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Abbas MKG, Rassam A, Karamshahi F, Abunora R, Abouseada M. The Role of AI in Drug Discovery. Chembiochem 2024; 25:e202300816. [PMID: 38735845 DOI: 10.1002/cbic.202300816] [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] [Received: 12/03/2023] [Revised: 05/09/2024] [Accepted: 05/10/2024] [Indexed: 05/14/2024]
Abstract
The emergence of Artificial Intelligence (AI) in drug discovery marks a pivotal shift in pharmaceutical research, blending sophisticated computational techniques with conventional scientific exploration to break through enduring obstacles. This review paper elucidates the multifaceted applications of AI across various stages of drug development, highlighting significant advancements and methodologies. It delves into AI's instrumental role in drug design, polypharmacology, chemical synthesis, drug repurposing, and the prediction of drug properties such as toxicity, bioactivity, and physicochemical characteristics. Despite AI's promising advancements, the paper also addresses the challenges and limitations encountered in the field, including data quality, generalizability, computational demands, and ethical considerations. By offering a comprehensive overview of AI's role in drug discovery, this paper underscores the technology's potential to significantly enhance drug development, while also acknowledging the hurdles that must be overcome to fully realize its benefits.
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Affiliation(s)
- M K G Abbas
- Center for Advanced Materials, Qatar University, P.O. Box, 2713, Doha, Qatar
| | - Abrar Rassam
- Secondary Education, Educational Sciences, Qatar University, P.O. Box, 2713, Doha, Qatar
| | - Fatima Karamshahi
- Department of Chemistry and Earth Sciences, Qatar University, P.O. Box, 2713, Doha, Qatar
| | - Rehab Abunora
- Faculty of Medicine, General Medicine and Surgery, Helwan University, Cairo, Egypt
| | - Maha Abouseada
- Department of Chemistry and Earth Sciences, Qatar University, P.O. Box, 2713, Doha, Qatar
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2
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Zhang T, An W, You S, Chen S, Zhang S. G protein-coupled receptors and traditional Chinese medicine: new thinks for the development of traditional Chinese medicine. Chin Med 2024; 19:92. [PMID: 38956679 PMCID: PMC11218379 DOI: 10.1186/s13020-024-00964-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Accepted: 06/19/2024] [Indexed: 07/04/2024] Open
Abstract
G protein-coupled receptors (GPCRs) widely exist in vivo and participate in many physiological processes, thus emerging as important targets for drug development. Approximately 30% of the Food and Drug Administration (FDA)-approved drugs target GPCRs. To date, the 'one disease, one target, one molecule' strategy no longer meets the demands of drug development. Meanwhile, small-molecule drugs account for 60% of FDA-approved drugs. Traditional Chinese medicine (TCM) has garnered widespread attention for its unique theoretical system and treatment methods. TCM involves multiple components, targets and pathways. Centered on GPCRs and TCM, this paper discusses the similarities and differences between TCM and GPCRs from the perspectives of syndrome of TCM, the consistency of TCM's multi-component and multi-target approaches and the potential of GPCRs and TCM in the development of novel drugs. A novel strategy, 'simultaneous screening of drugs and targets', was proposed and applied to the study of GPCRs. We combine GPCRs with TCM to facilitate the modernisation of TCM, provide valuable insights into the rational application of TCM and facilitate the research and development of novel drugs. This study offers theoretical support for the modernisation of TCM and introduces novel ideas for development of safe and effective drugs.
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Affiliation(s)
- Ting Zhang
- Institute of Herbgenomics, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China
- Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, 611100, China
| | - Wenqiao An
- Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, 611100, China
| | - Shengjie You
- Institute of Herbgenomics, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China
| | - Shilin Chen
- Institute of Herbgenomics, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China.
| | - Sanyin Zhang
- Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, 611100, China.
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Kairys V, Baranauskiene L, Kazlauskiene M, Zubrienė A, Petrauskas V, Matulis D, Kazlauskas E. Recent advances in computational and experimental protein-ligand affinity determination techniques. Expert Opin Drug Discov 2024; 19:649-670. [PMID: 38715415 DOI: 10.1080/17460441.2024.2349169] [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] [Received: 03/18/2024] [Accepted: 04/25/2024] [Indexed: 05/22/2024]
Abstract
INTRODUCTION Modern drug discovery revolves around designing ligands that target the chosen biomolecule, typically proteins. For this, the evaluation of affinities of putative ligands is crucial. This has given rise to a multitude of dedicated computational and experimental methods that are constantly being developed and improved. AREAS COVERED In this review, the authors reassess both the industry mainstays and the newest trends among the methods for protein - small-molecule affinity determination. They discuss both computational affinity predictions and experimental techniques, describing their basic principles, main limitations, and advantages. Together, this serves as initial guide to the currently most popular and cutting-edge ligand-binding assays employed in rational drug design. EXPERT OPINION The affinity determination methods continue to develop toward miniaturization, high-throughput, and in-cell application. Moreover, the availability of data analysis tools has been constantly increasing. Nevertheless, cross-verification of data using at least two different techniques and careful result interpretation remain of utmost importance.
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Affiliation(s)
- Visvaldas Kairys
- Department of Bioinformatics, Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Lina Baranauskiene
- Department of Biothermodynamics and Drug Design, Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | | | - Asta Zubrienė
- Department of Biothermodynamics and Drug Design, Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Vytautas Petrauskas
- Department of Biothermodynamics and Drug Design, Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Daumantas Matulis
- Department of Biothermodynamics and Drug Design, Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Egidijus Kazlauskas
- Department of Biothermodynamics and Drug Design, Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
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Gadiya Y, Shetty S, Hofmann-Apitius M, Gribbon P, Zaliani A. Exploring SureChEMBL from a drug discovery perspective. Sci Data 2024; 11:507. [PMID: 38755219 PMCID: PMC11099139 DOI: 10.1038/s41597-024-03371-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 05/13/2024] [Indexed: 05/18/2024] Open
Abstract
In the pharmaceutical industry, the patent protection of drugs and medicines is accorded importance because of the high costs involved in the development of novel drugs. Over the years, researchers have analyzed patent documents to identify freedom-to-operate spaces for novel drug candidates. To assist this, several well-established public patent document data repositories have enabled automated methodologies for extracting information on therapeutic agents. In this study, we delve into one such publicly available patent database, SureChEMBL, which catalogues patent documents related to life sciences. Our exploration begins by identifying patent compounds across public chemical data resources, followed by pinpointing sections in patent documents where the chemical annotations were found. Next, we exhibit the potential of compounds to serve as drug candidates by evaluating their conformity to drug-likeness criteria. Lastly, we examine the drug development stage reported for these compounds to understand their clinical success. In summary, our investigation aims at providing a comprehensive overview of the patent compounds catalogued in SureChEMBL, assessing their relevance to pharmaceutical drug discovery.
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Affiliation(s)
- Yojana Gadiya
- Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP), Schnackenburgallee 114, 22525, Hamburg, Germany.
- Fraunhofer Cluster of Excellence for Immune-Mediated Diseases (CIMD), Theodor Stern Kai 7, 60590, Frankfurt, Germany.
