AI DRUG

人工智能和药物发现

关键词： Artificial intelligence Traditional Chinese Medicine Network Pharmacology

episodic ataxia type 2

CACNA1A

%0 Journal Article %T Advances in the Application of Traditional Chinese Medicine Using Artificial Intelligence: A Review. %A Sheng · Zhang/Zhang S %A Wei · Wang/Wang W %A Xitian · Pi/Pi X %A Zichun · He/He Z %A Hongying · Liu/Liu H %+ [Key Laboratory of Biorheological Science and Technology of Ministry of Education, College of Bioengineering, Chongqing University, Chongqing 400030, P. R. China., Department of Cardiology, Chongqing University Cancer Hospital, Chongqing 400030, P. R. China., Key Laboratory of Biorheological Science and Technology of Ministry of Education, College of Bioengineering, Chongqing University, Chongqing 400030, P. R. China., Department of Pulmonary Medicine, Chongqing Red Cross Hospital, Chongqing 400020, P. R. China., Key Laboratory of Biorheological Science and Technology of Ministry of Education, College of Bioengineering, Chongqing University, Chongqing 400030, P. R. China.] %J The American journal of Chinese medicine. %D 2023 51 %N 5 %P 1067-1083 %K [Artificial Intelligence,Decision Support,Knowledge Discovering,Pattern Recognition,Review,Traditional Chinese Medicine] %X Traditional Chinese medicine (TCM), as one of the crystallizations of Chinese wisdom, emphasizes the balance of Yin and Yang to keep the body healthy. Under the theoretical guidance of a holistic view, the diagnostic process in TCM has characteristics of subjectivity, fuzziness, and complexity. Therefore, realizing standardization and achieving objective quantitative analysis are the bottlenecks of the development of TCM. The emergence of artificial intelligence (AI) technology has brought unprecedented challenges and opportunities to traditional medicine, which is expected to provide objective measurements and improve the clinical efficacy. However, the combination of TCM and AI is still in its infancy and currently faces many challenges. Therefore, this review provides a comprehensive discussion of the existing advances, problems, and prospects of the applications of AI technologies in TCM with the hope of promoting a better understanding of the TCM modernization and intellectualization. %@ 1793-6853 %L 10.1142/S0192415X23500490 %W HZBOOK

%0 Journal Article %T Artificial Intelligence-Based Traditional Chinese Medicine Assistive Diagnostic System: Validation Study. %A Hong · Zhang/Zhang H %A Wandong · Ni/Ni W %A Jing · Li/Li J %A Jiajun · Zhang/Zhang J %+ [Computer Center, Guanganmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China., Certification Center of Traditional Chinese Medicine, Physician Qualification, State Administration of Traditional Chinese Medicine, Beijing, China., Computer Center, Guanganmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China., Department of Software Engineering, NCT Lab Corp, Billerica, MA, United States.] %J JMIR medical informatics. %D 2020 8 %N 6 %P e17608 %K [AI,BiLSTM-CRF,CNN,ML,NLP,TCM,artificial intelligence,assistive diagnostic system,convolutional neural network,disease diagnosis,machine learning,natural language processing,syndrome differentiation,syndrome prediction,traditional Chinese medicine] %X BACKGROUND: Artificial intelligence-based assistive diagnostic systems imitate the deductive reasoning process of a human physician in biomedical disease diagnosis and treatment decision making. While impressive progress in this area has been reported, most of the reported successes are applications of artificial intelligence in Western medicine. The application of artificial intelligence in traditional Chinese medicine has lagged mainly because traditional Chinese medicine practitioners need to perform syndrome differentiation as well as biomedical disease diagnosis before a treatment decision can be made. Syndrome, a concept unique to traditional Chinese medicine, is an abstraction of a variety of signs and symptoms. The fact that the relationship between diseases and syndromes is not one-to-one but rather many-to-many makes it very challenging for a machine to perform syndrome predictions. So far, only a handful of artificial intelligence-based assistive traditional Chinese medicine diagnostic models have been reported, and they are limited in application to a single disease-type. OBJECTIVE: The objective was to develop an artificial intelligence-based assistive diagnostic system capable of diagnosing multiple types of diseases that are common in traditional Chinese medicine, given a patient's electronic health record notes. The system was designed to simultaneously diagnose the disease and produce a list of corresponding syndromes. METHODS: Unstructured freestyle electronic health record notes were processed by natural language processing techniques to extract clinical information such as signs and symptoms which were represented by named entities. Natural language processing used a recurrent neural network model called bidirectional long short-term memory network-conditional random forest. A convolutional neural network was then used to predict the disease-type out of 187 diseases in traditional Chinese medicine. A novel traditional Chinese medicine syndrome prediction method-an integrated learning model-was used to produce a corresponding list of probable syndromes. By following a majority-rule voting method, the integrated learning model for syndrome prediction can take advantage of four existing prediction methods (back propagation, random forest, extreme gradient boosting, and support vector classifier) while avoiding their respective weaknesses which resulted in a consistently high prediction accuracy. RESULTS: A data set consisting of 22,984 electronic health records from Guanganmen Hospital of the China Academy of Chinese Medical Sciences that were collected between January 1, 2017 and September 7, 2018 was used. The data set contained a total of 187 diseases that are commonly diagnosed in traditional Chinese medicine. The diagnostic system was designed to be able to detect any one of the 187 disease-types. The data set was partitioned into a training set, a validation set, and a testing set in a ratio of 8:1:1. Test results suggested that the proposed system had a good diagnostic accuracy and a strong capability for generalization. The disease-type prediction accuracies of the top one, top three, and top five were 80.5%, 91.6%, and 94.2%, respectively. CONCLUSIONS: The main contributions of the artificial intelligence-based traditional Chinese medicine assistive diagnostic system proposed in this paper are that 187 commonly known traditional Chinese medicine diseases can be diagnosed and a novel prediction method called an integrated learning model is demonstrated. This new prediction method outperformed all four existing methods in our preliminary experimental results. With further improvement of the algorithms and the availability of additional electronic health record data, it is expected that a wider range of traditional Chinese medicine disease-types could be diagnosed and that better diagnostic accuracies could be achieved. %@ 2291-9694 %L 10.2196/17608 %W HZBOOK

