Traditional Medicines (TMs) treatments are used widely in practically all regions of the world. The rich practical experience of TMs and the massive literature have been insufficiently exploited and utilized. While past TM research approaches were successful, they can no longer meet the needs of new drug development at present. Artificial intelligence (AI) technologies, including network pharmacology, bioinformatics, systems biology, computational biology, chemical informatics, machine learning, deep learning, image processing, computer vision, and other technologies to link herbal medicine, chemical composition, symptoms, diseases, drugs and targets, are needed to join in. As an emerging scientific methodology, AI provides new approaches for screening the main components and pathways of single herb or prescriptions, and predicting the mechanism of action.
The goal of this Research Topic is to discover novel methods and strategies for the development and evaluation of new medicines through AI technologies. This Research Topic will focus on the opportunities using AI, the challenges and obstacles encountered, as well as on novel ways to overcome these. A typical example is the need to understand the causality of effects (in a pharmacological sense) with research methodologies which in general offer correlations. This is addressed within
Frontiers in Pharmacology, for example, by requiring the assessment of
in silico studies using experimental pharmacological data.
In this Research Topic, we welcome the following subtopics, but are not limited to the following:
• Application of Artificial Intelligence, Big Data, and Cloud Computing technologies in pharmacology of TM.
• Computational detection of chemical basis and corresponding biological mechanisms.
• Technologies to speed up pharmacokinetic investigations and toxicity test of TM.
• Development of Artificial Intelligence solution for TM adaptive biomedical informatics.
• Methodologies to assist of TM formula construction and optimization.
• Screening of herbs, ingredients, and component combinations.
• Application of Artificial Intelligence in diagnosis and efficacy evaluation of TM.
• Developing approaches to ascertain the use of best practice principles in the use of AI in phytopharmacological research.
Note for the authors:All manuscripts submitted must comply with oour guidelines,
The Four Pillars of Best Practice in Ethnopharmacology (you can freely download the full version
here).
We will not accept manuscripts which are using purely
in silico data in the context of pharmacological research.
Frontiers in Pharmacology focuses on pharmacological research and, as such, experimental, data-driven approaches. More generally, the research must be driven by a clear research question or a testable hypothesis.
In addition, the Artificial Intelligence in TM Research Topic prefer the manuscripts that have:
• Potential high impact in pharmacology of TM.
• Strong novelty of method related to Artificial Intelligence in TM.
Manuscript types include Original Research and Review articles. Review articles can provide concise and critical updates on Artificial Intelligence in TM.
