Computational Molecular Biology 2024, Vol.14, No.1, 20-27 http://bioscipublisher.com/index.php/cmb 21 1 Application in Drug Discovery 1.1 Molecular design and drug screening Campillos et al. (2008) believe that molecular design is a key step in the drug discovery process, with the goal of finding molecular structures with specific biological activities. Traditional methods of molecular design, which rely mainly on the expertise and experience of chemists, are inefficient. AI technology can learn from large amounts of compound data and predict the biological activity of molecules through methods such as deep learning and machine learning, thereby assisting chemists to quickly screen potential drug candidates. Deep neural networks have been successfully applied to predict the biological activity of molecules. These models can predict the biological activity of new molecules by learning the structural characteristics of known active molecules, thus screening potential drug candidates. In addition, molecular generation methods based on generative adversarial networks (Gans) can also automatically generate molecular structures with specific biological activities, further expanding the scope of drug screening. The advantages of AI technology in drug research and development are first reflected in its powerful computing power. Ching et al. (2018) Using deep learning and big data analysis, AI can simulate the interaction between molecules and biological targets, and predict the way and effect of combining drugs with organisms. This predictive ability allows researchers to make an initial assessment of a drug's effects before a trial, and thus to identify the most likely candidates for success. This not only greatly shortens the drug development cycle, but also reduces the cost of research and development. In addition to molecular design and drug screening, AI also plays an important role in other drug development processes. In the field of bioinformatics, AI technology can process massive genomics, proteomics and other big data to help researchers reveal the mechanism of drug action and targets. The understanding of these mechanisms provides a clearer direction and goal for drug development, and further improves the success rate of drug development (Figure 1). Figure 1 Analysis and screening of the drugs were performed using the AI software 1.2 Optimization of drug development process Huang et al. (2019) proposed that AI could not only play a role in drug screening and molecular design, but also optimize the entire drug development process. The process of drug development involves many steps, including the synthesis of compounds, the testing of biological activity, and the study of pharmacokinetics. AI technology can improve the efficiency of drug development by optimizing these links. In terms of compound synthesis, AI can reduce the number and cost of experiments by predicting the synthesis route of compounds. The traditional drug discovery process is often a linear, progressive process, and the introduction of AI makes this process more flexible and efficient. AI can predict the possible results of the next experiment by analyzing the existing experimental data, so as to provide guidance for the experimenter and reduce ineffective experimental attempts.
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