Computational Molecular Biology 2024, Vol.14, No.1, 20-27 http://bioscipublisher.com/index.php/cmb 26 AI in drug discovery, and have achieved a series of encouraging results (Du et al., 2022). However, AI still faces a number of challenges in drug discovery, including data quality and privacy protection, interpretability and transparency, and ethical and legal issues. Addressing these challenges requires interdisciplinary cooperation and joint efforts, including strengthening data quality control and privacy protection mechanisms, improving the interpretability and transparency of AI models, and complying with relevant ethical norms and laws and regulations. In the face of the many challenges in the field of drug discovery, international cooperation and sharing of data resources have become particularly important. Governments, research institutions and companies should work together to develop data standards and sharing agreements, and establish mechanisms for sharing data across national borders. An open data platform and database should be established to integrate and share various types of drug research and development data to provide broader data resource support for global drug discovery research (Liang et al., 2020). In addition, international personnel exchanges and cooperation should be strengthened to jointly train and introduce researchers with interdisciplinary backgrounds and AI expertise, and promote the innovative application of AI in drug discovery. In the future, the application of AI in drug discovery will show the following trends. AI technology will be more popular and mature, including deep learning, transfer learning, reinforcement learning and other technologies will be more widely used, and combined with traditional drug discovery methods, to achieve more efficient drug screening and optimization. The application of personalized medicine and precision medicine therapy will be more prominent. Through the analysis of patients' genomic data, clinical manifestation data and drug metabolism data, combined with artificial intelligence technology, personalized drug compatibility and treatment plan design can be achieved to improve the accuracy and effectiveness of drug therapy. Applications in clinical trial design and data analysis will also be further strengthened. By simulating clinical trial process, optimizing sample selection and data analysis methods, improve the efficiency and success rate of clinical trials, and accelerate the marketing and clinical application of new drugs. The application of drug safety evaluation and drug side effect prediction will also become a research hotspot in the future. By integrating various types of biomedical data and pharmaceutical chemistry data, combined with artificial intelligence technology, rapid prediction and evaluation of drug safety and side effects can be achieved to provide a more comprehensive. Conflict of Interest Disclosure The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Aliper A., Plis S., Artemov A., Ulloa A., Mamoshina P., and Zhavoronkov A., 2016, Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data, Molecular Pharmaceutics, 13(7): 2524-2530. https://doi.org/10.1021/acs.molpharmaceut.6b00248 Blanco-González A, Cabezón A, Seco-González A, Conde-Torres D, Antelo-Riveiro P., Piñeiro Á, Garcia-Fandino R.,2023, The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies. Pharmaceuticals (Basel), 16(6): 891. https://doi.org/10.3390/ph16060891 Campillos M., Kuhn M., Gavin A.C., Jensen L.J., and Bork P., 2008, Drug target identification using side-effect similarity, Science, 321(5886): 263-266. https://doi.org/10.1126/science.1158140 Ching T., Himmelstein D.S., Beaulieu-Jones B.K., Kalinin A.A., Brian T Do B.T., Way G.P., Ferrero E., Agapow P., Zietz M., Hoffman M.M., Xie W., Rosen G.L., Lengerich B.J., Israeli J., Lanchantin J., Woloszynek S., Carpenter A.E., Shrikumar A., Xu J.B., Cofer E.M., Lavender C.A., Srinivas C Turaga S.C., Alexandari A.M., Lu Z.Y., Harris D.J., DeCaprio D., Qi Y.J., Kundaje A., Peng Y.F., Wiley L.K., Segler M.H.S., Boca S.M., Swamidass S.J., Huang A., Gitter A., and Greene C.S., 2018, Opportunities and obstacles for deep learning in biology and medicine, Journal of The Royal Society Interface, 15(141): 20170387. https://doi.org/10.1098/rsif.2017.0387 Du H., Wu Y.F., and Du X., 2022, Dvances in application of Artificial Intelligence in new drug research and development, Progress in Pharmaceutical Sciences, 46(11): 875-880. Ekins S., Puhl A.C., Zorn K.M., Lane T.R., Russo D.P., Klein J.J., and Hickey A.J., 2019, Exploiting machine learning for end-to-end drug discovery and development, Nature Reviews Drug Discovery, 18(8): 435-441. https://doi.org/10.1038/s41563-019-0338-z
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