Artificial intelligence in antidiabetic drug discovery: The advances in QSAR and the prediction of α-glucosidase inhibitors DOI Creative Commons
Adeshina I. Odugbemi, Clement N. Nyirenda, Alan Christoffels

et al.

Computational and Structural Biotechnology Journal, Journal Year: 2024, Volume and Issue: 23, P. 2964 - 2977

Published: July 6, 2024

Artificial Intelligence is transforming drug discovery, particularly in the hit identification phase of therapeutic compounds. One tool that has been instrumental this transformation Quantitative Structure-Activity Relationship (QSAR) analysis. This computer-aided design uses machine learning to predict biological activity new compounds based on numerical representation chemical structures against various targets. With diabetes mellitus becoming a significant health challenge recent times, there intense research interest modulating antidiabetic α-Glucosidase an target gained attention due its ability suppress postprandial hyperglycaemia, key contributor diabetic complications. review explored detailed approach developing QSAR models, focusing strategies for generating input variables (molecular descriptors) and computational approaches ranging from classical algorithms modern deep algorithms. We also highlighted studies have used these develop predictive models α-glucosidase inhibitors modulate critical target.

Language: Английский

Attention is all you need: utilizing attention in AI-enabled drug discovery DOI Creative Commons
Yang Zhang, Caiqi Liu, Mujiexin Liu

et al.

Briefings in Bioinformatics, Journal Year: 2023, Volume and Issue: 25(1)

Published: Nov. 22, 2023

Abstract Recently, attention mechanism and derived models have gained significant traction in drug development due to their outstanding performance interpretability handling complex data structures. This review offers an in-depth exploration of the principles underlying attention-based advantages discovery. We further elaborate on applications various aspects development, from molecular screening target binding property prediction molecule generation. Finally, we discuss current challenges faced application mechanisms Artificial Intelligence technologies, including quality, model computational resource constraints, along with future directions for research. Given accelerating pace technological advancement, believe that will increasingly prominent role anticipate these usher revolutionary breakthroughs pharmaceutical domain, significantly development.

Language: Английский

Citations

128
Computer-Aided Drug Design and Drug Discovery: A Prospective Analysis DOI Creative Commons
Sarfaraz K. Niazi, Zamara Mariam

Pharmaceuticals, Journal Year: 2023, Volume and Issue: 17(1), P. 22 - 22

Published: Dec. 22, 2023

In the dynamic landscape of drug discovery, Computer-Aided Drug Design (CADD) emerges as a transformative force, bridging realms biology and technology. This paper overviews CADDs historical evolution, categorization into structure-based ligand-based approaches, its crucial role in rationalizing expediting discovery. As CADD advances, incorporating diverse biological data ensuring privacy become paramount. Challenges persist, demanding optimization algorithms robust ethical frameworks. Integrating Machine Learning Artificial Intelligence amplifies predictive capabilities, yet considerations scalability challenges linger. Collaborative efforts global initiatives, exemplified by platforms like Open-Source Malaria, underscore democratization The convergence with personalized medicine offers tailored therapeutic solutions, though dilemmas accessibility concerns must be navigated. Emerging technologies quantum computing, immersive technologies, green chemistry promise to redefine future CADD. trajectory CADD, marked rapid advancements, anticipates accuracy, addressing biases AI, sustainability metrics. concludes highlighting need for proactive measures navigating ethical, technological, educational frontiers shape healthier, brighter

Language: Английский

Citations

105
Harnessing the Power of AI: A Comprehensive Review of Its Impact and Challenges in Nursing Science and Healthcare DOI Open Access
Seema Yelne,

Minakshi Chaudhary,

Karishma Dod

et al.

