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Song Z, Chen G, Chen CYC. AI empowering traditional Chinese medicine? Chem Sci 2024; 15:d4sc04107k. [PMID: 39355231 PMCID: PMC11440359 DOI: 10.1039/d4sc04107k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2024] [Accepted: 09/22/2024] [Indexed: 10/03/2024] Open
Abstract
For centuries, Traditional Chinese Medicine (TCM) has been a prominent treatment method in China, incorporating acupuncture, herbal remedies, massage, and dietary therapy to promote holistic health and healing. TCM has played a major role in drug discovery, with over 60% of small-molecule drugs approved by the FDA from 1981 to 2019 being derived from natural products. However, TCM modernization faces challenges such as data standardization and the complexity of TCM formulations. The establishment of comprehensive TCM databases has significantly improved the efficiency and accuracy of TCM research, enabling easier access to information on TCM ingredients and encouraging interdisciplinary collaborations. These databases have revolutionized TCM research, facilitating advancements in TCM modernization and patient care. In addition, advancements in AI algorithms and database data quality have accelerated progress in AI for TCM. The application of AI in TCM encompasses a wide range of areas, including herbal screening and new drug discovery, diagnostic and treatment principles, pharmacological mechanisms, network pharmacology, and the incorporation of innovative AI technologies. AI also has the potential to enable personalized medicine by identifying patterns and correlations in patient data, leading to more accurate diagnoses and tailored treatments. The potential benefits of AI for TCM are vast and diverse, promising continued progress and innovation in the field.
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Affiliation(s)
- Zhilin Song
- State Key Laboratory of Chemical Oncogenomics, School of Chemical Biology and Biotechnology, Peking University Shenzhen Graduate School Shenzhen Guangdong 518055 China
- AI for Science (AI4S)-Preferred Program, School of Electronic and Computer Engineering, Peking University Shenzhen Graduate School Shenzhen Guangdong 518055 China
| | - Guanxing Chen
- Artificial Intelligence Medical Research Center, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University Shenzhen Guangdong 518107 China
| | - Calvin Yu-Chian Chen
- State Key Laboratory of Chemical Oncogenomics, School of Chemical Biology and Biotechnology, Peking University Shenzhen Graduate School Shenzhen Guangdong 518055 China
- AI for Science (AI4S)-Preferred Program, School of Electronic and Computer Engineering, Peking University Shenzhen Graduate School Shenzhen Guangdong 518055 China
- Department of Medical Research, China Medical University Hospital Taichung 40447 Taiwan
- Department of Bioinformatics and Medical Engineering, Asia University Taichung 41354 Taiwan
- Guangdong L-Med Biotechnology Co., Ltd Meizhou Guangdong 514699 China
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Bae H, Park SY, Kim CE. A practical guide to implementing artificial intelligence in traditional East Asian medicine research. Integr Med Res 2024; 13:101067. [PMID: 39253696 PMCID: PMC11381867 DOI: 10.1016/j.imr.2024.101067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2024] [Revised: 06/24/2024] [Accepted: 07/02/2024] [Indexed: 09/11/2024] Open
Abstract
In this paper, we present a comprehensive guide for implementing artificial intelligence (AI) techniques in traditional East Asian medicine (TEAM) research. We cover essential aspects of the AI model development pipeline, including research objective establishment, data collection and preprocessing, model selection, evaluation, and interpretation. The unique considerations in applying AI to TEAM datasets, such as data scarcity, imbalance, and model interpretability, are discussed. We provide practical tips and recommendations based on best practices and our own experience. The potential of large language models in TEAM research is also highlighted. Finally, we discuss the challenges and future directions of AI application in TEAM, emphasizing the need for standardized data collection and sharing platforms.