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53113, Bonn, Germany.
| | - Simran Shetty
- Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP), Schnackenburgallee 114, 22525, Hamburg, Germany
- Fraunhofer Cluster of Excellence for Immune-Mediated Diseases (CIMD), Theodor Stern Kai 7, 60590, Frankfurt, Germany
- Hamburg University of Applied Sciences (HAW), 20099, Hamburg, Germany
| | - Martin Hofmann-Apitius
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53113, Bonn, Germany
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53757, Sankt Augustin, Germany
| | - Philip Gribbon
- Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP), Schnackenburgallee 114, 22525, Hamburg, Germany
- Fraunhofer Cluster of Excellence for Immune-Mediated Diseases (CIMD), Theodor Stern Kai 7, 60590, Frankfurt, Germany
| | - Andrea Zaliani
- Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP), Schnackenburgallee 114, 22525, Hamburg, Germany
- Fraunhofer Cluster of Excellence for Immune-Mediated Diseases (CIMD), Theodor Stern Kai 7, 60590, Frankfurt, Germany
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Kalemati M, Zamani Emani M, Koohi S. DCGAN-DTA: Predicting drug-target binding affinity with deep convolutional generative adversarial networks. BMC Genomics 2024; 25:411. [PMID: 38724911 PMCID: PMC11080241 DOI: 10.1186/s12864-024-10326-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Accepted: 04/19/2024] [Indexed: 05/13/2024] Open
Abstract
BACKGROUND In recent years, there has been a growing interest in utilizing computational approaches to predict drug-target binding affinity, aiming to expedite the early drug discovery process. To address the limitations of experimental methods, such as cost and time, several machine learning-based techniques have been developed. However, these methods encounter certain challenges, including the limited availability of training data, reliance on human intervention for feature selection and engineering, and a lack of validation approaches for robust evaluation in real-life applications. RESULTS To mitigate these limitations, in this study, we propose a method for drug-target binding affinity prediction based on deep convolutional generative adversarial networks. Additionally, we conducted a series of validation experiments and implemented adversarial control experiments using straw models. These experiments serve to demonstrate the robustness and efficacy of our predictive models. We conducted a comprehensive evaluation of our method by comparing it to baselines and state-of-the-art methods. Two recently updated datasets, namely the BindingDB and PDBBind, were used for this purpose. Our findings indicate that our method outperforms the alternative methods in terms of three performance measures when using warm-start data splitting settings. Moreover, when considering physiochemical-based cold-start data splitting settings, our method demonstrates superior predictive performance, particularly in terms of the concordance index. CONCLUSION The results of our study affirm the practical value of our method and its superiority over alternative approaches in predicting drug-target binding affinity across multiple validation sets. This highlights the potential of our approach in accelerating drug repurposing efforts, facilitating novel drug discovery, and ultimately enhancing disease treatment. The data and source code for this study were deposited in the GitHub repository, https://github.com/mojtabaze7/DCGAN-DTA . Furthermore, the web server for our method is accessible at https://dcgan.shinyapps.io/bindingaffinity/ .
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Affiliation(s)
- Mahmood Kalemati
- Department of Computer Engineering, Sharif University of Technology, Tehran, Iran
| | - Mojtaba Zamani Emani
- Department of Computer Engineering, Sharif University of Technology, Tehran, Iran
| | - Somayyeh Koohi
- Department of Computer Engineering, Sharif University of Technology, Tehran, Iran.
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Chen L, Zhao X, Liu X, Ouyang Y, Xu C, Shi Y. Development of small molecule drugs targeting immune checkpoints. Cancer Biol Med 2024; 21:j.issn.2095-3941.2024.0034. [PMID: 38727005 PMCID: PMC11131045 DOI: 10.20892/j.issn.2095-3941.2024.0034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Accepted: 03/28/2024] [Indexed: 05/29/2024] Open
Abstract
Immune checkpoint inhibitors (ICIs) are used to relieve and refuel anti-tumor immunity by blocking the interaction, transcription, and translation of co-inhibitory immune checkpoints or degrading co-inhibitory immune checkpoints. Thousands of small molecule drugs or biological materials, especially antibody-based ICIs, are actively being studied and antibodies are currently widely used. Limitations, such as anti-tumor efficacy, poor membrane permeability, and unneglected tolerance issues of antibody-based ICIs, remain evident but are thought to be overcome by small molecule drugs. Recent structural studies have broadened the scope of candidate immune checkpoint molecules, as well as innovative chemical inhibitors. By way of comparison, small molecule drug-based ICIs represent superior oral bioavailability and favorable pharmacokinetic features. Several ongoing clinical trials are exploring the synergetic effect of ICIs and other therapeutic strategies based on multiple ICI functions, including immune regulation, anti-angiogenesis, and cell cycle regulation. In this review we summarized the current progression of small molecule ICIs and the mechanism underlying immune checkpoint proteins, which will lay the foundation for further exploration.
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Affiliation(s)
- Luoyi Chen
- Department of Oncology, Sichuan Academy of Medical Sciences, Sichuan Provincial People’s Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Xinchen Zhao
- Department of Oncology, Sichuan Academy of Medical Sciences, Sichuan Provincial People’s Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Xiaowei Liu
- Institute for Breast Health Medicine, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Yujie Ouyang
- Acupuncture and Massage College, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Chuan Xu
- Department of Oncology, Sichuan Academy of Medical Sciences, Sichuan Provincial People’s Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Ying Shi
- Department of Oncology, Sichuan Academy of Medical Sciences, Sichuan Provincial People’s Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu 610054, China
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Rubinic I, Kurtov M, Rubinic I, Likic R, Dargan PI, Wood DM. Artificial intelligence in clinical pharmacology: A case study and scoping review of large language models and bioweapon potential. Br J Clin Pharmacol 2024; 90:620-628. [PMID: 37658550 DOI: 10.1111/bcp.15899] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Revised: 08/23/2023] [Accepted: 08/24/2023] [Indexed: 09/03/2023] Open
Abstract
This paper aims to explore the possibility of employing large language models (LLMs) - a type of artificial intelligence (AI) - in clinical pharmacology, with a focus on its possible misuse in bioweapon development. Additionally, ethical considerations, legislation and potential risk reduction measures are analysed. The existing literature is reviewed to investigate the potential misuse of AI and LLMs in bioweapon creation. The search includes articles from PubMed, Scopus and Web of Science Core Collection that were identified using a specific protocol. To explore the regulatory landscape, the OECD.ai platform was used. The review highlights the dual-use vulnerability of AI and LLMs, with a focus on bioweapon development. Subsequently, a case study is used to illustrate the potential of AI manipulation resulting in harmful substance synthesis. Existing regulations inadequately address the ethical concerns tied to AI and LLMs. Mitigation measures are proposed, including technical solutions (explainable AI), establishing ethical guidelines through collaborative efforts, and implementing policy changes to create a comprehensive regulatory framework. The integration of AI and LLMs into clinical pharmacology presents invaluable opportunities, while also introducing significant ethical and safety considerations. Addressing the dual-use nature of AI requires robust regulations, as well as adopting a strategic approach grounded in technical solutions and ethical values following the principles of transparency, accountability and safety. Additionally, AI's potential role in developing countermeasures against novel hazardous substances is underscored. By adopting a proactive approach, the potential benefits of AI and LLMs can be fully harnessed while minimizing the associated risks.