Cureus, Journal Year: 2023, Volume and Issue: unknown

Published: Nov. 22, 2023

This comprehensive review delves into the impact and challenges of Artificial Intelligence (AI) in nursing science healthcare. AI has already demonstrated its transformative potential these fields, with applications spanning from personalized care diagnostic accuracy to predictive analytics telemedicine. However, integration complexities, including concerns related data privacy, ethical considerations, biases algorithms datasets. The future healthcare appears promising, poised advance diagnostics, treatment, practices. Nevertheless, it is crucial remember that should complement, not replace, professionals, preserving essential human element care. To maximize AI's healthcare, interdisciplinary collaboration, guidelines, protection patient rights are essential. concludes a call action, emphasizing need for ongoing research collective efforts ensure contributes improved outcomes while upholding highest standards ethics patient-centered

Language: Английский

Citations

91
Anticancer Drug Discovery Based on Natural Products: From Computational Approaches to Clinical Studies DOI Creative Commons
Pritee Chunarkar Patil, Mohammed Kaleem, Richa Mishra

et al.

Biomedicines, Journal Year: 2024, Volume and Issue: 12(1), P. 201 - 201

Published: Jan. 16, 2024

Globally, malignancies cause one out of six mortalities, which is a serious health problem. Cancer therapy has always been challenging, apart from major advances in immunotherapies, stem cell transplantation, targeted therapies, hormonal precision medicine, and palliative care, traditional therapies such as surgery, radiation therapy, chemotherapy. Natural products are integral to the development innovative anticancer drugs cancer research, offering scientific community possibility exploring novel natural compounds against cancers. The role like Vincristine Vinblastine thoroughly implicated management leukemia Hodgkin’s disease. computational method initial key approach drug discovery, among various approaches. This review investigates synergy between techniques, highlights their significance discovery process. transition experimental validation highlighted through vitro vivo studies, with examples betulinic acid withaferin A. path toward therapeutic applications have demonstrated clinical studies silvestrol artemisinin, preclinical investigations trials. article also addresses challenges limitations potential anti-cancer drugs. Moreover, integration deep learning artificial intelligence methods may be useful for enhancing products.

Language: Английский

Citations

85
Large Language Models: A Comprehensive Survey of its Applications, Challenges, Limitations, and Future Prospects DOI Creative Commons
Muhammad Usman Hadi,

qasem al tashi,

Rizwan Qureshi

et al.

Published: Nov. 16, 2023

<p>Within the vast expanse of computerized language processing, a revolutionary entity known as Large Language Models (LLMs) has emerged, wielding immense power in its capacity to comprehend intricate linguistic patterns and conjure coherent contextually fitting responses. models are type artificial intelligence (AI) that have emerged powerful tools for wide range tasks, including natural processing (NLP), machine translation, question-answering. This survey paper provides comprehensive overview LLMs, their history, architecture, training methods, applications, challenges. The begins by discussing fundamental concepts generative AI architecture pre- trained transformers (GPT). It then an history evolution over time, different methods been used train them. discusses applications medical, education, finance, engineering. also how LLMs shaping future they can be solve real-world problems. challenges associated with deploying scenarios, ethical considerations, model biases, interpretability, computational resource requirements. highlights techniques enhancing robustness controllability addressing bias, fairness, generation quality issues. Finally, concludes highlighting LLM research need addressed order make more reliable useful. is intended provide researchers, practitioners, enthusiasts understanding evolution, By consolidating state-of-the-art knowledge field, this serves valuable further advancements development utilization applications. GitHub repo project available at https://github.com/anas-zafar/LLM-Survey</p>

Language: Английский

Citations

70
Artificial intelligence in neuro-oncology: advances and challenges in brain tumor diagnosis, prognosis, and precision treatment DOI Creative Commons
Sirvan Khalighi, Kartik Reddy, Abhishek Midya

et al.

npj Precision Oncology, Journal Year: 2024, Volume and Issue: 8(1)