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Affiliation(s)
- Hyojin Bae
- Department of Physiology, Seoul National University College of Medicine, Seoul, Korea
| | - Sa-Yoon Park
- Department of Physiology, College of Korean Medicine, Wonkwang University, Iksan, Korea
- Department of Physiology, College of Korean Medicine, Gachon University, Seongnam, Korea
| | - Chang-Eop Kim
- Department of Physiology, College of Korean Medicine, Gachon University, Seongnam, Korea
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Zhou E, Shen Q, Hou Y. Integrating artificial intelligence into the modernization of traditional Chinese medicine industry: a review. Front Pharmacol 2024; 15:1181183. [PMID: 38464717 PMCID: PMC10921893 DOI: 10.3389/fphar.2024.1181183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 02/08/2024] [Indexed: 03/12/2024] Open
Abstract
Traditional Chinese medicine (TCM) is the practical experience and summary of the Chinese nation for thousands of years. It shows great potential in treating various chronic diseases, complex diseases and major infectious diseases, and has gradually attracted the attention of people all over the world. However, due to the complexity of prescription and action mechanism of TCM, the development of TCM industry is still in a relatively conservative stage. With the rise of artificial intelligence technology in various fields, many scholars began to apply artificial intelligence technology to traditional Chinese medicine industry and made remarkable progress. This paper comprehensively summarizes the important role of artificial intelligence in the development of traditional Chinese medicine industry from various aspects, including new drug discovery, data mining, quality standardization and industry technology of traditional Chinese medicine. The limitations of artificial intelligence in these applications are also emphasized, including the lack of pharmacological research, database quality problems and the challenges brought by human-computer interaction. Nevertheless, the development of artificial intelligence has brought new opportunities and innovations to the modernization of traditional Chinese medicine. Integrating artificial intelligence technology into the comprehensive application of Chinese medicine industry is expected to overcome the major problems faced by traditional Chinese medicine industry and further promote the modernization of the whole traditional Chinese medicine industry.
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Affiliation(s)
- E. Zhou
- Yuhu District Healthcare Security Administration, Xiangtan, China
| | - Qin Shen
- Department of Respiratory Medicine, Hunan Provincial People’s Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha, China
| | - Yang Hou
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, China
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Zhang S, Zhang X, Du J, Wang W, Pi X. Multi-target meridians classification based on the topological structure of anti-cancer phytochemicals using deep learning. JOURNAL OF ETHNOPHARMACOLOGY 2024; 319:117244. [PMID: 37777031 DOI: 10.1016/j.jep.2023.117244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 09/24/2023] [Accepted: 09/27/2023] [Indexed: 10/02/2023]
Abstract
ETHNOPHARMACOLOGICAL RELEVANCE Traditional Chinese medicine (TCM) meridian is the key theoretical guidance of prescription against tumor in clinical practice. However, there is no scientific and systematic verification of therapeutic action of herbs under meridians context. Several studies have determined the Chinese herbal medicine (CHM) phytochemicals for intrinsic attribute or meridians classification based on artificial intelligence (AI) tools. However, it is challenging to represent the complex molecular structures with large heterogeneity through the current technologies. In addition, the multiple correspondence between herbs and meridians has not been paid much attention. AIM OF THE STUDY We aim to develop an AI framework to classify multi-target meridians through the topological structure of phytochemicals. MATERIALS AND METHODS A total of 354 anti-cancer herbs, their corresponding TCM meridians and 5471 ingredient compounds were collected from public databases of CancerHSP, ETCM, and Hit 2.0. The statistical analysis of herbal and compound datasets, clustering analysis of the associated cancers, and correlational analysis of meridian tropism were preliminary conducted. Then a deep learning (DL) hybrid model named GRMC consisting of graph convolutional network (GCN) and recurrent neural network (RNN) was employed to generate the meridian multi-label sequences based on molecular graph. RESULTS The curing herbs against tumors have tight relationships to lung, liver, stomach, and spleen meridians. These herbs behave different properties in curing certain cancer. Certain cancer types have co-occurrence such as ovarian, bladder and cervical cancer. Compounds have multitarget meridians with characteristics of higher-order correlations. Compared with the other state-of-the-art algorithms on the datasets and previous methods dealing with conventional fixed fingerprints of herbal compounds, the proposed GRMC has superior overall performance on testing dataset with the one error of 0.183, hamming loss of 0.112, mean averaged accuracy (MAA) of 0.855, mean averaged precision (MAP) of 0.891, mean averaged recall (MAR) of 0.812, and mean averaged F1 score (MAF) of 0.849. CONCLUSIONS The proposed method can predict multi-targeted meridians through neural graph features in herbal compounds and outperforms several comparison methods. It could provide a basis for understanding the molecular scientific evidence of TCM meridians.