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Affiliation(s)
- Igor Rubinic
- University of Rijeka School of Medicine, Rijeka, Croatia
- Clinical Hospital Centre Rijeka, Rijeka, Croatia
| | | | - Ivan Rubinic
- School of Engineering, University of Rijeka, Rijeka, Croatia
| | - Robert Likic
- University of Zagreb School of Medicine, Zagreb, Croatia
- Clinical Hospital Centre Zagreb, Zagreb, Croatia
| | - Paul I Dargan
- Faculty of Life Sciences and Medicine, King's College London, London, UK
- Clinical Toxicology, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - David M Wood
- Faculty of Life Sciences and Medicine, King's College London, London, UK
- Clinical Toxicology, Guy's and St Thomas' NHS Foundation Trust, London, UK
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Gangwal A, Ansari A, Ahmad I, Azad AK, Kumarasamy V, Subramaniyan V, Wong LS. Generative artificial intelligence in drug discovery: basic framework, recent advances, challenges, and opportunities. Front Pharmacol 2024; 15:1331062. [PMID: 38384298 PMCID: PMC10879372 DOI: 10.3389/fphar.2024.1331062] [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: 11/07/2023] [Accepted: 01/17/2024] [Indexed: 02/23/2024] Open
Abstract
There are two main ways to discover or design small drug molecules. The first involves fine-tuning existing molecules or commercially successful drugs through quantitative structure-activity relationships and virtual screening. The second approach involves generating new molecules through de novo drug design or inverse quantitative structure-activity relationship. Both methods aim to get a drug molecule with the best pharmacokinetic and pharmacodynamic profiles. However, bringing a new drug to market is an expensive and time-consuming endeavor, with the average cost being estimated at around $2.5 billion. One of the biggest challenges is screening the vast number of potential drug candidates to find one that is both safe and effective. The development of artificial intelligence in recent years has been phenomenal, ushering in a revolution in many fields. The field of pharmaceutical sciences has also significantly benefited from multiple applications of artificial intelligence, especially drug discovery projects. Artificial intelligence models are finding use in molecular property prediction, molecule generation, virtual screening, synthesis planning, repurposing, among others. Lately, generative artificial intelligence has gained popularity across domains for its ability to generate entirely new data, such as images, sentences, audios, videos, novel chemical molecules, etc. Generative artificial intelligence has also delivered promising results in drug discovery and development. This review article delves into the fundamentals and framework of various generative artificial intelligence models in the context of drug discovery via de novo drug design approach. Various basic and advanced models have been discussed, along with their recent applications. The review also explores recent examples and advances in the generative artificial intelligence approach, as well as the challenges and ongoing efforts to fully harness the potential of generative artificial intelligence in generating novel drug molecules in a faster and more affordable manner. Some clinical-level assets generated form generative artificial intelligence have also been discussed in this review to show the ever-increasing application of artificial intelligence in drug discovery through commercial partnerships.
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Affiliation(s)
- Amit Gangwal
- Department of Natural Product Chemistry, Shri Vile Parle Kelavani Mandal’s Institute of Pharmacy, Dhule, Maharashtra, India
| | - Azim Ansari
- Computer Aided Drug Design Center Shri Vile Parle Kelavani Mandal’s Institute of Pharmacy, Dhule, Maharashtra, India
| | - Iqrar Ahmad
- Department of Pharmaceutical Chemistry, Prof. Ravindra Nikam College of Pharmacy, Dhule, India
| | - Abul Kalam Azad
- Faculty of Pharmacy, University College of MAIWP International, Batu Caves, Malaysia
| | - Vinoth Kumarasamy
- Department of Parasitology and Medical Entomology, Faculty of Medicine, Universiti Kebangsaan Malaysia, Cheras, Malaysia
| | - Vetriselvan Subramaniyan
- Pharmacology Unit, Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Selangor, Malaysia
- School of Bioengineering and Biosciences, Lovely Professional University, Phagwara, Punjab, India
| | - Ling Shing Wong
- Faculty of Health and Life Sciences, INTI International University, Nilai, Malaysia
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He D, Wang R, Xu Z, Wang J, Song P, Wang H, Su J. The use of artificial intelligence in the treatment of rare diseases: A scoping review. Intractable Rare Dis Res 2024; 13:12-22. [PMID: 38404730 PMCID: PMC10883845 DOI: 10.5582/irdr.2023.01111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 11/28/2023] [Accepted: 12/22/2023] [Indexed: 02/27/2024] Open
Abstract
With the increasing application of artificial intelligence (AI) in medicine and healthcare, AI technologies have the potential to improve the diagnosis, treatment, and prognosis of rare diseases. Presently, existing research predominantly focuses on the areas of diagnosis and prognosis, with relatively fewer studies dedicated to the domain of treatment. The purpose of this review is to systematically analyze the existing literature on the application of AI in the treatment of rare diseases. We searched three databases for related studies, and established criteria for the selection of retrieved articles. From the 407 unique articles identified across the three databases, 13 articles from 8 countries were selected, which investigated 10 different rare diseases. The most frequently studied rare disease group was rare neurologic diseases (n = 5/13, 38.46%). Among the four identified therapeutic domains, 7 articles (53.85%) focused on drug research, with 5 specifically focused on drug discovery (drug repurposing, the discovery of drug targets and small-molecule inhibitors), 1 on pre-clinical studies (drug interactions), and 1 on clinical studies (information strength assessment of clinical parameters). Across the selected 13 articles, we identified total 32 different algorithms, with random forest (RF) being the most commonly used (n = 4/32, 12.50%). The predominant purpose of AI in the treatment of rare diseases in these articles was to enhance the performance of analytical tasks (53.33%). The most common data source was database data (35.29%), with 5 of these studies being in the field of drug research, utilizing classic databases such as RCSB, PDB and NCBI. Additionally, 47.37% of the articles highlighted the existing challenge of data scarcity or small sample sizes.
