.1016/j.sbi.2021.10.001
doi: 10.1038/nrd4309 27. Corso G, Stärk H, Jing B, Barzilay R, Jaakkola T. DiffDock:
Diffusion steps, twists and turns for molecular docking.
16. Van Norman GA. Limitations of animal studies for predicting Conference paper at ICLR; 2023.
toxicity in clinical trials: Part 2: Potential alternatives to the
use of animals in preclinical trials. JACC Basic Transl Sci. doi: 10.48550/arXiv.2210.01776
2019;4(7):845-854. 28. Turon G, Hlozek J, Woodland JG, Kumar A, Chibale K,
doi: 10.1016/j.jacbts.2020.03.010 Duran-Frigola M. First fully-automated AI/ML virtual
screening cascade implemented at a drug discovery centre
17. Van de Vliet M, Joossens M. The resemblance between in Africa. Nat Commun. 2023;14(1):5736.
bacterial gut colonization in pigs and humans.
Microorganisms. 2022;10(9):1831. doi: 10.1038/s41467-023-41512-2
doi: 10.3390/microorganisms10091831 29. Zhou G, Rusnac DV, Park H, et al. An artificial intelligence
accelerated virtual screening platform for drug discovery.
18. Rao M, Nassiri V, Alhambra C, et al. AI/ML models to Nat Commun. 2024;15:7761.
predict the severity of drug-induced liver injury for small
molecules. Chem Res Toxicol. 2023;36(7):1129-1139. doi: 10.1038/s41467-024-52061-7
doi: 10.1021/acs.chemrestox.3c00098 30. Yan Y, Zhao Y, Yao H, et al. RPBP: Deep retrosynthesis
reaction prediction based on byproducts. J Chem Inf Model.
19. Singh S, Gupta H, Sharma P, Sahi S. Advances in Artificial 2023;63(19):5956-5970.
Intelligence (AI)-assisted approaches in drug screening.
Artif Intell Chem. 2024;2(1):100039. doi: 10.1021/acs.jcim.3c00274
doi: 10.1016/j.aichem.2023.100039 31. Jiang Y, Yu Y, Kong M, et al. Artificial Intelligence for
retrosynthesis prediction. Engineering. 2023;25:32-50.
20. Paul D, Sanap G, Shenoy S, Kalyane D, Kalia K, Tekade RK.

Volume 2 Issue 2 (2025) 39 doi: 10.36922/aih.4423