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Affiliation(s)
- Sheng Zhang
- Key Laboratory of Biorheological Science and Technology of Ministry of Education, College of Bioengineering, Chongqing University, No.174 Shazheng Road, Shapingba District, Chongqing, 400044, PR China.
| | - Xianwei Zhang
- Key Laboratory of Biorheological Science and Technology of Ministry of Education, College of Bioengineering, Chongqing University, No.174 Shazheng Road, Shapingba District, Chongqing, 400044, PR China.
| | - Jiayin Du
- School of Pharmacy, Chongqing University, Chongqing, 400044, PR China.
| | - Wei Wang
- Department of Cardiology, Chongqing University Cancer Hospital, No. 181 Hanyu Road, Shapingba District, Chongqing, 400030, PR China.
| | - Xitian Pi
- Key Laboratory of Biorheological Science and Technology of Ministry of Education, College of Bioengineering, Chongqing University, No.174 Shazheng Road, Shapingba District, Chongqing, 400044, PR China.
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Wang H, Li J, Tu W, Wang Z, Zhang Y, Chang L, Wu Y, Zhang X. Identification of Blood Biomarkers Related to Energy Metabolism and Construction of Diagnostic Prediction Model Based on Three Independent Alzheimer's Disease Cohorts. J Alzheimers Dis 2024; 100:1261-1287. [PMID: 39093073 PMCID: PMC11380308 DOI: 10.3233/jad-240301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
Background Blood biomarkers are crucial for the diagnosis and therapy of Alzheimer's disease (AD). Energy metabolism disturbances are closely related to AD. However, research on blood biomarkers related to energy metabolism is still insufficient. Objective This study aims to explore the diagnostic and therapeutic significance of energy metabolism-related genes in AD. Methods AD cohorts were obtained from GEO database and single center. Machine learning algorithms were used to identify key genes. GSEA was used for functional analysis. Six algorithms were utilized to establish and evaluate diagnostic models. Key gene-related drugs were screened through network pharmacology. Results We identified 4 energy metabolism genes, NDUFA1, MECOM, RPL26, and RPS27. These genes have been confirmed to be closely related to multiple energy metabolic pathways and different types of T cell immune infiltration. Additionally, the transcription factors INSM2 and 4 lncRNAs were involved in regulating 4 genes. Further analysis showed that all biomarkers were downregulated in the AD cohorts and not affected by aging and gender. More importantly, we constructed a diagnostic prediction model of 4 biomarkers, which has been validated by various algorithms for its diagnostic performance. Furthermore, we found that valproic acid mainly interacted with these biomarkers through hydrogen bonding, salt bonding, and hydrophobic interaction. Conclusions We constructed a predictive model based on 4 energy metabolism genes, which may be helpful for the diagnosis of AD. The 4 validated genes could serve as promising blood biomarkers for AD. Their interaction with valproic acid may play a crucial role in the therapy of AD.