%0 Journal Article %T Deep learning and machine intelligence: New computational modeling techniques for discovery of the combination rules and pharmacodynamic characteristics of Traditional Chinese Medicine. %A Dongna · Li/Li D %A Jing · Hu/Hu J %A Lin · Zhang/Zhang L %A Lili · Li/Li L %A Qingsheng · Yin/Yin Q %A Jiangwei · Shi/Shi J %A Hong · Guo/Guo H %A Yanjun · Zhang/Zhang Y %A Pengwei · Zhuang/Zhuang P %+ [State Key Laboratory of Component-based Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China., State Key Laboratory of Component-based Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China., State Key Laboratory of Component-based Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China., State Key Laboratory of Component-based Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China., State Key Laboratory of Component-based Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China., First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, China; National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, China., State Key Laboratory of Component-based Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China., State Key Laboratory of Component-based Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China; First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, China; National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, China. Electronic address: zyjsunye@163.com., State Key Laboratory of Component-based Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China. Electronic address: zhuangpengwei@163.com.] %J European journal of pharmacology. %D 2022 933 %P 175260 %K [AI technology, Drug discovery, Traditional Chinese medicine, Virtual screening] %X It has been increasingly accepted that Multi-Ingredient-Based interventions provide advantages over single-target therapy for complex diseases. With the growing development of Traditional Chinese Medicine (TCM) and continually being refined of a holistic view, "multi-target" and "multi-pathway" integration characteristics of which are being accepted. However, its effector substances, efficacy targets, especially the combination rules and mechanisms remain unclear, and more powerful strategies to interpret the synergy are urgently needed. Artificial intelligence (AI) and computer vision lead to a rapidly expanding in many fields, including diagnosis and treatment of TCM. AI technology significantly improves the reliability and accuracy of diagnostics, target screening, and new drug research. While all AI techniques are capable of matching models to biological big data, the specific methods are complex and varied. Retrieves literature by the keywords such as "artificial intelligence", "machine learning", "deep learning", "traditional Chinese medicine" and "Chinese medicine". Search the application of computer algorithms of TCM between 2000 and 2021 in PubMed, Web of Science, China National Knowledge Infrastructure (CNKI), Elsevier and Springer. This review concentrates on the application of computational in herb quality evaluation, drug target discovery, optimized compatibility and medical diagnoses of TCM. We describe the characteristics of biological data for which different AI techniques are applicable, and discuss some of the best data mining methods and the problems faced by deep learning and machine learning methods applied to Chinese medicine. %@ 1879-0712 %L 10.1016/j.ejphar.2022.175260 %W HZBOOK