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Affiliation(s)
- Da He
- Shanghai Health Development Research Center (Shanghai Medical Information Center), Shanghai, China
| | - Ru Wang
- Shanghai Health Development Research Center (Shanghai Medical Information Center), Shanghai, China
| | - Zhilin Xu
- EYE & ENT Hospital of Fudan University, Shanghai, China
| | - Jiangna Wang
- Jiangxi University of Chinese Medicine, Shanghai, China
| | - Peipei Song
- Center for Clinical Sciences, National Center for Global Health and Medicine, Tokyo, Japan
| | - Haiyin Wang
- Shanghai Health Development Research Center (Shanghai Medical Information Center), Shanghai, China
| | - Jinying Su
- Shanghai University of Traditional Chinese Medicine, Shanghai, China
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Haghir Ebrahim Abadi MH, Ghasemlou A, Bayani F, Sefidbakht Y, Vosough M, Mozaffari-Jovin S, Uversky VN. AI-driven covalent drug design strategies targeting main protease (m pro) against SARS-CoV-2: structural insights and molecular mechanisms. J Biomol Struct Dyn 2024:1-29. [PMID: 38287509 DOI: 10.1080/07391102.2024.2308769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 01/17/2024] [Indexed: 01/31/2024]
Abstract
The emergence of new SARS-CoV-2 variants has raised concerns about the effectiveness of COVID-19 vaccines. To address this challenge, small-molecule antivirals have been proposed as a crucial therapeutic option. Among potential targets for anti-COVID-19 therapy, the main protease (Mpro) of SARS-CoV-2 is important due to its essential role in the virus's life cycle and high conservation. The substrate-binding region of the core proteases of various coronaviruses, including SARS-CoV-2, SARS-CoV, and Middle East respiratory syndrome coronavirus (MERS-CoV), could be used for the generation of new protease inhibitors. Various drug discovery methods have employed a diverse range of strategies, targeting both monomeric and dimeric forms, including drug repurposing, integrating virtual screening with high-throughput screening (HTS), and structure-based drug design, each demonstrating varying levels of efficiency. Covalent inhibitors, such as Nirmatrelvir and MG-101, showcase robust and high-affinity binding to Mpro, exhibiting stable interactions confirmed by molecular docking studies. Development of effective antiviral drugs is imperative to address potential pandemic situations. This review explores recent advances in the search for Mpro inhibitors and the application of artificial intelligence (AI) in drug design. AI leverages vast datasets and advanced algorithms to streamline the design and identification of promising Mpro inhibitors. AI-driven drug discovery methods, including molecular docking, predictive modeling, and structure-based drug repurposing, are at the forefront of identifying potential candidates for effective antiviral therapy. In a time when COVID-19 potentially threat global health, the quest for potent antiviral solutions targeting Mpro could be critical for inhibiting the virus.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
| | | | - Fatemeh Bayani
- Protein Research Center, Shahid Beheshti University, Tehran, Iran
| | - Yahya Sefidbakht
- Protein Research Center, Shahid Beheshti University, Tehran, Iran
| | - Massoud Vosough
- Department of Regenerative Medicine, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran
| | - Sina Mozaffari-Jovin
- Department of Medical Genetics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
- Medical Genetics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Vladimir N Uversky
- Department of Molecular Medicine, University of South Florida, Tampa, FL, USA
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11
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Liu J, Yang M, Yu Y, Xu H, Li K, Zhou X. Large language models in bioinformatics: applications and perspectives. ARXIV 2024:arXiv:2401.04155v1. [PMID: 38259343 PMCID: PMC10802675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Large language models (LLMs) are a class of artificial intelligence models based on deep learning, which have great performance in various tasks, especially in natural language processing (NLP). Large language models typically consist of artificial neural networks with numerous parameters, trained on large amounts of unlabeled input using self-supervised or semi-supervised learning. However, their potential for solving bioinformatics problems may even exceed their proficiency in modeling human language. In this review, we will present a summary of the prominent large language models used in natural language processing, such as BERT and GPT, and focus on exploring the applications of large language models at different omics levels in bioinformatics, mainly including applications of large language models in genomics, transcriptomics, proteomics, drug discovery and single cell analysis. Finally, this review summarizes the potential and prospects of large language models in solving bioinformatic problems.
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Affiliation(s)
- Jiajia Liu
- Center for Computational Systems Medicine, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, 77030, USA
| | - Mengyuan Yang
- School of Life Sciences, Zhengzhou University, Zhengzhou, Henan 450001, China
| | - Yankai Yu
- School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, Sichuan 611756, China
| | - Haixia Xu
- The Center of Gerontology and Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Kang Li
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Xiaobo Zhou
- Center for Computational Systems Medicine, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, 77030, USA
- McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- School of Dentistry, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
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12
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Das K, Paltani M, Tripathi PK, Kumar R, Verma S, Kumar S, Jain CK. Current implications and challenges of artificial intelligence technologies in therapeutic intervention of colorectal cancer. EXPLORATION OF TARGETED ANTI-TUMOR THERAPY 2023; 4:1286-1300. [PMID: 38213536 PMCID: PMC10776591 DOI: 10.37349/etat.2023.00197] [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: 06/12/2023] [Accepted: 08/28/2023] [Indexed: 01/13/2024] Open
Abstract
Irrespective of men and women, colorectal cancer (CRC), is the third most common cancer in the population with more than 1.85 million cases annually. Fewer than 20% of patients only survive beyond five years from diagnosis. CRC is a highly preventable disease if diagnosed at the early stage of malignancy. Several screening methods like endoscopy (like colonoscopy; gold standard), imaging examination [computed tomographic colonography (CTC)], guaiac-based fecal occult blood (gFOBT), immunochemical test from faeces, and stool DNA test are available with different levels of sensitivity and specificity. The available screening methods are associated with certain drawbacks like invasiveness, cost, or sensitivity. In recent years, computer-aided systems-based screening, diagnosis, and treatment have been very promising in the early-stage detection and diagnosis of CRC cases. Artificial intelligence (AI) is an enormously in-demand, cost-effective technology, that uses various tools machine learning (ML), and deep learning (DL) to screen, diagnose, and stage, and has great potential to treat CRC. Moreover, different ML algorithms and neural networks [artificial neural network (ANN), k-nearest neighbors (KNN), and support vector machines (SVMs)] have been deployed to predict precise and personalized treatment options. This review examines and summarizes different ML and DL models used for therapeutic intervention in CRC cancer along with the gap and challenges for AI.
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Affiliation(s)
- Kriti Das
- Department of Artificial Intelligence and Precision Medicine, School of Allied Health Sciences and Management, Delhi Pharmaceutical Sciences and Research University, New Delhi 110017, India
| | - Maanvi Paltani
- Department of Artificial Intelligence and Precision Medicine, School of Allied Health Sciences and Management, Delhi Pharmaceutical Sciences and Research University, New Delhi 110017, India
| | - Pankaj Kumar Tripathi
- Department of Biotechnology, Jaypee Institute of Information Technology, Noida 201309, Uttar Pradesh, India