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Affiliation(s)
- Hongqi Wang
- Department of Anatomy, School of Basic Medical Sciences, Capital Medical University, Beijing, China
| | - Jilai Li
- Department of Neurology, Aerospace Center Hospital, Peking University Aerospace School of Clinical Medicine, Beijing, China
| | - Wenjun Tu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zhiqun Wang
- Department of Radiology, Aerospace Center Hospital, Peking University Aerospace School of Clinical Medicine, Beijing, China
| | - Yiming Zhang
- Department of Physiology and Pathophysiology, School of Basic Medical Sciences, Capital Medical University, Beijing, China
| | - Lirong Chang
- Department of Anatomy, School of Basic Medical Sciences, Capital Medical University, Beijing, China
| | - Yan Wu
- Department of Anatomy, School of Basic Medical Sciences, Capital Medical University, Beijing, China
| | - Xia Zhang
- Department of Neurology, Aerospace Center Hospital, Peking University Aerospace School of Clinical Medicine, Beijing, China
- Department of Physiology and Pathophysiology, School of Basic Medical Sciences, Capital Medical University, Beijing, China
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Bouali N, Hamadou WS, Badraoui R, Lajimi RH, Hamdi A, Alreshidi M, Adnan M, Soua Z, Siddiqui AJ, Noumi E, Snoussi M. Phytochemical Composition, Antioxidant, and Anticancer Activities of Sidr Honey: In Vitro and In Silico Computational Investigation. Life (Basel) 2022; 13:life13010035. [PMID: 36675984 PMCID: PMC9867352 DOI: 10.3390/life13010035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 12/12/2022] [Accepted: 12/19/2022] [Indexed: 12/24/2022] Open
Abstract
Cancer is one of the major causes of death worldwide. The repercussions of conventional therapeutic approaches present a challenge in the delivery of new effective treatments. Thus, more attention is being awarded to natural products, mainly honey. Honey could be the basis for the development of new therapies for cancer patients. The aim of this study is to assess the phytochemical profiling, antioxidant, drug-likeness properties, and anticancer activity of Ziziphus honey (ZH) derived from the Hail region of Saudi Arabia. The phytochemical profiling using high resolution-liquid chromatography mass spectrometry (HR-LCMS) revealed 10 compounds belonging to several familial classes and one tripeptide. Potential antioxidant activity was noted as assessed by DPPH (IC50 0.670 mg/mL), ABTS (IC50 3.554 mg/mL), and β-carotene (IC50 > 5 mg/mL). The ZH exerted a notable cytotoxic effect in a dose-dependent manner against three cancer cell lines: lung (A549), breast (MCF-7), and colon (HCT-116), with respective IC50 values of 5.203%, 6.02%, and 7.257%. The drug-likeness investigation unveiled that most of the identified compounds meet Lipinski’s rule. The molecular docking analysis revealed interesting antioxidant and anticancer activities for most targeted proteins and supported the in vitro findings. The Miraxanthin-III compound exhibited the most stabilized interaction. This study provides deeper insights on ZH as prominent source of bioactive compounds with potent antioxidant and anticancer effects.
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Affiliation(s)
- Nouha Bouali
- Department of Biology, College of Science, University of Hail, P.O. Box 2440, Hail 2440, Saudi Arabia
| | - Walid Sabri Hamadou
- Department of Biology, College of Science, University of Hail, P.O. Box 2440, Hail 2440, Saudi Arabia
- Research Unit: Molecular Biology of Leukemia and Lymphoma, Department of Biochemistry, University of Medecine of Sousse, Sousse 4002, Tunisia
| | - Riadh Badraoui
- Department of Biology, College of Science, University of Hail, P.O. Box 2440, Hail 2440, Saudi Arabia
- Section of Histology—Cytology, University of Medicine of Tunis, University of Tunis El Manar, La Rabta 1007, Road Djebal Lakhdhar, Tunis 1007, Tunisia
- Department of Histo-Embryology and Cytogenetics, Medicine Faculty of Sfax, University of Sfax, Road of Majida Boulia, Sfax 3029, Tunisia
| | - Ramzi Hadj Lajimi
- Department of Biology, College of Science, University of Hail, P.O. Box 2440, Hail 2440, Saudi Arabia
- Department of Chemistry, College of Science, University of Hail, P.O. Box 2440, Hail 2440, Saudi Arabia
- Laboratory of Water, Membranes and Environmental Biotechnologies, Center of Research and Water Technologies, P.O. Box 273, Soliman 8020, Tunisia
| | - Assia Hamdi
- Laboratory of Galenic and Pharmacological Chemical Development of Drugs, University of Pharmacy, Monastir 5000, Tunisia
| | - Mousa Alreshidi
- Department of Biology, College of Science, University of Hail, P.O. Box 2440, Hail 2440, Saudi Arabia
| | - Mohd Adnan
- Department of Biology, College of Science, University of Hail, P.O. Box 2440, Hail 2440, Saudi Arabia
| | - Zohra Soua
- Research Unit: Molecular Biology of Leukemia and Lymphoma, Department of Biochemistry, University of Medecine of Sousse, Sousse 4002, Tunisia
| | - Arif Jamal Siddiqui
- Department of Biology, College of Science, University of Hail, P.O. Box 2440, Hail 2440, Saudi Arabia
| | - Emira Noumi
- Department of Biology, College of Science, University of Hail, P.O. Box 2440, Hail 2440, Saudi Arabia