%0 Journal Article %T Uncovering the scientific landscape: A bibliometric and Visualized Analysis of artificial intelligence in Traditional Chinese Medicine. %A Siyang · Cao/Cao S %A Yihao · Wei/Wei Y %A Yaohang · Yue/Yue Y %A Deli · Wang/Wang D %A Ao · Xiong/Xiong A %A Jun · Yang/Yang J %A Hui · Zeng/Zeng H %+ [National & Local Joint Engineering Research Centre of Orthopaedic Biomaterials, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China., Shenzhen Key Laboratory of Orthopaedic Diseases and Biomaterials Research, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China., Department of Bone & Joint Surgery, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China., National & Local Joint Engineering Research Centre of Orthopaedic Biomaterials, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China., Shenzhen Key Laboratory of Orthopaedic Diseases and Biomaterials Research, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China., Department of Bone & Joint Surgery, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China., National & Local Joint Engineering Research Centre of Orthopaedic Biomaterials, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China., Shenzhen Key Laboratory of Orthopaedic Diseases and Biomaterials Research, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China., Department of Bone & Joint Surgery, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China., National & Local Joint Engineering Research Centre of Orthopaedic Biomaterials, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China., Shenzhen Key Laboratory of Orthopaedic Diseases and Biomaterials Research, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China., Department of Bone & Joint Surgery, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China., National & Local Joint Engineering Research Centre of Orthopaedic Biomaterials, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China., Shenzhen Key Laboratory of Orthopaedic Diseases and Biomaterials Research, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China., Department of Bone & Joint Surgery, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China., Department of Radiology, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China., National & Local Joint Engineering Research Centre of Orthopaedic Biomaterials, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China., Shenzhen Key Laboratory of Orthopaedic Diseases and Biomaterials Research, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China., Department of Bone & Joint Surgery, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China.] %J Heliyon. %D 2024 10 %N 18 %P e37439 %K [Artificial intelligence, Bibliometrics, Data visualization, Global scientific frontiers, Traditional Chinese medicine] %X The emergence of artificial intelligence (AI) technology has presented new challenges and opportunities for Traditional Chinese Medicine (TCM), aiming to provide objective assessments and improve clinical effectiveness. However, there is a lack of comprehensive analyses on the research trajectory, key directions, current trends, and future perspectives in this field. This research aims to comprehensively update the progress of AI in TCM over the past 24 years, based on data from the Web of Science database covering January 1, 2000, to March 1, 2024. Using advanced analytical tools, we conducted detailed bibliometric and visual analyses. The results highlight China's predominant influence, contributing 54.35 % of the total publications and playing a key role in shaping research in this field. Significant productivity was observed at institutions such as the China Academy of Chinese Medical Sciences, Beijing University of Chinese Medicine, and Shanghai University of Traditional Chinese Medicine, with Wang Yu being the most prolific contributor. The journal Molecules contributed the most publications in this field. This study identified hepatocellular carcinoma, chemical and drug-induced liver injury, Papillon-Lefèvre disease, Parkinson's disease, and anorexia as the most significant disorders researched. This comprehensive bibliometric assessment benefits both seasoned researchers and newcomers, offering quick access to essential information and fostering the generation of innovative ideas in this field. %@ 2405-8440 %L 10.1016/j.heliyon.2024.e37439 %W HZBOOK

%0 Journal Article %T Comprehensive applications of the artificial intelligence technology in new drug research and development. %A Hongyu · Chen/Chen H %A Dong · Lu/Lu D %A Ziyi · Xiao/Xiao Z %A Shensuo · Li/Li S %A Wen · Zhang/Zhang W %A Xin · Luan/Luan X %A Weidong · Zhang/Zhang W %A Guangyong · Zheng/Zheng G %+ [Shanghai Frontiers Science Center for Chinese Medicine Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China. ROR: https://ror.org/00z27jk27. GRID: grid.412540.6. ISNI: 0000 0001 2372 7462, Shanghai Frontiers Science Center for Chinese Medicine Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China. ROR: https://ror.org/00z27jk27. GRID: grid.412540.6. ISNI: 0000 0001 2372 7462, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD USA. GRID: grid.21107.35. ISNI: 0000 0001 2171 9311, Shanghai Frontiers Science Center for Chinese Medicine Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China. ROR: https://ror.org/00z27jk27. GRID: grid.412540.6. ISNI: 0000 0001 2372 7462, Shanghai Frontiers Science Center for Chinese Medicine Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China. ROR: https://ror.org/00z27jk27. GRID: grid.412540.6. ISNI: 0000 0001 2372 7462, Shanghai Frontiers Science Center for Chinese Medicine Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China. ROR: https://ror.org/00z27jk27. GRID: grid.412540.6. ISNI: 0000 0001 2372 7462, Shanghai Frontiers Science Center for Chinese Medicine Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China. ROR: https://ror.org/00z27jk27. GRID: grid.412540.6. ISNI: 0000 0001 2372 7462, Shanghai Frontiers Science Center for Chinese Medicine Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China. ROR: https://ror.org/00z27jk27. GRID: grid.412540.6. ISNI: 0000 0001 2372 7462] %J Health information science and systems. %D 2024 12 %N 1 %P 41 %K [Artificial intelligence, Drug research and development, Drug target identification, Knowledge graph, Machine learning] %X PURPOSE: Target-based strategy is a prevalent means of drug research and development (R&D), since targets provide effector molecules of drug action and offer the foundation of pharmacological investigation. Recently, the artificial intelligence (AI) technology has been utilized in various stages of drug R&D, where AI-assisted experimental methods show higher efficiency than sole experimental ones. It is a critical need to give a comprehensive review of AI applications in drug R &D for biopharmaceutical field. METHODS: Relevant literatures about AI-assisted drug R&D were collected from the public databases (Including Google Scholar, Web of Science, PubMed, IEEE Xplore Digital Library, Springer, and ScienceDirect) through a keyword searching strategy with the following terms [("Artificial Intelligence" OR "Knowledge Graph" OR "Machine Learning") AND ("Drug Target Identification" OR "New Drug Development")]. RESULTS: In this review, we first introduced common strategies and novel trends of drug R&D, followed by characteristic description of AI algorithms widely used in drug R&D. Subsequently, we depicted detailed applications of AI algorithms in target identification, lead compound identification and optimization, drug repurposing, and drug analytical platform construction. Finally, we discussed the challenges and prospects of AI-assisted methods for drug discovery. CONCLUSION: Collectively, this review provides comprehensive overview of AI applications in drug R&D and presents future perspectives for biopharmaceutical field, which may promote the development of drug industry. %@ 2047-2501 %L 10.1007/s13755-024-00300-y %W HZBOOK