| | - Rajnish Kumar
- Department of Medical Laboratory Technology, School of Allied Health Sciences, Delhi Pharmaceutical Sciences and Research University, Delhi 110017, India
| | - Saniya Verma
- Department of Medical Laboratory Technology, School of Allied Health Sciences, Delhi Pharmaceutical Sciences and Research University, Delhi 110017, India
| | - Subodh Kumar
- Department of Medical Laboratory Technology, School of Allied Health Sciences, Delhi Pharmaceutical Sciences and Research University, Delhi 110017, India
| | - Chakresh Kumar Jain
- Department of Biotechnology, Jaypee Institute of Information Technology, Noida 201309, Uttar Pradesh, India
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13
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Ali S, Shaikh S, Ahmad K, Choi I. Identification of active compounds as novel dipeptidyl peptidase-4 inhibitors through machine learning and structure-based molecular docking simulations. J Biomol Struct Dyn 2023:1-10. [PMID: 38100571 DOI: 10.1080/07391102.2023.2292299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 11/23/2023] [Indexed: 12/17/2023]
Abstract
The enzyme dipeptidyl peptidase 4 (DPP4) is a potential therapeutic target for type 2 diabetes (T2DM). Many synthetic anti-DPP4 medications are available to treat T2DM. The need for secure and efficient medicines has been unmet due to the adverse side effects of existing DPP4 medications. The present study implemented a combined approach to machine learning and structure-based virtual screening to identify DPP4 inhibitors. Two ML models were trained based on DPP4 IC50 datasets. The ML models random forest (RF) and multilayer perceptron (MLP) neural network showed good accuracy, with the area under the curve being 0.93 and 0.91, respectively. The natural compound library was screened through ML models, and 1% (217) of compounds were selected for further screening. Structure-based virtual screening was performed along with positive control sitagliptin to obtain more specific and selective leads for DPP4. Based on binding affinity, drug-likeness properties, and interaction with DPP4, Z-614 and Z-997 compounds showed high binding affinity and specificity in the catalytic pocket of DPP4. Finally, the stability conformation of the DPP4 enzyme complex was checked by a molecular dynamics (MD) simulation. The MD simulation showed that both compounds bind better in the catalytic pocket, but the Z-614 compound altered the DPP4 native conformation. Therefore, Z-614 showed a high deviation in the backbone. This combined approach (ML and structure-based) study reported that Z-997 binds most stably to DPP4 in their catalytic pocket with a binding free energy of -70.3 kJ/mol, suggesting its therapeutic potential as a treatment option for T2DM disease.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Shahid Ali
- Department of Medical Biotechnology, Yeungnam University, Gyeongsan, South Korea
- Research Institute of Cell Culture, Yeungnam University, Gyeongsan, South Korea
| | - Sibhghatulla Shaikh
- Department of Medical Biotechnology, Yeungnam University, Gyeongsan, South Korea
- Research Institute of Cell Culture, Yeungnam University, Gyeongsan, South Korea
| | - Khurshid Ahmad
- Department of Medical Biotechnology, Yeungnam University, Gyeongsan, South Korea
- Research Institute of Cell Culture, Yeungnam University, Gyeongsan, South Korea
| | - Inho Choi
- Department of Medical Biotechnology, Yeungnam University, Gyeongsan, South Korea
- Research Institute of Cell Culture, Yeungnam University, Gyeongsan, South Korea
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14
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Alkubaisi BO, Aljobowry R, Ali SM, Sultan S, Zaraei SO, Ravi A, Al-Tel TH, El-Gamal MI. The latest perspectives of small molecules FMS kinase inhibitors. Eur J Med Chem 2023; 261:115796. [PMID: 37708796 DOI: 10.1016/j.ejmech.2023.115796] [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] [Received: 07/03/2023] [Revised: 09/03/2023] [Accepted: 09/04/2023] [Indexed: 09/16/2023]
Abstract
FMS kinase is a type III tyrosine kinase receptor that plays a central role in the pathophysiology and management of several diseases, including a range of cancer types, inflammatory disorders, neurodegenerative disorders, and bone disorders among others. In this review, the pathophysiological pathways of FMS kinase in different diseases and the recent developments of its monoclonal antibodies and inhibitors during the last five years are discussed. The biological and biochemical features of these inhibitors, including binding interactions, structure-activity relationships (SAR), selectivity, and potencies are discussed. The focus of this article is on the compounds that are promising leads and undergoing advanced clinical investigations, as well as on those that received FDA approval. In this article, we attempt to classify the reviewed FMS inhibitors according to their core chemical structure including pyridine, pyrrolopyridine, pyrazolopyridine, quinoline, and pyrimidine derivatives.
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Affiliation(s)
- Bilal O Alkubaisi
- Research Institute for Medical and Health Sciences, University of Sharjah, Sharjah, 27272, United Arab Emirates
| | - Raya Aljobowry
- College of Pharmacy, University of Sharjah, Sharjah, 27272, United Arab Emirates
| | - Salma M Ali
- College of Pharmacy, University of Sharjah, Sharjah, 27272, United Arab Emirates
| | - Sara Sultan
- College of Pharmacy, University of Sharjah, Sharjah, 27272, United Arab Emirates
| | - Seyed-Omar Zaraei
- Research Institute for Medical and Health Sciences, University of Sharjah, Sharjah, 27272, United Arab Emirates
| | - Anil Ravi
- Research Institute for Medical and Health Sciences, University of Sharjah, Sharjah, 27272, United Arab Emirates
| | - Taleb H Al-Tel
- Research Institute for Medical and Health Sciences, University of Sharjah, Sharjah, 27272, United Arab Emirates; College of Pharmacy, University of Sharjah, Sharjah, 27272, United Arab Emirates.
| | - Mohammed I El-Gamal
- Research Institute for Medical and Health Sciences, University of Sharjah, Sharjah, 27272, United Arab Emirates; College of Pharmacy, University of Sharjah, Sharjah, 27272, United Arab Emirates; Faculty of Pharmacy, Mansoura University, Mansoura, 35516, Egypt.
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15
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Tiwari PC, Pal R, Chaudhary MJ, Nath R. Artificial intelligence revolutionizing drug development: Exploring opportunities and challenges. Drug Dev Res 2023; 84:1652-1663. [PMID: 37712494 DOI: 10.1002/ddr.22115] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 08/14/2023] [Accepted: 09/04/2023] [Indexed: 09/16/2023]
Abstract
By harnessing artificial intelligence (AI) algorithms and machine learning techniques, the entire drug discovery process stands to undergo a profound transformation, offering a myriad of advantages. Foremost among these is the ability of AI to conduct swift and efficient screenings of expansive compound libraries, significantly augmenting the identification of potential drug candidates. Moreover, AI algorithms can prove instrumental in predicting the efficacy and safety profiles of candidate compounds, thus endowing invaluable insights and reducing reliance on extensive preclinical and clinical testing. This predictive capacity of AI has the potential to streamline the drug development pipeline and enhance the success rate of clinical trials, ultimately resulting in the emergence of more efficacious and safer therapeutic agents. However, the deployment of AI in drug discovery introduces certain challenges that warrant attention. A primary hurdle entails the imperative acquisition of high-quality and diverse data. Furthermore, ensuring the interpretability of AI models assumes critical importance in securing regulatory endorsement and cultivating trust within scientific and medical communities. Addressing ethical considerations, including data privacy and mitigating bias, represents an additional momentous challenge, requiring assiduous navigation. In this review, we provide an intricate and comprehensive overview of the multifaceted challenges intrinsic to conventional drug development paradigms, while simultaneously interrogating the efficacy of AI in effectively surmounting these formidable obstacles.
