Adaptive multi-view learning method for enhanced drug repurposing using chemical-induced transcriptional profiles, knowledge graphs, and large language models DOI Creative Commons
Yudong Yan,

Yinqi Yang,

Zhuohao Tong

и другие.

Journal of Pharmaceutical Analysis, Год журнала: 2025, Номер unknown, С. 101275 - 101275

Опубликована: Март 1, 2025

Язык: Английский

Global Regulatory Frameworks for the Use of Artificial Intelligence (AI) in the Healthcare Services Sector DOI Open Access
Kavitha Palaniappan,

Elaine Yan Ting Lin,

Silke Vogel

и другие.

Healthcare, Год журнала: 2024, Номер 12(5), С. 562 - 562

Опубликована: Фев. 28, 2024

The healthcare sector is faced with challenges due to a shrinking workforce and rise in chronic diseases that are worsening demographic epidemiological shifts. Digital health interventions include artificial intelligence (AI) being identified as some of the potential solutions these challenges. ultimate aim AI systems improve patient’s outcomes satisfaction, overall population’s health, well-being professionals. applications services vast expected assist, automate, augment several services. Like any other emerging innovation, also comes its own risks requires regulatory controls. A review literature was undertaken study existing landscape for developed nations. In global landscape, most regulations revolve around Software Medical Device (SaMD) regulated under digital products. However, it necessary note current may not suffice AI-based technologies capable working autonomously, adapting their algorithms, improving performance over time based on new real-world data they have encountered. Hence, convergence healthcare, similar voluntary code conduct by US-EU Trade Technology Council, would be beneficial all nations, developing or developed.

Язык: Английский

Процитировано

44

Antimicrobial resistance crisis: could artificial intelligence be the solution? DOI Creative Commons
Guangyu Liu, Dan Yu,

Mei-Mei Fan

и другие.

Military Medical Research, Год журнала: 2024, Номер 11(1)

Опубликована: Янв. 23, 2024

Abstract Antimicrobial resistance is a global public health threat, and the World Health Organization (WHO) has announced priority list of most threatening pathogens against which novel antibiotics need to be developed. The discovery introduction are time-consuming expensive. According WHO’s report antibacterial agents in clinical development, only 18 have been approved since 2014. Therefore, critically needed. Artificial intelligence (AI) rapidly applied drug development its recent technical breakthrough dramatically improved efficiency antibiotics. Here, we first summarized recently marketed antibiotics, antibiotic candidates development. In addition, systematically reviewed involvement AI utilization, including small molecules, antimicrobial peptides, phage therapy, essential oils, as well mechanism prediction, stewardship.

Язык: Английский

Процитировано

39

Supervised machine learning in drug discovery and development: Algorithms, applications, challenges, and prospects DOI Creative Commons
George Obaido, Ibomoiye Domor Mienye, Oluwaseun Francis Egbelowo

и другие.

Machine Learning with Applications, Год журнала: 2024, Номер 17, С. 100576 - 100576

Опубликована: Июль 24, 2024

Язык: Английский

Процитировано

20

Revolutionizing Prostate Cancer Therapy: Artificial intelligence – based Nanocarriers for Precision Diagnosis and Treatment DOI
Moein Shirzad,

Afsaneh Salahvarzi,

Sobia Razzaq

и другие.

Critical Reviews in Oncology/Hematology, Год журнала: 2025, Номер unknown, С. 104653 - 104653

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

3

Advances in Artificial Intelligence (AI)-assisted approaches in drug screening DOI Creative Commons
Samvedna Singh, Himanshi Gupta, Priyanshu Sharma

и другие.

