International Journal of Molecular Medical Science, 2024, Vol.14, No.1, 16-23 http://medscipublisher.com/index.php/ijmms 19 Furthermore, artificial intelligence can discover and validate molecular biomarkers by integrating diverse data to identify disease-related markers and conduct rapid validation. AI can also utilize individual genomic information, clinical data, and environmental factors to establish models for assessing an individual's risk of developing a particular disease, providing personalized disease prevention and intervention strategies for individuals. In summary, AI is widely applied in molecular medicine research, particularly in early disease diagnosis, offering researchers new methods and tools. 2.2 Recognition and prediction of molecular markers Molecular markers refer to molecular features associated with specific diseases or physiological states, such as genes, proteins, or metabolic products. The application of artificial intelligence (AI) technologies enables the rapid and accurate identification and prediction of these molecular markers (Ullah et al., 2020). The application of AI in molecular medicine research plays an important role in the recognition and prediction of molecular markers. By processing and analyzing large-scale molecular biology data and conducting integrated analyses of different data sources, AI assists researchers in gaining a better understanding of the molecular mechanisms underlying diseases and provides a scientific basis for early diagnosis and personalized treatment. Artificial intelligence can utilize machine learning and deep learning algorithms to analyze large-scale molecular biology data, such as genomic, transcriptomic, and proteomic data. By processing and pattern recognition of these data, artificial intelligence can uncover molecular features associated with diseases. For instance, through the analysis of extensive genomic data, artificial intelligence can identify genetic mutations associated with the occurrence and development of diseases. This aids in determining potential therapeutic targets and predicting the efficacy of drugs for specific individuals. Furthermore, artificial intelligence can leverage molecular image processing and analysis techniques to diagnose and classify diseases by analyzing the expression patterns of molecular markers in biological tissue slices or cell images. The comprehensive analysis of multiple data sources is also a powerful application of artificial intelligence in the identification and prediction of molecular markers. By integrating data from various dimensions, such as genomic, transcriptomic, proteomic data, as well as clinical manifestations, artificial intelligence can discover more precise and effective molecular markers, aiding in predicting the occurrence and development of diseases. 2.3 Drug design and discovery The application of artificial intelligence in molecular medicine research is extensive, with a significant role in drug design and discovery. Artificial intelligence can utilize machine learning and deep learning algorithms to analyze vast datasets of compounds and drug activity, thereby enabling drug screening and virtual screening to rapidly assess the potential activity and properties of candidate compounds. Additionally, through algorithms like generative adversarial networks, artificial intelligence can generate new drug molecules, expand chemical space and search for new drugs with better activity and selectivity. Artificial intelligence can predict the side effects and toxic effects of drugs, aiding in optimizing drug design and developing safer and more effective medications. By predicting and simulating the structure and function of proteins, artificial intelligence can also enhance the activity and selectivity of drugs. Furthermore, utilizing machine learning and data mining techniques, artificial intelligence can identify synergistic drug combinations to improve therapeutic outcomes. In summary, the application of artificial intelligence in molecular medicine research provides new perspectives and tools for drug design and discovery, accelerating the drug development process and offering greater possibilities for disease treatment (Liu, 2018). 2.4 Treatment response and personalized therapy The application of artificial intelligence in molecular medicine research extends to the realms of treatment response and personalized therapy. By analyzing multidimensional information such as clinical data, biomarker data, and genomic data, artificial intelligence can establish predictive models to forecast patients' responses to specific treatment modalities, thereby enabling personalized therapy. Additionally, through the integration of
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