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Affiliation(s)
- Prafulla C Tiwari
- Department of Pharmacology and Therapeutics, King George's Medical University, Lucknow, Uttar Pradesh, India
| | - Rishi Pal
- Department of Pharmacology and Therapeutics, King George's Medical University, Lucknow, Uttar Pradesh, India
| | - Manju J Chaudhary
- Department of Physiology, Government Medical College, Kannauj, Uttar Pradesh, India
| | - Rajendra Nath
- Department of Pharmacology and Therapeutics, King George's Medical University, Lucknow, Uttar Pradesh, India
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16
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Rustandi T, Prihandiwati E, Nugroho F, Hayati F, Afriani N, Alfian R, Aisyah N, Niah R, Rahim A, As-Shiddiq H. Application of artificial intelligence in the development of Jamu "traditional Indonesian medicine" as a more effective drug. Front Artif Intell 2023; 6:1274975. [PMID: 38028667 PMCID: PMC10656769 DOI: 10.3389/frai.2023.1274975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 10/16/2023] [Indexed: 12/01/2023] Open
Affiliation(s)
- Tedi Rustandi
- Department of Pharmacy, School of Health Sciences ISFI, Banjarmasin, Indonesia
- Faculty of Pharmacy, Pancasila University, Jakarta, Indonesia
| | - Erna Prihandiwati
- Department of Pharmacy, School of Health Sciences ISFI, Banjarmasin, Indonesia
| | - Fatah Nugroho
- Faculty of Veterinary Medicine, Gadjah Mada University, Yogyakarta, Special Region of Yogyakarta, Indonesia
| | - Fakhriah Hayati
- Bhayangkara Hospital, Banjarmasin, Indonesia
- Faculty of Pharmacy, Ahmad Dahlan University, Yogyakarta, Special Region of Yogyakarta, Indonesia
| | - Nita Afriani
- Department of Biology, IPB University, Bogor, Indonesia
| | - Riza Alfian
- Department of Pharmacy, School of Health Sciences ISFI, Banjarmasin, Indonesia
| | - Noor Aisyah
- Department of Pharmacy, School of Health Sciences ISFI, Banjarmasin, Indonesia
| | - Rakhmadhan Niah
- Department of Pharmacy, School of Health Sciences ISFI, Banjarmasin, Indonesia
| | - Aulia Rahim
- Department of Pharmacy, School of Health Sciences ISFI, Banjarmasin, Indonesia
| | - Hasbi As-Shiddiq
- Faculty of Pharmacy, Muhammadiyah Banjarmasin University, Banjarmasin, Indonesia
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17
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Xu Z, Wang X, Meng J, Zhang L, Song B. m5U-GEPred: prediction of RNA 5-methyluridine sites based on sequence-derived and graph embedding features. Front Microbiol 2023; 14:1277099. [PMID: 37937221 PMCID: PMC10627201 DOI: 10.3389/fmicb.2023.1277099] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 10/02/2023] [Indexed: 11/09/2023] Open
Abstract
5-Methyluridine (m5U) is one of the most common post-transcriptional RNA modifications, which is involved in a variety of important biological processes and disease development. The precise identification of the m5U sites allows for a better understanding of the biological processes of RNA and contributes to the discovery of new RNA functional and therapeutic targets. Here, we present m5U-GEPred, a prediction framework, to combine sequence characteristics and graph embedding-based information for m5U identification. The graph embedding approach was introduced to extract the global information of training data that complemented the local information represented by conventional sequence features, thereby enhancing the prediction performance of m5U identification. m5U-GEPred outperformed the state-of-the-art m5U predictors built on two independent species, with an average AUROC of 0.984 and 0.985 tested on human and yeast transcriptomes, respectively. To further validate the performance of our newly proposed framework, the experimentally validated m5U sites identified from Oxford Nanopore Technology (ONT) were collected as independent testing data, and in this project, m5U-GEPred achieved reasonable prediction performance with ACC of 91.84%. We hope that m5U-GEPred should make a useful computational alternative for m5U identification.
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Affiliation(s)
- Zhongxing Xu
- Department of Public Health, School of Medicine and Holistic Integrative Medicine, Nanjing University of Chinese Medicine, Nanjing, China
- School of AI and Advanced Computing, Xi'an Jiaotong-Liverpool University, Suzhou, China
| | - Xuan Wang
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, China
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
| | - Jia Meng
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, China
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
- AI University Research Centre, Xi'an Jiaotong-Liverpool University, Suzhou, China
| | - Lin Zhang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China
| | - Bowen Song
- Department of Public Health, School of Medicine and Holistic Integrative Medicine, Nanjing University of Chinese Medicine, Nanjing, China
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18
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Zhong NN, Wang HQ, Huang XY, Li ZZ, Cao LM, Huo FY, Liu B, Bu LL. Enhancing head and neck tumor management with artificial intelligence: Integration and perspectives. Semin Cancer Biol 2023; 95:52-74. [PMID: 37473825 DOI: 10.1016/j.semcancer.2023.07.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 07/11/2023] [Accepted: 07/15/2023] [Indexed: 07/22/2023]
Abstract
Head and neck tumors (HNTs) constitute a multifaceted ensemble of pathologies that primarily involve regions such as the oral cavity, pharynx, and nasal cavity. The intricate anatomical structure of these regions poses considerable challenges to efficacious treatment strategies. Despite the availability of myriad treatment modalities, the overall therapeutic efficacy for HNTs continues to remain subdued. In recent years, the deployment of artificial intelligence (AI) in healthcare practices has garnered noteworthy attention. AI modalities, inclusive of machine learning (ML), neural networks (NNs), and deep learning (DL), when amalgamated into the holistic management of HNTs, promise to augment the precision, safety, and efficacy of treatment regimens. The integration of AI within HNT management is intricately intertwined with domains such as medical imaging, bioinformatics, and medical robotics. This article intends to scrutinize the cutting-edge advancements and prospective applications of AI in the realm of HNTs, elucidating AI's indispensable role in prevention, diagnosis, treatment, prognostication, research, and inter-sectoral integration. The overarching objective is to stimulate scholarly discourse and invigorate insights among medical practitioners and researchers to propel further exploration, thereby facilitating superior therapeutic alternatives for patients.
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Affiliation(s)
- Nian-Nian Zhong
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Han-Qi Wang
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Xin-Yue Huang
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Zi-Zhan Li
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Lei-Ming Cao
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Fang-Yi Huo
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Bing Liu
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China; Department of Oral & Maxillofacial - Head Neck Oncology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China.
| | - Lin-Lin Bu
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China; Department of Oral & Maxillofacial - Head Neck Oncology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China.
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19
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Liu Z, Du Y, Sun Z, Cheng B, Bi Z, Yao Z, Liang Y, Zhang H, Yao R, Kang S, Shi Y, Wan H, Qin D, Xiang L, Leng L, Chen S. Manual correction of genome annotation improved alternative splicing identification of Artemisia annua. PLANTA 2023; 258:83. [PMID: 37721598 DOI: 10.1007/s00425-023-04237-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 09/04/2023] [Indexed: 09/19/2023]
Abstract
Gene annotation is essential for genome-based studies. However, algorithm-based genome annotation is difficult to fully and correctly reveal genomic information, especially for species with complex genomes. Artemisia annua L. is the only commercial resource of artemisinin production though the content of artemisinin is still to be improved. Genome-based genetic modification and breeding are useful strategies to boost artemisinin content and therefore, ensure the supply of artemisinin and reduce costs, but better gene annotation is urgently needed. In this study, we manually corrected the newly released genome annotation of A. annua using second- and third-generation transcriptome data. We found that incorrect gene information may lead to differences in structural, functional, and expression levels compared to the original expectations. We also identified alternative splicing events and found that genome annotation information impacted identifying alternative splicing genes. We further demonstrated that genome annotation information and alternative splicing could affect gene expression estimation and gene function prediction. Finally, we provided a valuable version of A. annua genome annotation and demonstrated the importance of gene annotation in future research.
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Affiliation(s)
- Zhaoyu Liu
- School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, 300193, China
- Institute of Herbgenomics, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China
| | - Yupeng Du
- College of Life Science, Northeast Forestry University, Harbin, 150040, China
| | - Zhihao Sun
- Institute of Herbgenomics, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China
- School of Basic Medical Sciences, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China
| | - Bohan Cheng
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, China
| | - Zenghao Bi
- Institute of Herbgenomics, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China
| | - Zhicheng Yao
- School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen, 333403, China
| | - Yuting Liang
- Institute of Herbgenomics, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China
| | - Huiling Zhang
- College of Horticulture, Sichuan Agricultural University, Chengdu, 611130, China
| | - Run Yao
- Institute of Herbgenomics, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China
| | - Shen Kang
- Institute of Herbgenomics, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China
| | - Yuhua Shi
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, China
| | - Huihua Wan
- Key Laboratory of Beijing for Identification and Safety Evaluation of Chinese Medicine, Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, China
| | - Dou Qin
- Prescription Laboratory of Xinjiang Traditional Uyghur Medicine, Xinjiang Institute of Traditional Uyghur Medicine, Urmuqi, 830000, China
| | - Li Xiang
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, China.