%0 Journal Article %T Network pharmacology for traditional Chinese medicine in era of artificial intelligence. %A Weibo · Zhao/Zhao W %A Boyang · Wang/Wang B %A Shao · Li/Li S %+ [Institute for TCM-X, Department of Automation, Tsinghua University, 100084 Beijing, China., Institute for TCM-X, Department of Automation, Tsinghua University, 100084 Beijing, China., Institute for TCM-X, Department of Automation, Tsinghua University, 100084 Beijing, China.] %J Chinese herbal medicines. %D 2024 16 %N 4 %P 558-560 %K [artificial intelligence, network pharmacology, traditional Chinese medicine] %X 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. %@ 2589-3610 %L 10.1016/j.chmed.2024.08.004 %W HZBOOK

%0 Journal Article %T The Research and Development Thinking on the Status of Artificial Intelligence in Traditional Chinese Medicine. %A Nan · Li/Li N %A Jiarui · Yu/Yu J %A Xiaobo · Mao/Mao X %A Yuping · Zhao/Zhao Y %A Luqi · Huang/Huang L %+ [School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China., School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China., School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China., China Academy of Chinese Medical Sciences, Beijng 100020, China., China Academy of Chinese Medical Sciences, Beijng 100020, China.] %J Evidence-based complementary and alternative medicine : eCAM. %D 2022 2022 %P 7644524 %X With the rapid development and application of artificial intelligence (AI) in medical field, the diagnostic ways of human health and the social medical structures have changed. Based on the concept of holism and the theory of syndrome differentiation and treatment, TCM realizes comprehensive informatization and intelligence with the help of AI technology in data mining, intelligent diagnosis and treatment, intelligent learning, and decision-making. Furthermore, the intelligent research of TCM technology will further promote the improvement in TCM diagnosis and treatment rules and the leaping development of TCM intelligent instruments. In this article, we performed a systematic review of scientific literature about TCM and AI. Moreover, the practical problems of TCM intellectualization, the current situation and demand of TCM, and the influence of AI in the TCM field are discussed by searching for literature using TCM scientific databases, reference lists, expert consultation, and targeted websites. Finally, we look forward to the application prospects of AI and propose a possible future direction of intelligent TCM in the current health-care system in China. %@ 1741-427X %L 10.1155/2022/7644524 %W HZBOOK

