Optimization of potential targets for antidepressant Chinese medicines: AI and multi-omics methods
Chaofang Lei, Zhigang Chen, Chongyang Ma, Le Xie, Dahua Wu, Jianbei Chen, Jiaxu Chen

TL;DR
This paper explores how combining AI and multi-omics methods can improve the discovery of targets for antidepressant Chinese medicines.
Contribution
The paper proposes integrating AI and multi-omics as a novel approach for optimizing antidepressant Chinese medicine targets.
Findings
Current evaluation methods for Chinese medicine are limited by single, qualitative indicators.
AI and multi-omics technologies offer a more comprehensive and effective way to evaluate Chinese medicine quality and efficacy.
The combination of AI and multi-omics is a promising direction for future drug target discovery in TCM.
Abstract
The complexity of traditional Chinese medicine (TCM), with its numerous components, targets, and varying efficacy, presents challenges for current evaluation methods. Most existing methods rely on single, qualitative indicators, which provide limited insight into the overall quality. These methods fail to fully capture the intrinsic quality, efficacy, and safety of Chinese medicine, highlighting the need for more advanced biological evaluation techniques. Target-based drug discovery has become the primary approach in pharmaceutical research and development, where drug targets play a crucial role in guiding the entire process. As our understanding deepens, integrating artificial intelligence (AI) with multi-omics technologies has opened new possibilities for enhancing treatment precision. AI’s efficiency in identifying drug targets marks a significant leap forward in drug discovery,…
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Taxonomy
TopicsComputational Drug Discovery Methods · Cell Image Analysis Techniques · Traditional Chinese Medicine Studies
