Decoding Biological Networks: A Network Pharmacology Approach Powered by Artificial Intelligence

In recent years, significant advances have been made in elucidating the complex structure of biological systems and understanding diseases at the molecular level. These advances have shown that drug development strategies focusing on a single target are often inadequate in the treatment of complex diseases in which multiple genetic and environmental factors have a major impact . This has increased the importance of multidisciplinary approaches and led the scientific world to seek more holistic models. Systems biology-based approaches are gaining more and more importance, and the concept of network pharmacology, which evaluates the intrinsic dynamics of biological systems through networks, including multi-component, multi-target and multi-level analyzes, is coming to the fore. Network pharmacology has become a strategic tool in today's drug discovery processes, especially because it provides the opportunity to evaluate gene, protein and drug interactions at multiple levels. However, for this approach to be used efficiently, large and complex datasets need to be processed. At this point, the possibilities offered by artificial intelligence come into play. Artificial intelligence, especially machine learning and deep learning techniques, make the analysis of biological data much more meaningful and faster. Modeling processes that could take months with traditional methods can now be done in minutes. However, artificial intelligence has become not only an accelerator, but also a tool that can establish new relationships, make predictions and generate hypotheses. Network pharmacology aims to analyze the interactions of genes, proteins, metabolites, etc. through networks. However, these networks are often very large and complex, so classical statistical approaches may be insufficient. The role of artificial intelligence at this point is critical. Especially in studies conducted in countries with a strong traditional medicine background such as China and India, the effects of herbal compounds on the network have been systematically mapped with the help of artificial intelligence. Similarly, evaluating the ethnobotanical knowledge widespread in Anatolia with these methods can make important contributions to both the preservation of cultural heritage and the discovery of potential therapeutic candidates. Although the potential of AI-enabled network pharmacology is high, there are some challenges that slow the progress of this field. First of all, the decision processes of AI algorithms are often “black box” in nature, meaning that they have limited explainability, making biological interpretation difficult. Moreover, the quality, standardization and reliability of the data are factors that directly affect model performance. In particular, noise and missing data problems, which are frequently encountered in -omics data sets, can reduce the reliability of AI models. Another important aspect is the need for interdisciplinary collaboration. Developing a common language between biologists, computer scientists, pharmacologists and clinicians is critical for the successful implementation of these models. In this context, issues such as platforms that encourage open data sharing, the development of modular and explainable artificial intelligence models, and ensuring data security within the framework of ethical rules are expected to be more on the agenda in the coming years. Network pharmacology approaches supported by artificial intelligence pioneer the understanding of disease mechanisms at the system level and the development of effective treatment strategies. Especially in the modeling of multi-target and multi-component therapies, this approach offers more holistic and effective solutions compared to classical methods . This interdisciplinary field has great potential in terms of scientifically analyzing complex therapeutic elements such as herbal products, developing individualized medicine practices and accelerating drug discovery processes. Through this letter, I hope that more researchers will embrace, support and strengthen international collaborations in this powerful approach that combines artificial intelligence and network pharmacology.