Computational Molecular Biology 2024, Vol.14, No.1, 20-27 http://bioscipublisher.com/index.php/cmb 22 Blanco-Gonzalez et al. (2023) introduced that the predictive model based on the graph neural network can automatically plan the optimal synthesis route according to the structure of the target molecule. In terms of biological activity testing, AI can reduce the number of experiments necessary by building predictive models. These models can predict the biological activity of compounds based on their structural characteristics, thus screening drug candidates for further experimental validation. In terms of pharmacokinetic research, AI can also predict the efficacy and safety of drugs, and predict the absorption, distribution, metabolism and excretion processes of drugs in the body by analyzing a large number of experimental data, providing a basis for the optimal design of drugs. Help researchers screen potential drug candidates at an early stage, reducing the risk and cost of late-stage clinical trials. Another important role is to provide data support and analysis. In the process of drug development, a large amount of experimental data is generated, including the structure, biological activity, pharmacokinetics and so on. AI can deeply mine and analyze these data, find the correlation and rule between the data, and provide a more comprehensive and in-depth understanding of drug development (Figure 2). Figure 2 Through the optimization process of AI drug development 1.3 Application of artificial intelligence in clinical trial design and data analysis Clinical trials are an integral part of the drug development process. However, the design and implementation of clinical trials often face many challenges, such as the determination of sample size, the optimization of trial design, and the complexity of data analysis. In terms of clinical trial design, AI can provide the optimal trial design scheme according to the characteristics of diseases and the nature of drugs (Aliper et al., 2016). For example, using AI algorithms to accurately calculate the sample size can reduce unnecessary sample waste while ensuring the reliability of test results. In addition, AI can also control and adjust various variables in the test process to ensure the smooth progress of the test. In terms of data analysis, AI can efficiently process and analyze the massive data generated by clinical trials. Traditional data analysis methods are often difficult to cope with such a huge amount of data, but AI algorithms can complete the data cleaning, integration and analysis in a short time, so as to provide a more accurate and comprehensive interpretation of the test results. In addition, AI can also predict and simulate trial data to provide deeper understanding and guidance for drug development. Gupta et al. (2021) discuss the critical role of artificial intelligence, particularly artificial neural networks such as deep neural networks or recurrent networks, in drug discovery. Emphasis is placed on the significant impact of AI in quantitative structure-property relationships (QSPR) or quantitative structure-activity relationships (QSAR). AI's ability to design from scratch demonstrates its strength in generating new bioactive molecules with desired properties (Figure 3).
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