- Prescription Laboratory of Xinjiang Traditional Uyghur Medicine, Xinjiang Institute of Traditional Uyghur Medicine, Urmuqi, 830000, China.
| | - Liang Leng
- Institute of Herbgenomics, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China.
| | - Shilin Chen
- School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, 300193, China.
- Institute of Herbgenomics, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China.
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20
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Liu J, Zheng J, Cai X, Wu D, Yin C. A descriptive study based on the comparison of ChatGPT and evidence-based neurosurgeons. iScience 2023; 26:107590. [PMID: 37705958 PMCID: PMC10495632 DOI: 10.1016/j.isci.2023.107590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 06/21/2023] [Accepted: 08/04/2023] [Indexed: 09/15/2023] Open
Abstract
ChatGPT is an artificial intelligence product developed by OpenAI. This study aims to investigate whether ChatGPT can respond in accordance with evidence-based medicine in neurosurgery. We generated 50 neurosurgical questions covering neurosurgical diseases. Each question was posed three times to GPT-3.5 and GPT-4.0. We also recruited three neurosurgeons with high, middle, and low seniority to respond to questions. The results were analyzed regarding ChatGPT's overall performance score, mean scores by the items' specialty classification, and question type. In conclusion, GPT-3.5's ability to respond in accordance with evidence-based medicine was comparable to that of neurosurgeons with low seniority, and GPT-4.0's ability was comparable to that of neurosurgeons with high seniority. Although ChatGPT is yet to be comparable to a neurosurgeon with high seniority, future upgrades could enhance its performance and abilities.
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Affiliation(s)
- Jiayu Liu
- Department of Neurosurgery, the First Medical Centre, Chinese PLA General Hospital, Beijing 100853, China
| | - Jiqi Zheng
- School of Health Humanities, Peking University, Beijing 100191, China
| | - Xintian Cai
- Department of Graduate School, Xinjiang Medical University, Urumqi 830001, China
| | - Dongdong Wu
- Department of Information, Daping Hospital, Army Medical University, Chongqing 400042, China
| | - Chengliang Yin
- Faculty of Medicine, Macau University of Science and Technology, Macau 999078, China
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21
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Han R, Yoon H, Kim G, Lee H, Lee Y. Revolutionizing Medicinal Chemistry: The Application of Artificial Intelligence (AI) in Early Drug Discovery. Pharmaceuticals (Basel) 2023; 16:1259. [PMID: 37765069 PMCID: PMC10537003 DOI: 10.3390/ph16091259] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 08/24/2023] [Accepted: 09/04/2023] [Indexed: 09/29/2023] Open
Abstract
Artificial intelligence (AI) has permeated various sectors, including the pharmaceutical industry and research, where it has been utilized to efficiently identify new chemical entities with desirable properties. The application of AI algorithms to drug discovery presents both remarkable opportunities and challenges. This review article focuses on the transformative role of AI in medicinal chemistry. We delve into the applications of machine learning and deep learning techniques in drug screening and design, discussing their potential to expedite the early drug discovery process. In particular, we provide a comprehensive overview of the use of AI algorithms in predicting protein structures, drug-target interactions, and molecular properties such as drug toxicity. While AI has accelerated the drug discovery process, data quality issues and technological constraints remain challenges. Nonetheless, new relationships and methods have been unveiled, demonstrating AI's expanding potential in predicting and understanding drug interactions and properties. For its full potential to be realized, interdisciplinary collaboration is essential. This review underscores AI's growing influence on the future trajectory of medicinal chemistry and stresses the importance of ongoing synergies between computational and domain experts.
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Affiliation(s)
| | | | | | | | - Yoonji Lee
- College of Pharmacy, Chung-Ang University, Seoul 06974, Republic of Korea
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22
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Barrett JS, Goyal RK, Gobburu J, Baran S, Varshney J. An AI Approach to Generating MIDD Assets Across the Drug Development Continuum. AAPS J 2023; 25:70. [PMID: 37430126 DOI: 10.1208/s12248-023-00838-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 06/22/2023] [Indexed: 07/12/2023] Open
Abstract
Model-informed drug development involves developing and applying exposure-based, biological, and statistical models derived from preclinical and clinical data sources to inform drug development and decision-making. Discrete models are generated from individual experiments resulting in a single model expression that is utilized to inform a single stage-gate decision. Other model types provide a more holistic view of disease biology and potentially disease progression depending on the appropriateness of the underlying data sources for that purpose. Despite this awareness, most data integration and model development approaches are still reliant on internal (within company) data stores and traditional structural model types. An AI/ML-based MIDD approach relies on more diverse data and is informed by past successes and failures including data outside a host company (external data sources) that may enhance predictive value and enhance data generated by the sponsor to reflect more informed and timely experimentation. The AI/ML methodology also provides a complementary approach to more traditional modeling efforts that support MIDD and thus yields greater fidelity in decision-making. Early pilot studies support this assessment but will require broader adoption and regulatory support for more evidence and refinement of this paradigm. An AI/ML-based approach to MIDD has the potential to transform regulatory science and the current drug development paradigm, optimize information value, and increase candidate and eventually product confidence with respect to safety and efficacy. We highlight early experiences with this approach using the AI compute platforms as representative examples of how MIDD can be facilitated with an AI/ML approach.
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Affiliation(s)
- Jeffrey S Barrett
- Aridhia Bioinformatics, 163 Bath Street, Glasgow, Scotland, G2 4SQ, UK.
| | - Rahul K Goyal
- Center for Translational Medicine, University of Maryland Baltimore, Baltimore, Maryland, USA
| | - Jogarao Gobburu
- Center for Translational Medicine, University of Maryland Baltimore, Baltimore, Maryland, USA
- Pumas-AI, Baltimore, Maryland, USA
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Bas TG. Biosimilars for the next decade in Latin America: a window of opportunity. Expert Opin Biol Ther 2023; 23:659-669. [PMID: 37542714 DOI: 10.1080/14712598.2023.2245780] [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] [Received: 05/05/2023] [Revised: 07/31/2023] [Accepted: 08/04/2023] [Indexed: 08/07/2023]
Abstract
INTRODUCTION Biosimilars are gaining popularity in Latin America (LA). The biosimilars market is expected to grow rapidly over the next decade as a cost-effective alternative to expensive patented biologics. The drivers for the growing demand include needs for affordable health care, the prevalence of chronic diseases, expiration of patents for numerous biologic medicines and the advent of artificial intelligence (AI). Countries such as Argentina, Brazil and Mexico have implemented regulatory frameworks for the approval of biosimilars as well as for investment in local manufacturing capacity, sale, and distribution. Some LA countries face challenges related to low quality institutional frameworks and deficient public policies for regulatory harmonization of these medicines. AREAS COVERED The aim of this article is to analyze the broad window of opportunity for biosimilars in LA (Brazil, Mexico and Argentina) in the next decade, considering their regulations and institutional quality, as well as an affordable cost for patients with chronic diseases and highlight the biosimilars approved in the three countries studied. Likewise, the future contribution of AI in the drug R&D process is considered. EXPERT OPINION Preparing the next decade of biosimilars in LA will involve improving international regulatory frameworks, institutional quality, investments and capacity in R&D (competencies, infrastructure, and AI).
