Bioscience Method 2024, Vol.15, No.1, 37-49 http://bioscipublisher.com/index.php/bm 47 drugs based on individual genetic, physiological, and pathological characteristics (Gorshkov et al., 2019). These development trends will also have potential impacts. The overall speed and efficiency of drug development are expected to be significantly improved, accelerating the speed of new drug launch. Medical costs may be reduced due to the widespread use of AI drug screening, benefiting more people. However, as the application of AI in drug development becomes increasingly widespread, it may also trigger more ethical and regulatory issues, requiring the development of corresponding policies and regulations to regulate it. References Abbasnezhad A., Salami F., and Mohebbati R., 2022, A review: Systematic research approach on toxicity model of liver and kidney in laboratory animals, Animal Models and Experimental Medicine, 5: 436-444. https://doi.org/10.1002/ame2.12230 PMid:35918879 PMCid:PMC9610155 Alexander M., Schoeder C., Brown J., Smart C., Moth C., Wikswo J., Capra J., Meiler J., Chen W., and Madhur M., 2020, Predicting susceptibility to SARS-CoV-2 infection based on structural differences in ACE2 across species, The FASEB Journal, 34: 15946-15960. https://doi.org/10.1096/fj.202001808R PMid:33015868 PMCid:PMC7675292 Balbach S., and Korn C., 2004, Pharmaceutical evaluation of early development candidates "the 100 mg-approach", International Journal of Pharmaceutics, 275: 1-2, 1-12 . https://doi.org/10.1016/j.ijpharm.2004.01.034 PMid:15081133 Beaurivage C., Naumovska E., Chang Y., Elstak E., Nicolas A., Wouters H., Moolenbroek G., Lanz H., Trietsch S., Joore J., Vulto P., Janssen R., Erdmann K., Stallen J., and Kurek D., 2019, Development of a gut-on-a-chip model for high throughput disease modeling and drug discovery, International Journal of Molecular Sciences, 20(22): 5661. https://doi.org/10.3390/ijms20225661 PMid:31726729 PMCid:PMC6888156 Benson J., Chen Y., Cornell-Kennon S., Dorsch M., Kim S., Leszczyniecka M., Sellers W., and Lengauer C., 2006, Validating cancer drug targets, Nature, 441: 451-456. https://doi.org/10.1038/nature04873 PMid:16724057 Burton P., Banner N., Elliot M., Knoppers B., and Banks J., 2017, Policies and strategies to facilitate secondary use of research data in the health sciences, International Journal of Epidemiology, 46: 1729-1733. https://doi.org/10.1093/ije/dyx195 PMid:29025140 PMCid:PMC5837447 Cai J., Luo J., Wang S., and Yang S., 2018, Feature selection in machine learning: A new perspective, Neurocomputing, 300: 70-79. https://doi.org/10.1016/j.neucom.2017.11.077 Costa C., Frampas C., Longman K., Palitsin V., Ismail M., Sears P., Nilforooshan R., and Bailey M., 2019, Paper spray screening and liquid chromatography/mass spectrometry confirmation for medication adherence testing: A two‐step process, Rapid Communications in Mass Spectrometry, 35(S2): E8553. https://doi.org/10.1002/rcm.8553 PMid:31414505 PMCid:PMC8047880 Deng J., Yang Z., Samaras D., and Wang F., 2021, Artificial intelligence in drug discovery: applications and techniques, Briefings in bioinformatics, 23(1): bbab430. https://doi.org/10.1093/bib/bbab430 PMid:34734228 Dogan T., 2018, UniProt: a worldwide hub of protein knowledge, Nucleic Acids Research, 47: 506-515. https://doi.org/10.1093/nar/gky1049 PMid:30395287 PMCid:PMC6323992 Gorshkov K., Chen C., Marshall R., Mihatov N., Choi Y., Nguyen D., Southall N., Chen K., Park J., and Zheng W., 2019, Advancing precision medicine with personalized drug screening, Drug discovery today, 24(1): 272-278. https://doi.org/10.1016/j.drudis.2018.08.010 PMid:30125678 PMCid:PMC6372320 Hessler G., and Baringhaus K., 2018, Artificial Intelligence in Drug Design, Molecules: A Journal of Synthetic Chemistry and Natural Product Chemistry, 23(10): 2520. https://doi.org/10.3390/molecules23102520 PMid:30279331 PMCid:PMC6222615
RkJQdWJsaXNoZXIy MjQ4ODYzMg==