Bioscience Method 2024, Vol.15, No.1, 37-49 http://bioscipublisher.com/index.php/bm 45 When selecting animal models, it is necessary to consider factors such as the characteristics of the disease, the genetic background of the animal, physiological and pathological reactions, and the operability of the experiment. For example, Alexander (2020) found that certain diseases may be more prominent in specific species of animals, or certain animals may be more sensitive to specific drug responses. Singh and Seed (2021) discussed the importance of experimental animal models in drug discovery and development, particularly in understanding the origin, pathology, and development of safe and effective treatment and cure methods for human diseases. Although animal models are crucial in drug development, the low conversion rate of research results has led to many new drugs failing in clinical trials. This emphasizes the importance of selecting appropriate animal models and conducting precise experimental design in the early stages of drug development. 4.3 Analysis of experimental results and evaluation of the effectiveness of candidate drugs The analysis of experimental results involves interpreting and interpreting experimental data, as well as objectively evaluating the efficacy of candidate drugs. This process not only requires researchers to have solid statistical and data analysis abilities, but also requires a deep understanding of the background of drug development and the original intention of experimental design. When analyzing experimental results, it is necessary to control the quality of the collected data to ensure its completeness and accuracy. Process and analyze data using appropriate statistical methods and data analysis tools. This may include descriptive statistics, analysis of variance, regression analysis, etc., to reveal the patterns and trends behind the data. When evaluating the effectiveness of candidate drugs, it is necessary to comprehensively consider multiple indicators. The first is the pharmacodynamic indicator, which refers to the degree to which the drug affects the target biomolecule or cell in vitro or in vivo experiments. This is usually evaluated by comparing the differences between the drug treatment group and the control group. In addition, it is necessary to pay attention to pharmacokinetic indicators, understand the absorption, distribution, metabolism, and excretion processes of drugs in vivo, in order to predict their concentration changes and therapeutic effects in vivo. In addition to the above indicators, safety assessment is also an indispensable part of evaluating the effectiveness of candidate drugs. This includes an evaluation of the potential toxicity, mutagenicity, carcinogenicity, and other aspects of the drug. Through animal experiments and clinical trials, the safety of drugs can be comprehensively evaluated, providing important basis for subsequent clinical applications and marketing. When evaluating the effectiveness of candidate drugs, attention should also be paid to the reliability and reproducibility of experimental results. This requires researchers to follow scientific principles and norms in experimental design and data analysis, ensuring the accuracy and credibility of experimental results. Multiple repeated experiments are required to verify the stability and consistency of drug efficacy. 5 Case Studies 5.1 Analysis of successful cases of AI based drug screening In recent years, artificial intelligence (AI) technology has made significant progress in the field of drug screening, greatly accelerating the development process of new drugs. Here is a successful case of AI based drug screening: Case name: AlphaFold helps to develop COVID-19 drugs Case background: At the beginning of 2020, COVID-19 (SARS CoV-2) broke out in the world, and effective drugs and vaccines are urgently needed to deal with this global health crisis. The traditional drug development process is time-consuming and labor-intensive, so researchers have begun to explore the use of AI technology to accelerate the process of drug screening and design. AI technology application: In this case, AlphaFold is a deep learning based protein structure prediction tool developed by DeepMind in the UK. It can predict the three-dimensional structure of proteins by analyzing amino acid sequences. This technology has played a key role in the research and development of COVID-19 drugs.
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