%0 Journal Article %T Recent progress in artificial intelligence and machine learning for novel diabetes mellitus medications development. %A Qi · Guo/Guo Q %A Bo · Fu/Fu B %A Yuan · Tian/Tian Y %A Shujun · Xu/Xu S %A Xin · Meng/Meng X %+ [School of Pharmacy, Heilongjiang University of Chinese Medicine, Harbin, P. R. China., School of Pharmacy, Heilongjiang University of Chinese Medicine, Harbin, P. R. China., School of Pharmacy, Heilongjiang University of Chinese Medicine, Harbin, P. R. China., School of Pharmacy, Heilongjiang University of Chinese Medicine, Harbin, P. R. China., School of Pharmacy, Heilongjiang University of Chinese Medicine, Harbin, P. R. China.] %J Current medical research and opinion. %D 2024 40 %N 9 %P 1483-1493 %K [Artificial Intelligence, Machine Learning, clinical, diabetes mellitus, drug discovery, novel drug development] %X Diabetes mellitus, stemming from either insulin resistance or inadequate insulin secretion, represents a complex ailment that results in prolonged hyperglycemia and severe complications. Patients endure severe ramifications such as kidney disease, vision impairment, cardiovascular disorders, and susceptibility to infections, leading to significant physical suffering and imposing substantial socio-economic burdens. This condition has evolved into an increasingly severe health crisis. There is an urgent need to develop new treatments with improved efficacy and fewer adverse effects to meet clinical demands. However, novel drug development is costly, time-consuming, and often associated with side effects and suboptimal efficacy, making it a major challenge. Artificial Intelligence (AI) and Machine Learning (ML) have revolutionized drug development across its comprehensive lifecycle, spanning drug discovery, preclinical studies, clinical trials, and post-market surveillance. These technologies have significantly accelerated the identification of promising therapeutic candidates, optimized trial designs, and enhanced post-approval safety monitoring. Recent advances in AI, including data augmentation, interpretable AI, and integration of AI with traditional experimental methods, offer promising strategies for overcoming the challenges inherent in AI-based drug discovery. Despite these advancements, there exists a notable gap in comprehensive reviews detailing AI and ML applications throughout the entirety of developing medications for diabetes mellitus. This review aims to fill this gap by evaluating the impact and potential of AI and ML technologies at various stages of diabetes mellitus drug development. It does that by synthesizing current research findings and technological advances so as to effectively control diabetes mellitus and mitigate its far-reaching social and economic impacts. The integration of AI and ML promises to revolutionize diabetes mellitus treatment strategies, offering hope for improved patient outcomes and reduced healthcare burdens worldwide. %@ 1473-4877 %L 10.1080/03007995.2024.2387187 %W HZBOOK

%0 Journal Article %T Examining Real-World Medication Consultations and Drug-Herb Interactions: ChatGPT Performance Evaluation. %A Hsing-Yu · Hsu/Hsu HY %A Kai-Cheng · Hsu/Hsu KC %A Shih-Yen · Hou/Hou SY %A Ching-Lung · Wu/Wu CL %A Yow-Wen · Hsieh/Hsieh YW %A Yih-Dih · Cheng/Cheng YD %+ [Department of Pharmacy, China Medical University Hospital, Taichung, Taiwan., Graduate Institute of Clinical Pharmacy, College of Medicine, National Taiwan University, Taipei, Taiwan., Artificial Intelligence Center, China Medical University Hospital, Taichung, Taiwan., Department of Medicine, China Medical University, Taichung, Taiwan., Artificial Intelligence Center, China Medical University Hospital, Taichung, Taiwan., School of Pharmacy, College of Pharmacy, China Medical University, Taichung, Taiwan., Department of Pharmacy, China Medical University Hospital, Taichung, Taiwan., School of Pharmacy, College of Pharmacy, China Medical University, Taichung, Taiwan., Department of Pharmacy, China Medical University Hospital, Taichung, Taiwan., School of Pharmacy, College of Pharmacy, China Medical University, Taichung, Taiwan.] %J JMIR medical education. %D 2023 9 %P e48433 %K [ChatGPT, LLM, NLP, chat generative pre-trained transformer, drug-herb interactions, language models, large language model, natural language processing, pharmacist, real-world medication consultation questions] %X BACKGROUND: Since OpenAI released ChatGPT, with its strong capability in handling natural tasks and its user-friendly interface, it has garnered significant attention. OBJECTIVE: A prospective analysis is required to evaluate the accuracy and appropriateness of medication consultation responses generated by ChatGPT. METHODS: A prospective cross-sectional study was conducted by the pharmacy department of a medical center in Taiwan. The test data set comprised retrospective medication consultation questions collected from February 1, 2023, to February 28, 2023, along with common questions about drug-herb interactions. Two distinct sets of questions were tested: real-world medication consultation questions and common questions about interactions between traditional Chinese and Western medicines. We used the conventional double-review mechanism. The appropriateness of each response from ChatGPT was assessed by 2 experienced pharmacists. In the event of a discrepancy between the assessments, a third pharmacist stepped in to make the final decision. RESULTS: Of 293 real-world medication consultation questions, a random selection of 80 was used to evaluate ChatGPT's performance. ChatGPT exhibited a higher appropriateness rate in responding to public medication consultation questions compared to those asked by health care providers in a hospital setting (31/51, 61% vs 20/51, 39%; P=.01). CONCLUSIONS: The findings from this study suggest that ChatGPT could potentially be used for answering basic medication consultation questions. Our analysis of the erroneous information allowed us to identify potential medical risks associated with certain questions; this problem deserves our close attention. %@ 2369-3762 %L 10.2196/48433 %W HZBOOK