Artificial Intelligence Chemistry, Год журнала: 2023, Номер 2(1), С. 100039 - 100039

Опубликована: Дек. 19, 2023

Artificial intelligence (AI) is revolutionizing the current process of drug design and development, addressing challenges encountered in its various stages. By utilizing AI, efficiency significantly improved through enhanced precision, reduced time cost, high-performance algorithms AI-enabled computer-aided (CADD). Effective screening techniques are crucial for identifying potential hit compounds from large volumes data compound repositories. The inclusion AI discovery, including lead molecules, has proven to be more effective than traditional vitro assays. This articlereviews advancements methods achieved AI-enhanced applications, machine learning (ML), deep (DL) algorithms. It specifically focuses on applications discovery phase, exploring strategies optimization such as Quantitative structure-activity relationship (QSAR) modeling, pharmacophore de novo designing, high-throughput virtual screening. Valuable insights into different aspects discussed, highlighting role AI-based tools, pipelines, case studies simplifying complexities associated with discovery.

Язык: Английский

Процитировано

38

Strategies of Artificial intelligence tools in the domain of nanomedicine DOI

Mohammad Habeeb,

Huay Woon You, Mutheeswaran Umapathi

и другие.

Journal of Drug Delivery Science and Technology, Год журнала: 2023, Номер 91, С. 105157 - 105157

Опубликована: Ноя. 10, 2023

Язык: Английский

Процитировано

27

Continuous flow synthesis enabling reaction discovery DOI Creative Commons
Antonella Ilenia Alfano, Jorge García‐Lacuna, Oliver Griffiths

и другие.

Chemical Science, Год журнала: 2024, Номер 15(13), С. 4618 - 4630

Опубликована: Янв. 1, 2024

This article defines the role that continuous flow chemistry can have in new reaction discovery, thereby creating molecular assembly opportunities beyond our current capabilities. Most notably focus is based upon photochemical, electrochemical and temperature sensitive processes where methods machine assisted processing significant impact on chemical reactivity patterns. These platforms are ideally placed to exploit future innovation data acquisition, feed-back control through artificial intelligence (AI) learning (ML) techniques.

Язык: Английский

Процитировано

15

Advanced Design of Soft Robots with Artificial Intelligence DOI Creative Commons
Ying Cao, Bingang Xu, Bin Li

и другие.

Nano-Micro Letters, Год журнала: 2024, Номер 16(1)

Опубликована: Июнь 13, 2024

In recent years, breakthrough has been made in the field of artificial intelligence (AI), which also revolutionized industry robotics. Soft robots featured with high-level safety, less weight, lower power consumption have always one research hotspots. Recently, multifunctional sensors for perception soft robotics rapidly developed, while more algorithms and models machine learning high accuracy optimized proposed. Designs AI advanced ranging from multimodal sensing, human–machine interaction to effective actuation robotic systems. Nonetheless, comprehensive reviews concerning new developments strategies ingenious design systems equipped are rare. Here, development is systematically reviewed AI. First, background mechanisms briefed, after focused on how endow AI, including aspects feeling, thought reaction, illustrated. Next, applications summarized discussed together proposed performance enhancement. Design thoughts future intelligent pointed out. Finally, some perspectives put forward.

Язык: Английский

Процитировано

12

Deep learning assisted prediction of osteogenic capability of orthopedic implant surfaces based on early cell morphology DOI
Andi Li, Xinyi Li, Zhiwen Zhang

и другие.

Acta Biomaterialia, Год журнала: 2025, Номер unknown

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

1

Two heads are better than one: Unravelling the potential Impact of Artificial Intelligence in nanotechnology DOI Creative Commons
Gaurav Gopal Naik,

Vijay A. Jagtap

Nano TransMed, Год журнала: 2024, Номер 3, С. 100041 - 100041

Опубликована: Июль 9, 2024

Artificial Intelligence (AI) and Nanotechnology are two cutting-edge fields that hold immense promise for revolutionizing various aspects of science, technology, everyday life. This review delves into the intersection these disciplines, highlighting synergistic relationship between AI Nanotechnology. It explores how techniques such as machine learning, deep neural networks being employed to enhance efficiency, precision, scalability nanotechnology applications. Furthermore, it discusses challenges, opportunities, future prospects integrating with nanotechnology, paving way transformative advancements in diverse domains ranging from healthcare materials science environmental sustainability beyond.

Язык: Английский

Процитировано

7