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Affiliation(s)
- Tomas Gabriel Bas
- Universidad Catolica del Norte (Chile), Escuela de Ciencias Empresariales, Coquimbo, Chile
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Zhu A, Chiba S, Shimizu Y, Kunitake K, Okuno Y, Aoki Y, Yokota T. Ensemble-Learning and Feature Selection Techniques for Enhanced Antisense Oligonucleotide Efficacy Prediction in Exon Skipping. Pharmaceutics 2023; 15:1808. [PMID: 37513994 PMCID: PMC10384346 DOI: 10.3390/pharmaceutics15071808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 06/13/2023] [Accepted: 06/15/2023] [Indexed: 07/30/2023] Open
Abstract
Antisense oligonucleotide (ASO)-mediated exon skipping has become a valuable tool for investigating gene function and developing gene therapy. Machine-learning-based computational methods, such as eSkip-Finder, have been developed to predict the efficacy of ASOs via exon skipping. However, these methods are computationally demanding, and the accuracy of predictions remains suboptimal. In this study, we propose a new approach to reduce the computational burden and improve the prediction performance by using feature selection within machine-learning algorithms and ensemble-learning techniques. We evaluated our approach using a dataset of experimentally validated exon-skipping events, dividing it into training and testing sets. Our results demonstrate that using a three-way-voting approach with random forest, gradient boosting, and XGBoost can significantly reduce the computation time to under ten seconds while improving prediction performance, as measured by R2 for both 2'-O-methyl nucleotides (2OMe) and phosphorodiamidate morpholino oligomers (PMOs). Additionally, the feature importance ranking derived from our approach is in good agreement with previously published results. Our findings suggest that our approach has the potential to enhance the accuracy and efficiency of predicting ASO efficacy via exon skipping. It could also facilitate the development of novel therapeutic strategies. This study could contribute to the ongoing efforts to improve ASO design and optimize gene therapy approaches.
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Affiliation(s)
- Alex Zhu
- Phillips Academy, Andover, MA 01810, USA
- Department of Medical Generics, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB T6G 2H7, Canada
| | - Shuntaro Chiba
- HPC- and AI-Driven Drug Development Platform Division, RIKEN Center for Computational Science, Yokohama 230-0045, Japan
| | - Yuki Shimizu
- Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
| | - Katsuhiko Kunitake
- Department of Molecular Therapy, National Institute of Neuroscience, National Center of Neurology and Psychiatry (NCNP), Kodaira, Tokyo 187-8551, Japan
| | - Yasushi Okuno
- HPC- and AI-Driven Drug Development Platform Division, RIKEN Center for Computational Science, Yokohama 230-0045, Japan
- Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
| | - Yoshitsugu Aoki
- Department of Molecular Therapy, National Institute of Neuroscience, National Center of Neurology and Psychiatry (NCNP), Kodaira, Tokyo 187-8551, Japan
| | - Toshifumi Yokota
- Department of Medical Generics, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB T6G 2H7, Canada
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Guo FW, Zhang Q, Gu YC, Shao CL. Sulfur-containing marine natural products as leads for drug discovery and development. Curr Opin Chem Biol 2023; 75:102330. [PMID: 37257309 DOI: 10.1016/j.cbpa.2023.102330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 04/25/2023] [Accepted: 04/26/2023] [Indexed: 06/02/2023]
Abstract
Among the large series of marine natural products (MNPs), sulfur-containing MNPs have emerged as potential therapeutic agents for the treatment of a range of diseases. Herein, we reviewed 95 new sulfur-containing MNPs isolated during the period between 2021 and March 2023. In addition, we discuss that the widely used strategies and the emerging technologies including natural product-based antibody drug conjugates (ADCs), small-molecule-based proteolysis targeting chimeras (PROTACs), nanotechnology-based drug carriers, artificial intelligence (AI)-driven drug discovery have been used for improving the efficiency and success rate of NP-based drug development. We also provide perspectives regarding the challenges and opportunities in sulfur-containing MNPs based drug discovery and development and future research directions.
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Affiliation(s)
- Feng-Wei Guo
- Key Laboratory of Marine Drugs, The Ministry of Education of China School of Medicine and Pharmacy, Ocean University of China, Qingdao 266003, China; Laoshan Laboratory, Qingdao, 266237, China
| | - Qun Zhang
- Key Laboratory of Marine Drugs, The Ministry of Education of China School of Medicine and Pharmacy, Ocean University of China, Qingdao 266003, China; Laoshan Laboratory, Qingdao, 266237, China
| | - Yu-Cheng Gu
- Syngenta Jealott's Hill International Research Centre, Bracknell, Berkshire, RG42 6EY, UK.
| | - Chang-Lun Shao
- Key Laboratory of Marine Drugs, The Ministry of Education of China School of Medicine and Pharmacy, Ocean University of China, Qingdao 266003, China; Laoshan Laboratory, Qingdao, 266237, China.
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Miller LE, Bhattacharyya D, Miller VM, Bhattacharyya M. Recent Trend in Artificial Intelligence-Assisted Biomedical Publishing: A Quantitative Bibliometric Analysis. Cureus 2023; 15:e39224. [PMID: 37337487 PMCID: PMC10277011 DOI: 10.7759/cureus.39224] [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] [Accepted: 05/19/2023] [Indexed: 06/21/2023] Open
Abstract
The rapid advancements in artificial intelligence (AI) technology in recent years have led to its integration into biomedical publishing. However, the extent to which AI has contributed to developing biomedical literature is unclear. This study aimed to identify trends in AI-generated content within peer-reviewed biomedical literature. We first tested the sensitivity and specificity of commercially available AI-detection software (Originality.AI, Collingwood, Ontario, Canada). Next, we conducted a MEDLINE (Medical Literature Analysis and Retrieval System Online) search to identify randomized controlled trials with available abstracts indexed between January 2020 and March 2023. We randomly selected 30 abstracts per quarter during this period and pasted the abstracts into the AI detection software to determine the probability of AI-generated content. The software yielded 100% sensitivity, 95% specificity, and excellent overall discriminatory ability with an area under the receiving operating curve of 97.6%. Among the 390 MEDLINE-indexed abstracts included in the analysis, the prevalence with a high probability (≥ 90%) of AI-generated text increased during the study period from 21.7% to 36.7% (p=0.01) based on a chi-square test for trend. The increasing prevalence of AI-generated text during the study period was also observed in various sensitivity analyses using AI probability thresholds ranging from 50% to 99% (all p≤0.01). The results of this study suggest that the prevalence of AI-assisted publishing in peer-reviewed journals has been increasing in recent years, even before the widespread adoption of ChatGPT (OpenAI, San Francisco, California, United States) and similar tools. The extent to which natural writing characteristics of the authors, utilization of common AI-powered applications, and introduction of AI elements during the post-acceptance publication phase influence AI detection scores warrants further study.
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