%0 Journal Article %T Artificial intelligence for drug discovery: Resources, methods, and applications. %A Wei · Chen/Chen W %A Xuesong · Liu/Liu X %A Sanyin · Zhang/Zhang S %A Shilin · Chen/Chen S %+ [State Key Laboratory of Southwestern Chinese Medicine Resources, Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China., Institute of Herbgenomics, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China., College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China., State Key Laboratory of Southwestern Chinese Medicine Resources, Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China., Institute of Herbgenomics, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China., State Key Laboratory of Southwestern Chinese Medicine Resources, Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China., Institute of Herbgenomics, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.] %J Molecular therapy. Nucleic acids. %D 2023 31 %P 691-702 %K [MT: Bioinformatics, artificial intelligence, bioinformatics, data resources, drug discovery and development, molecular descriptors] %X Conventional wet laboratory testing, validations, and synthetic procedures are costly and time-consuming for drug discovery. Advancements in artificial intelligence (AI) techniques have revolutionized their applications to drug discovery. Combined with accessible data resources, AI techniques are changing the landscape of drug discovery. In the past decades, a series of AI-based models have been developed for various steps of drug discovery. These models have been used as complements of conventional experiments and have accelerated the drug discovery process. In this review, we first introduced the widely used data resources in drug discovery, such as ChEMBL and DrugBank, followed by the molecular representation schemes that convert data into computer-readable formats. Meanwhile, we summarized the algorithms used to develop AI-based models for drug discovery. Subsequently, we discussed the applications of AI techniques in pharmaceutical analysis including predicting drug toxicity, drug bioactivity, and drug physicochemical property. Furthermore, we introduced the AI-based models for de novo drug design, drug-target structure prediction, drug-target interaction, and binding affinity prediction. Moreover, we also highlighted the advanced applications of AI in drug synergism/antagonism prediction and nanomedicine design. Finally, we discussed the challenges and future perspectives on the applications of AI to drug discovery. %@ 2162-2531 %L 10.1016/j.omtn.2023.02.019 %W HZBOOK

%0 Journal Article %T Chinese Medicine in the Era of Artificial Intelligence: Challenges and Development Prospects. %A Chaoyu · Wang/Wang C %A Guowei · Dai/Dai G %A Yue · Luo/Luo Y %A Chuanbiao · Wen/Wen C %A Qingfeng · Tang/Tang Q %+ [Chengdu University of Traditional Chinese Medicine, Chengdu 611137, P. R. China., Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu 610072, P. R. China., College of Computer Science, Sichuan University, Chengdu 610065, P. R. China., National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, Chengdu 610065, P. R. China., School of Intelligent Medicine, Chengdu University of Traditional Chinese Medicine, Sichuan, Chengdu 611137, P. R. China., School of Intelligent Medicine, Chengdu University of Traditional Chinese Medicine, Sichuan, Chengdu 611137, P. R. China., Digital and Intelligent Health Research Center, Anqing Normal University, Anqing 246133, P. R. China.] %J The American journal of Chinese medicine. %D 2025%P 1-32 %K [Artificial Intelligence (AI), Healthcare, Herbal Medicine, Research Advances, Traditional Chinese Medicine (TCM)] %X Traditional Chinese medicine (TCM) has protected the health of Chinese people for thousands of years. With the rapid development of artificial intelligence (AI), various fields of TCM are facing both opportunities and challenges. This review discusses the development prospects and challenges of Chinese medicine in the AI era, emphasizing that AI, as an important tool in the process of Chinese medicine healthcare services, can assist doctors in making objective, rational and professional treatment decisions, and that AI has a strong potential for development in the field of Chinese medicine. However, the emotions, complex thoughts, and humanistic values of doctors are qualities that AI is currently unable to realize, so as the dominant player, the doctor is indispensable to the medical process. By summarizing and analyzing the current development status of AI in diagnosis, drug research, health management and education in TCM, this paper reveals the development prospects and potential risks of combining TCM with AI, and suggests that AI is an important aid for modernizing and improving the quality of TCM medical care in a coordinated manner. %@ 1793-6853 %L 10.1142/S0192415X25500144 %W HZBOOK