- Laboratory of Genetics, Biodiversity and Valorisation of Bioressources, High Institute of Biotechnology University of Monastir, Monastir 5000, Tunisia
- Correspondence:
| | - Mejdi Snoussi
- Department of Biology, College of Science, University of Hail, P.O. Box 2440, Hail 2440, Saudi Arabia
- Laboratory of Genetics, Biodiversity and Valorisation of Bioressources, High Institute of Biotechnology University of Monastir, Monastir 5000, Tunisia
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Li D, Hu J, Zhang L, Li L, Yin Q, Shi J, Guo H, Zhang Y, Zhuang P. Deep learning and machine intelligence: New computational modeling techniques for discovery of the combination rules and pharmacodynamic characteristics of Traditional Chinese Medicine. Eur J Pharmacol 2022; 933:175260. [PMID: 36116517 DOI: 10.1016/j.ejphar.2022.175260] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Revised: 08/15/2022] [Accepted: 09/05/2022] [Indexed: 11/19/2022]
Abstract
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.
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Affiliation(s)
- Dongna Li
- State Key Laboratory of Component-based Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China
| | - Jing Hu
- State Key Laboratory of Component-based Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China
| | - Lin Zhang
- State Key Laboratory of Component-based Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China
| | - Lili Li
- State Key Laboratory of Component-based Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China
| | - Qingsheng Yin
- State Key Laboratory of Component-based Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China
| | - Jiangwei Shi
- First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, China; National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, China
| | - Hong Guo
- State Key Laboratory of Component-based Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China
| | - Yanjun Zhang
- 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.
| | - Pengwei Zhuang
- State Key Laboratory of Component-based Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China.
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Machine learning & deep learning in data-driven decision making of drug discovery and challenges in high-quality data acquisition in the pharmaceutical industry. Future Med Chem 2021; 14:245-270. [PMID: 34939433 DOI: 10.4155/fmc-2021-0243] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
Predicting novel small molecule bioactivities for the target deconvolution, hit-to-lead optimization in drug discovery research, requires molecular representation. Previous reports have demonstrated that machine learning (ML) and deep learning (DL) have substantial implications in virtual screening, peptide synthesis, drug ADMET screening and biomarker discovery. These strategies can increase the positive outcomes in the drug discovery process without false-positive rates and can be achieved in a cost-effective way with a minimum duration of time by high-quality data acquisition. This review substantially discusses the recent updates in AI tools as cheminformatics application in medicinal chemistry for the data-driven decision making of drug discovery and challenges in high-quality data acquisition in the pharmaceutical industry while improving small-molecule bioactivities and properties.
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Kashyap K, Siddiqi MI. Recent trends in artificial intelligence-driven identification and development of anti-neurodegenerative therapeutic agents. Mol Divers 2021; 25:1517-1539. [PMID: 34282519 DOI: 10.1007/s11030-021-10274-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Accepted: 07/05/2021] [Indexed: 12/12/2022]
Abstract
Neurological disorders affect various aspects of life. Finding drugs for the central nervous system is a very challenging and complex task due to the involvement of the blood-brain barrier, P-glycoprotein, and the drug's high attrition rates. The availability of big data present in online databases and resources has enabled the emergence of artificial intelligence techniques including machine learning to analyze, process the data, and predict the unknown data with high efficiency. The use of these modern techniques has revolutionized the whole drug development paradigm, with an unprecedented acceleration in the central nervous system drug discovery programs. Also, the new deep learning architectures proposed in many recent works have given a better understanding of how artificial intelligence can tackle big complex problems that arose due to central nervous system disorders. Therefore, the present review provides comprehensive and up-to-date information on machine learning/artificial intelligence-triggered effort in the brain care domain. In addition, a brief overview is presented on machine learning algorithms and their uses in structure-based drug design, ligand-based drug design, ADMET prediction, de novo drug design, and drug repurposing. Lastly, we conclude by discussing the major challenges and limitations posed and how they can be tackled in the future by using these modern machine learning/artificial intelligence approaches.
