Machine Learning‐Enabled Drug‐Induced Toxicity Prediction

Advanced Science, Год журнала: 2025, Номер unknown

Опубликована: Фев. 3, 2025

Abstract Unexpected toxicity has become a significant obstacle to drug candidate development, accounting for 30% of discovery failures. Traditional assessment through animal testing is costly and time‐consuming. Big data artificial intelligence (AI), especially machine learning (ML), are robustly contributing innovation progress in toxicology research. However, the optimal AI model different types usually varies, making it essential conduct comparative analyses methods across domains. The diverse sources also pose challenges researchers focusing on specific studies. In this review, 10 categories drug‐induced examined, summarizing characteristics applicable ML models, including both predictive interpretable algorithms, striking balance between breadth depth. Key databases tools used prediction highlighted, toxicology, chemical, multi‐omics, benchmark databases, organized by their focus function clarify roles prediction. Finally, strategies turn into opportunities analyzed discussed. This review may provide with valuable reference understanding utilizing available resources bridge mechanistic insights, further advance application drugs‐induced

Язык: Английский

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Comprehensive Characterization of Oxidative Stress-Modulating Chemicals Using GPT-Based Text Mining

Environmental Science & Technology, Год журнала: 2024, Номер 58(46), С. 20540 - 20552

Опубликована: Ноя. 8, 2024

The screening of hazardous environmental pollutants is hindered by the limited availability toxicological databases. Large language model (LLM)-based text mining holds potential to automatically extract complex information from literature. Due its relevance diseases and challenge comprehensive characterization, oxidative stress serves as a suitable case for research texting mining. In this study, robust workflow utilizing LLM (i.e., GPT-4) was developed on tests, including data collection, preprocessing, prompt engineering, performance evaluation procedures. A total 17,780 relevant records were extracted 7166 articles, covering 2558 unique compounds. rising interest in observed over past two decades. list known prooxidants (

Язык: Английский

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Artificial Intelligence in Experimental Surgery: Ethical Breakthroughs and Technological Innovations within In Silico Models

Опубликована: Фев. 10, 2025

Integrating artificial intelligence (AI) into experimental surgery represents a transformative shift in biomedical research, offering innovative alternatives to traditional animal-based preclinical models. AI-driven methodologies, including computerized models and surgical simulations, enhance precision, reproducibility, ethical compliance while reducing reliance on _in vivo_ experimentation. This review systematically explores the role of AI optimizing procedures, operative techniques, technology, analyzing its impact decision-making, predictive modeling, training simulations. A comprehensive search was conducted across PubMed, Embase, Scopus, Web Science, SciELO, identifying studies AI-enhanced strategies, silico models, validation techniques. The findings highlight AI's potential replace animal testing, refine training, improve research accuracy. However, challenges remain, data standardization, regulatory adaptation, considerations related methodologies. Addressing these requires interdisciplinary collaboration development validated frameworks support widespread implementation surgery. Future should focus standardizing applications, ensuring methodological transparency, integrating clinical translation pathways. underscores revolutionary shaping future path more ethical, precise,

Язык: Английский

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Thalidomide: Following Tragedy, a Repurposed Molecule With Continuing Opportunities for Clinical Benefit

Clinical Therapeutics, Год журнала: 2025, Номер unknown

Опубликована: Фев. 1, 2025

Язык: Английский

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An Immune-Liver Microphysiological System Method for Evaluation and Quality Control of Hepatotoxicity Induced by Polygonum multiflorum Thunb. Extract

Journal of Ethnopharmacology, Год журнала: 2025, Номер unknown, С. 119633 - 119633

Опубликована: Март 1, 2025

Язык: Английский

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