Published: March 29, 2024

Abstract This review delves into the most recent advancements in applying artificial intelligence (AI) within neuro-oncology, specifically emphasizing work on gliomas, a class of brain tumors that represent significant global health issue. AI has brought transformative innovations to tumor management, utilizing imaging, histopathological, and genomic tools for efficient detection, categorization, outcome prediction, treatment planning. Assessing its influence across all facets malignant management- diagnosis, prognosis, therapy- models outperform human evaluations terms accuracy specificity. Their ability discern molecular aspects from imaging may reduce reliance invasive diagnostics accelerate time diagnoses. The covers techniques, classical machine learning deep learning, highlighting current applications challenges. Promising directions future research include multimodal data integration, generative AI, large medical language models, precise delineation characterization, addressing racial gender disparities. Adaptive personalized strategies are also emphasized optimizing clinical outcomes. Ethical, legal, social implications discussed, advocating transparency fairness integration neuro-oncology providing holistic understanding impact patient care.

Language: Английский

Citations

62
A Comprehensive Systematic Review of YOLO for Medical Object Detection (2018 to 2023) DOI Creative Commons
Mohammed Gamal Ragab, Said Jadid Abdulkadir, Amgad Muneer

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 57815 - 57836

Published: Jan. 1, 2024

YOLO (You Only Look Once) is an extensively utilized object detection algorithm that has found applications in various medical tasks. This been accompanied by the emergence of numerous novel variants recent years, such as YOLOv7 and YOLOv8. study encompasses a systematic exploration PubMed database to identify peer-reviewed articles published between 2018 2023. The search procedure 124 relevant studies employed for diverse tasks including lesion detection, skin classification, retinal abnormality identification, cardiac brain tumor segmentation, personal protective equipment detection. findings demonstrated effectiveness outperforming alternative existing methods these However, review also unveiled certain limitations, well-balanced annotated datasets, high computational demands. To conclude, highlights identified research gaps proposes future directions leveraging potential

Language: Английский

Citations

55
Advances in Artificial Intelligence (AI)-assisted approaches in drug screening DOI Creative Commons
Samvedna Singh, Himanshi Gupta, Priyanshu Sharma

et al.

Artificial Intelligence Chemistry, Journal Year: 2023, Volume and Issue: 2(1), P. 100039 - 100039

Published: Dec. 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.

Language: Английский

Citations

45
How successful are AI-discovered drugs in clinical trials? A first analysis and emerging lessons DOI Creative Commons

Madura K P Jayatunga,

Margaret Ayers,

Lotte Bruens

et al.

Drug Discovery Today, Journal Year: 2024, Volume and Issue: 29(6), P. 104009 - 104009

Published: April 30, 2024

AI techniques are making inroads into the field of drug discovery. As a result, growing number drugs and vaccines have been discovered using AI. However, questions remain about success these molecules in clinical trials. To address questions, we conducted first analysis pipelines AI-native Biotech companies. In Phase I find AI-discovered an 80–90% rate, substantially higher than historic industry averages. This suggests, argue, that is highly capable designing or identifying with drug-like properties. II rate ∼40%, albeit on limited sample size, comparable to Our findings highlight early signs potential for molecules.

Language: Английский

Citations

38
Unleashing the power of generative AI in drug discovery DOI Creative Commons

Amit Gangwal,

Antonio Lavecchia

Drug Discovery Today, Journal Year: 2024, Volume and Issue: 29(6), P. 103992 - 103992

Published: April 23, 2024

Artificial intelligence (AI) is revolutionizing drug discovery by enhancing precision, reducing timelines and costs, enabling AI-driven computer-aided design. This review focuses on recent advancements in deep generative models (DGMs) for de novo design, exploring diverse algorithms their profound impact. It critically analyses the challenges that are intricately interwoven into these technologies, proposing strategies to unlock full potential. features case studies of both successes failures advancing drugs clinical trials with AI assistance. Last, it outlines a forward-looking plan optimizing DGMs thereby fostering faster more cost-effective development.

Language: Английский

Citations

36