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Affiliation(s)
- Kushagra Kashyap
- Academy of Scientific and Innovative Research (AcSIR), CSIR-Central Drug Research Institute (CSIR-CDRI) Campus, Lucknow, India.,Molecular and Structural Biology Division, CSIR-Central Drug Research Institute (CSIR-CDRI), Sector 10, Jankipuram Extension, Sitapur Road, Lucknow, 226031, India
| | - Mohammad Imran Siddiqi
- Academy of Scientific and Innovative Research (AcSIR), CSIR-Central Drug Research Institute (CSIR-CDRI) Campus, Lucknow, India. .,Molecular and Structural Biology Division, CSIR-Central Drug Research Institute (CSIR-CDRI), Sector 10, Jankipuram Extension, Sitapur Road, Lucknow, 226031, India.
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Shanbhogue H M, Thirumaleshwar S, Kumar Tm P, Kumar S H. Artificial Intelligence in Pharmaceutical Field - A Critical Review. Curr Drug Deliv 2021; 18:1456-1466. [PMID: 34139981 DOI: 10.2174/1567201818666210617100613] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 04/09/2021] [Accepted: 04/17/2021] [Indexed: 12/15/2022]
Abstract
Artificial intelligence is an emerging sector in almost all fields. It is not confined only to a particular category and can be used in various fields like research, technology, and health. AI mainly concentrates on how computers analyze data and mimic the human thought process. As drug development involves high R & D costs and uncertainty in time consumption, artificial intelligence can serve as one of the promising solutions to overcome all these demerits. Due to the availability of enormous data, there are chances of missing out on some crucial details. For solving these issues, algorithms like machine learning, deep learning, and other expert systems are being used. On successful implementation of AI in the pharmaceutical field, the delays in drug development, and failure at the clinical and marketing level can be reduced. This review comprises information regarding the development of AI, its subfields, its overall implementation, and its application in the pharmaceutical sector and provides insights on challenges and limitations concerning AI.
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Affiliation(s)
- Maithri Shanbhogue H
- Department of Pharmaceutics, Industrial Pharmacy Group, JSS College of Pharmacy, Mysuru JSS Academy of Higher Education and Research Sri Shivarathreeshwara Nagara, Mysuru - 570015, Karnataka, India
| | - Shailesh Thirumaleshwar
- Department of Pharmaceutics, Industrial Pharmacy Group, JSS College of Pharmacy, Mysuru JSS Academy of Higher Education and Research Sri Shivarathreeshwara Nagara, Mysuru - 570015, Karnataka, India
| | - Pramod Kumar Tm
- Department of Pharmaceutics, Industrial Pharmacy Group, JSS College of Pharmacy, Mysuru JSS Academy of Higher Education and Research Sri Shivarathreeshwara Nagara, Mysuru - 570015, Karnataka, India
| | - Hemanth Kumar S
- Department of Pharmaceutics, Industrial Pharmacy Group, JSS College of Pharmacy, Mysuru JSS Academy of Higher Education and Research Sri Shivarathreeshwara Nagara, Mysuru - 570015, Karnataka, India
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Manco L, Maffei N, Strolin S, Vichi S, Bottazzi L, Strigari L. Basic of machine learning and deep learning in imaging for medical physicists. Phys Med 2021; 83:194-205. [DOI: 10.1016/j.ejmp.2021.03.026] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 03/07/2021] [Accepted: 03/16/2021] [Indexed: 02/08/2023] Open
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