Artificial Intelligence-Driven Drug Toxicity Prediction: Advances, Challenges, and Future Directions
Drug toxicity prediction plays a crucial role in the drug research and development process, ensuring clinical drug safety. However, traditional methods are hampered by high cost, low throughput, and uncertainty of cross-species extrapolation, which has become a key bottleneck restricting the efficie...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Toxics |
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| Online Access: | https://www.mdpi.com/2305-6304/13/7/525 |
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| author | Ruiqiu Zhang Hairuo Wen Zhi Lin Bo Li Xiaobing Zhou |
| author_facet | Ruiqiu Zhang Hairuo Wen Zhi Lin Bo Li Xiaobing Zhou |
| author_sort | Ruiqiu Zhang |
| collection | DOAJ |
| description | Drug toxicity prediction plays a crucial role in the drug research and development process, ensuring clinical drug safety. However, traditional methods are hampered by high cost, low throughput, and uncertainty of cross-species extrapolation, which has become a key bottleneck restricting the efficiency of new drug research and development. The breakthrough development of Artificial Intelligence (AI) technology, especially the application of deep learning and multimodal data fusion strategy, is reshaping the scientific paradigm of drug toxicology assessment. In this review, we focus on the application of AI in the field of drug toxicity prediction and systematically summarize the relevant literature and development status globally in the past years. The application of various toxicity databases in the prediction was elaborated in detail, and the research results and methods for the prediction of different toxicity endpoints were analyzed in depth, including acute toxicity, carcinogenicity, organ-specific toxicity, etc. Furthermore, this paper discusses the application progress of AI technologies (e.g., machine learning and deep learning model) in drug toxicity prediction, analyzes their advantages and challenges, and outlines the future development direction. It aims to provide a comprehensive and in-depth theoretical framework and actionable technical strategies for toxicity prediction in drug development. |
| format | Article |
| id | doaj-art-957a8abfec634e6cb25ba16575a8d919 |
| institution | DOAJ |
| issn | 2305-6304 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Toxics |
| spelling | doaj-art-957a8abfec634e6cb25ba16575a8d9192025-08-20T03:07:57ZengMDPI AGToxics2305-63042025-06-0113752510.3390/toxics13070525Artificial Intelligence-Driven Drug Toxicity Prediction: Advances, Challenges, and Future DirectionsRuiqiu Zhang0Hairuo Wen1Zhi Lin2Bo Li3Xiaobing Zhou4National Institutes for Food and Drug Control, Chinese Academy of Medical-Sciences and Peking Union Medical College, Beijing 100730, ChinaNational Center for Safety Evaluation of Drugs, National Institutes for Food and Drug Control, Beijing 100076, ChinaNational Center for Safety Evaluation of Drugs, National Institutes for Food and Drug Control, Beijing 100076, ChinaNational Institutes for Food and Drug Control, Chinese Academy of Medical-Sciences and Peking Union Medical College, Beijing 100730, ChinaNational Center for Safety Evaluation of Drugs, National Institutes for Food and Drug Control, Beijing 100076, ChinaDrug toxicity prediction plays a crucial role in the drug research and development process, ensuring clinical drug safety. However, traditional methods are hampered by high cost, low throughput, and uncertainty of cross-species extrapolation, which has become a key bottleneck restricting the efficiency of new drug research and development. The breakthrough development of Artificial Intelligence (AI) technology, especially the application of deep learning and multimodal data fusion strategy, is reshaping the scientific paradigm of drug toxicology assessment. In this review, we focus on the application of AI in the field of drug toxicity prediction and systematically summarize the relevant literature and development status globally in the past years. The application of various toxicity databases in the prediction was elaborated in detail, and the research results and methods for the prediction of different toxicity endpoints were analyzed in depth, including acute toxicity, carcinogenicity, organ-specific toxicity, etc. Furthermore, this paper discusses the application progress of AI technologies (e.g., machine learning and deep learning model) in drug toxicity prediction, analyzes their advantages and challenges, and outlines the future development direction. It aims to provide a comprehensive and in-depth theoretical framework and actionable technical strategies for toxicity prediction in drug development.https://www.mdpi.com/2305-6304/13/7/525Artificial Intelligencemachine learningdeep learningtoxicity endpointstransfer learning |
| spellingShingle | Ruiqiu Zhang Hairuo Wen Zhi Lin Bo Li Xiaobing Zhou Artificial Intelligence-Driven Drug Toxicity Prediction: Advances, Challenges, and Future Directions Toxics Artificial Intelligence machine learning deep learning toxicity endpoints transfer learning |
| title | Artificial Intelligence-Driven Drug Toxicity Prediction: Advances, Challenges, and Future Directions |
| title_full | Artificial Intelligence-Driven Drug Toxicity Prediction: Advances, Challenges, and Future Directions |
| title_fullStr | Artificial Intelligence-Driven Drug Toxicity Prediction: Advances, Challenges, and Future Directions |
| title_full_unstemmed | Artificial Intelligence-Driven Drug Toxicity Prediction: Advances, Challenges, and Future Directions |
| title_short | Artificial Intelligence-Driven Drug Toxicity Prediction: Advances, Challenges, and Future Directions |
| title_sort | artificial intelligence driven drug toxicity prediction advances challenges and future directions |
| topic | Artificial Intelligence machine learning deep learning toxicity endpoints transfer learning |
| url | https://www.mdpi.com/2305-6304/13/7/525 |
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