Uncovering the mechanisms of Yi Qi Tong Qiao Pill in the treatment of allergic rhinitis based on Network target analysis

Elucidating the role of artificial intelligence in drug development from the perspective of drug-target interactions

Drug development remains a critical issue in the field of biomedicine. With the rapid advancement of information technologies such as artificial intelligence (AI) and the advent of the big data era, AI-assisted drug development has become a new trend, particularly in predicting drug-target associations. To address the challenge of drug-target prediction, AI-driven models have emerged as powerful tools, offering innovative solutions by effectively extracting features from complex biological data, accurately modeling molecular interactions, and precisely predicting potential drug-target outcomes. Traditional machine learning (ML), network-based, and advanced deep learning architectures such as convolutional neural networks (CNNs), graph convolutional networks (GCNs), and transformers play a pivotal role. This review systematically compiles and evaluates AI algorithms for drug- and drug combination-target predictions, highlighting their theoretical frameworks, strengths, and limitations. CNNs effectively identify spatial patterns and molecular features critical for drug-target interactions. GCNs provide deep insights into molecular interactions via relational data, whereas transformers increase prediction accuracy by capturing complex dependencies within biological sequences. Network-based models offer a systematic perspective by integrating diverse data sources, and traditional ML efficiently handles large datasets to improve overall predictive accuracy. Collectively, these AI-driven methods are transforming drug-target predictions and advancing the development of personalized therapy. This review summarizes the application of AI in drug development, particularly in drug-target prediction, and offers recommendations on models and algorithms for researchers engaged in biomedical research. It also provides typical cases to better illustrate how AI can further accelerate development in the fields of biomedicine and drug discovery.

General expert consensus on the application of network pharmacology in the research and development of new traditional Chinese medicine drugs

The research and development of new traditional Chinese medicine (TCM) drugs have progressively established a novel system founded on the integration of TCM theory, human experience, and clinical trials (termed the "Three Combinations"). However, considering TCM's distinctive features of "syndrome differentiation and treatment" and "multicomponent formulations and complex mechanisms", current TCM drug development faces challenges such as insufficient understanding of the material basis and the overall mechanism of action and an incomplete evidence chain system. Moreover, significant obstacles persist in gathering human experience data, evaluating clinical efficacy, and controlling the quality of active ingredients, which impede the innovation process in TCM drug development. Network pharmacology, centered on the "network targets" theory, transcends the limitations of the conventional "single target" reductionist research model. It emphasizes the comprehensive effects of disease or syndrome biological networks as targets to elucidate the overall regulatory mechanism of TCM prescriptions. This approach aligns with the holistic perspective of TCM, offering a novel method consistent with TCM's holistic view for investigating the complex mechanisms of TCM and developing new TCM drugs. It is internationally recognized as a "next-generation drug research model". To advance the research of new tools, methods, and standards for TCM evaluation and to overcome fundamental, critical, and cutting-edge technical challenges in TCM regulation, this consensus aims to explore the characteristics, progress, challenges, applicable pathways, and specific applications of network pharmacology as a new theory, method, and tool in TCM drug development. The goal is to enhance the quality of TCM drug research and development and accelerate the efficiency of developing new TCM products.

Network pharmacology for traditional Chinese medicine in era of artificial intelligence

Traditional Chinese Medicine Network Pharmacology (TCM-NP) is an interdisciplinary discipline that integrates information science, systems biology, network science and pharmacology, providing a systematic research methodology for TCM studies. With the development of artificial intelligence (AI) and multi-omics technologies, TCM-NP has entered a new era and can incorporate multimodal and high-dimensional data in the context of big data to enhance both theoretical foundations and technical capabilities. Despite its advancement, TCM-NP still faces challenges, particularly in ensuring the quality of data and research, as well as achieving more profound scientific discoveries. The field needs further innovation to obtain more precise and biomedically meaningful results. Overall research progress in TCM-NP depends on developing more accurate algorithms together with utilizing higher-quality and larger-scale data. This paper gives a perspective on the trends and characteristics of TCM-NP development and application in the era of AI.