%0 Journal Article %T GENERATING RESEARCH HYPOTHESES TO OVERCOME KEY CHALLENGES IN THE EARLY DIAGNOSIS OF COLORECTAL CANCER - FUTURE APPLICATION OF AI. %A Lan · Yao/Yao L %A Heliang · Yin/Yin H %A Chengyuan · Yang/Yang C %A Shuyan · Han/Han S %A Jiamin · Ma/Ma J %A J Carolyn · Graff/Graff JC %A Cong-Yi · Wang/Wang CY %A Yan · Jiao/Jiao Y %A Jiafu · Ji/Ji J %A Weikuan · Gu/Gu W %A Gang · Wang/Wang G %+ [College of Health Management, Harbin Medical University, 157 Baojian Road, Harbin, Heilongjiang, 150081, China; Department of Orthopedic Surgery and BME-Campbell Clinic, University of Tennessee Health Science Centre, Memphis, Tennessee, 38163, USA. Electronic address: lyao5@uthsc.edu., Department of Orthopedic Surgery and BME-Campbell Clinic, University of Tennessee Health Science Centre, Memphis, Tennessee, 38163, USA; Centre of Integrative Research, The First Hospital of Qiqihar City, Qiqihar, Heilongjiang 161005, PR China. Electronic address: yinheliang999@163.com., Department of Orthopedic Surgery and BME-Campbell Clinic, University of Tennessee Health Science Centre, Memphis, Tennessee, 38163, USA. Electronic address: cyang31@uthsc.edu., Department of Integration of Chinese and Western Medicine, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing 100142, China. Electronic address: shuyanhan@bjmu.edu.cn., Department of Breast Surgery, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, 450000, PR China. Electronic address: mjxm1992@gmail.com., College of Nursing, University of Tennessee Health Science Center, Memphis, TN 38163 USA. Electronic address: jgraff@uthsc.edu., The Center for Biomedical Research, Dept of Respiratory and Critical Care Medicine, National Health Commission (NHC) Key Laboratory of Respiratory Diseases, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. Electronic address: wangcy@tjh.tjmu.edu.cn., Department of Orthopedic Surgery and BME-Campbell Clinic, University of Tennessee Health Science Centre, Memphis, Tennessee, 38163, USA., State Key Laboratory of Molecular Oncology, Beijing Key Laboratory of Carcinogenesis and Translational Research, Gastrointestinal Center of Peking University Cancer Hospital, Beijing 100142, China. Electronic address: jijiafu@hsc.pku.edu.cn., Department of Orthopedic Surgery and BME-Campbell Clinic, University of Tennessee Health Science Centre, Memphis, Tennessee, 38163, USA; Lt. Col. Luke Weathers, Jr. VA Medical Center, 116 N Pauline St. Memphis, TN 38105, USA; Department of Pharmaceutical Sciences, University of Tennessee Health Science Center, Memphis, TN, 38163, USA. Electronic address: wgu@uthsc.edu., Department of Pancreatic and Biliary Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, China; Key Laboratory of Hepatosplenic Surgery, Ministry of Education, The First Affiliated Hospital of Harbin Medical University, Harbin, China. Electronic address: wgilu79@163.com.] %J Cancer letters. %D 2025%P 217632 %K [AI, Artificial Intelligence, ChatGPT, Colorectal Cancer, Early Diagnosis] %X We intend to explore the capability of ChatGPT 4.0 in generating innovative research hypotheses to address key challenges in the early diagnosis of colorectal cancer (CRC). We asked ChatGPT to generate hypotheses focusing on three main challenges: improving screening accuracy, overcoming technological limitations, and identifying reliable biomarkers. The hypotheses were evaluated for novelty. The experimental plans provided by ChatGPT for selected hypotheses were assessed for completion and feasibility. As a result, ChatGPT generated a total of 65 hypotheses. ChatGPT rated all 65 hypotheses, with 25 hypotheses receiving the highest rating (5) and 40 hypotheses receiving a rating of 4 or lower. The research team evaluated a total of 65 hypotheses, assigning them the following grades: hypotheses were rated as excellent (Grade 5), 16 were deemed suitable (Grade 4), 31 were classified as satisfactory (Grade 3), 12 were identified as needing Improvement (Grade 2), and one was considered poor (Grade 1). Additionally, the study determined that 17 of the generated hypotheses had corresponding publications. Out of the three experimental plans assessed, one was rated excellent (5) for feasibility, while the others received good (4) and moderate (3) ratings. Predicted outcomes and alternative approaches were rated as good, with some areas requiring further improvement. Our data demonstrate that AI has the potential to revolutionize hypothesis generation in medical research, though further validation through experimental and clinical studies is needed. This study suggests that while AI can generate novel hypotheses, human expertise is essential for evaluating their practicality and relevance in scientific research. %@ 1872-7980 %L 10.1016/j.canlet.2025.217632 %W HZBOOK

%0 Journal Article %T A focus on harnessing big data and artificial intelligence: revolutionizing drug discovery from traditional Chinese medicine sources. %A Mingyu · Li/Li M %A Jian · Zhang/Zhang J %+ [State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine Shanghai China jian.zhang@sjtu.edu.cn., State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine Shanghai China jian.zhang@sjtu.edu.cn., Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine Shanghai 200025 China.] %J Chemical science. %D 2023 14 %N 39 %P 10628-10630 %X The advent of big data-driven artificial intelligence (AI) modeling has profoundly impacted the realm of drug discovery. Chen et al. (Q. Lv et al., Chem. Sci., 2023, https://doi.org/10.1039/D3SC02139D) have paved a way for modern drug discovery from traditional Chinese medicine (TCM) sources through their efforts over the past decade. They achieved this by creating TCMBank, the most extensive systematic central resource for TCM, which integrates standardized TCM-related big data and streamlines the AI-based drug discovery process. %@ 2041-6520 %L 10.1039/d3sc90185h %W HZBOOK

%0 Journal Article %T Opportunities and challenges of traditional Chinese medicine doctors in the era of artificial intelligence. %A Wenyu · Li/Li W %A Xiaolei · Ge/Ge X %A Shuai · Liu/Liu S %A Lili · Xu/Xu L %A Xu · Zhai/Zhai X %A Linyong · Yu/Yu L %+ [School of Marxism, Capital Normal University, Beijing, China., Wangjing Hospital of China Academy of Traditional Chinese Medicine, Beijing, China., Graduate School of Chinese Academy of Traditional Chinese Medicine, Beijing, China., Graduate School of Chinese Academy of Traditional Chinese Medicine, Beijing, China., Wangjing Hospital of China Academy of Traditional Chinese Medicine, Beijing, China., China Academy of Chinese Medical Sciences, Beijing, China.] %J Frontiers in medicine. %D 2023 10 %P 1336175 %K [Chinese medicine doctor, artificial intelligence, challenges, opportunities, traditional Chinese medicine] %X With the exponential advancement of artificial intelligence (AI) technology, the realm of medicine is experiencing a paradigm shift, engendering a multitude of prospects and trials for healthcare practitioners, encompassing those devoted to the practice of traditional Chinese medicine (TCM). This study explores the evolving landscape for TCM practitioners in the AI era, emphasizing that while AI can be helpful, it cannot replace the role of TCM practitioners. It is paramount to underscore the intrinsic worth of human expertise, accentuating that artificial intelligence (AI) is merely an instrument. On the one hand, AI-enabled tools like intelligent symptom checkers, diagnostic assistance systems, and personalized treatment plans can augment TCM practitioners' expertise and capacity, improving diagnosis accuracy and treatment efficacy. AI-empowered collaborations between Western medicine and TCM can strengthen holistic care. On the other hand, AI may disrupt conventional TCM workflow and doctor-patient relationships. Maintaining the humanistic spirit of TCM while embracing AI requires upholding professional ethics and establishing appropriate regulations. To leverage AI while retaining the essence of TCM, practitioners need to hone holistic analytical skills and see AI as complementary. By highlighting promising applications and potential risks of AI in TCM, this study provides strategic insights for stakeholders to promote the integrated development of AI and TCM for better patient outcomes. With proper implementation, AI can become a valuable assistant for TCM practitioners to elevate healthcare quality. %@ 2296-858X %L 10.3389/fmed.2023.1336175 %W HZBOOK

%0 Journal Article %T Network pharmacology: towards the artificial intelligence-based precision traditional Chinese medicine. %A Peng · Zhang/Zhang P %A Dingfan · Zhang/Zhang D %A Wuai · Zhou/Zhou W %A Lan · Wang/Wang L %A Boyang · Wang/Wang B %A Tingyu · Zhang/Zhang T %A Shao · Li/Li S %+ [Institute for TCM-X, MOE Key Laboratory of Bioinformatics/Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China., Institute for TCM-X, MOE Key Laboratory of Bioinformatics/Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China., China Mobile Information System Integration Co., Ltd, Beijing 100032, China., Institute for TCM-X, MOE Key Laboratory of Bioinformatics/Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China., Institute for TCM-X, MOE Key Laboratory of Bioinformatics/Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China., Institute for TCM-X, MOE Key Laboratory of Bioinformatics/Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China., Institute for TCM-X, MOE Key Laboratory of Bioinformatics/Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China.] %J Briefings in bioinformatics %D 2023 25 %N 1 %K [artificial intelligence, deep learning, network pharmacology, network target, traditional Chinese medicine] %X Network pharmacology (NP) provides a new methodological perspective for understanding traditional medicine from a holistic perspective, giving rise to frontiers such as traditional Chinese medicine network pharmacology (TCM-NP). With the development of artificial intelligence (AI) technology, it is key for NP to develop network-based AI methods to reveal the treatment mechanism of complex diseases from massive omics data. In this review, focusing on the TCM-NP, we summarize involved AI methods into three categories: network relationship mining, network target positioning and network target navigating, and present the typical application of TCM-NP in uncovering biological basis and clinical value of Cold/Hot syndromes. Collectively, our review provides researchers with an innovative overview of the methodological progress of NP and its application in TCM from the AI perspective. %@ 1477-4054 %L 10.1093/bib/bbad518 %W HZBOOK