Recent advances in AI-based toxicity prediction for drug discovery
Toxicity, defined as the potential harm a substance can cause to living organisms, requires the implementation of stringent regulatory standards to ensure public safety. These standards involve comprehensive testing frameworks, including hazard identification, dose-response evaluation, exposure asse...
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| Format: | Article |
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Chemistry |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fchem.2025.1632046/full |
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| author | Hyundo Lee Jisan Kim Ji-Woon Kim Yoonji Lee Yoonji Lee |
| author_facet | Hyundo Lee Jisan Kim Ji-Woon Kim Yoonji Lee Yoonji Lee |
| author_sort | Hyundo Lee |
| collection | DOAJ |
| description | Toxicity, defined as the potential harm a substance can cause to living organisms, requires the implementation of stringent regulatory standards to ensure public safety. These standards involve comprehensive testing frameworks, including hazard identification, dose-response evaluation, exposure assessment, and risk characterization. In drug discovery and development, these processes are often complex, time-consuming, and also resource-intensive. Toxicity-related failures in the later stages of drug development can lead to substantial financial losses, underscoring the need for reliable toxicity prediction during the early discovery phases. The advent of computational approaches has accelerated a shift toward in silico modeling, virtual screening, and, notably, artificial intelligence (AI) to identify potential toxicities earlier in the pipeline. Ongoing advances in databases, algorithms, and computational power have further expanded AI’s role in pharmaceutical research. Today, AI models are capable of predicting wide range of toxicity endpoints, such as hepatotoxicity, cardiotoxicity, nephrotoxicity, neurotoxicity, and genotoxicity, based on diverse molecular representations ranging from traditional descriptors to graph-based methods. This review provides an in-depth examination of AI-driven toxicity prediction, emphasizing its transformative impact on drug discovery and its growing importance in improving safety assessments. |
| format | Article |
| id | doaj-art-426549de050b4d338e4a2288cbd4341a |
| institution | Kabale University |
| issn | 2296-2646 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Chemistry |
| spelling | doaj-art-426549de050b4d338e4a2288cbd4341a2025-08-20T03:33:27ZengFrontiers Media S.A.Frontiers in Chemistry2296-26462025-07-011310.3389/fchem.2025.16320461632046Recent advances in AI-based toxicity prediction for drug discoveryHyundo Lee0Jisan Kim1Ji-Woon Kim2Yoonji Lee3Yoonji Lee4Department of Global Innovative Drugs, Chung-Ang University, Seoul, Republic of KoreaDepartment of Global Innovative Drugs, Chung-Ang University, Seoul, Republic of KoreaCollege of Pharmacy, Kyung Hee University, Seoul, Republic of KoreaDepartment of Global Innovative Drugs, Chung-Ang University, Seoul, Republic of KoreaCollege of Pharmacy, Chung-Ang University, Seoul, Republic of KoreaToxicity, defined as the potential harm a substance can cause to living organisms, requires the implementation of stringent regulatory standards to ensure public safety. These standards involve comprehensive testing frameworks, including hazard identification, dose-response evaluation, exposure assessment, and risk characterization. In drug discovery and development, these processes are often complex, time-consuming, and also resource-intensive. Toxicity-related failures in the later stages of drug development can lead to substantial financial losses, underscoring the need for reliable toxicity prediction during the early discovery phases. The advent of computational approaches has accelerated a shift toward in silico modeling, virtual screening, and, notably, artificial intelligence (AI) to identify potential toxicities earlier in the pipeline. Ongoing advances in databases, algorithms, and computational power have further expanded AI’s role in pharmaceutical research. Today, AI models are capable of predicting wide range of toxicity endpoints, such as hepatotoxicity, cardiotoxicity, nephrotoxicity, neurotoxicity, and genotoxicity, based on diverse molecular representations ranging from traditional descriptors to graph-based methods. This review provides an in-depth examination of AI-driven toxicity prediction, emphasizing its transformative impact on drug discovery and its growing importance in improving safety assessments.https://www.frontiersin.org/articles/10.3389/fchem.2025.1632046/fullartificial intelligencedrug discoverytoxicityin silico methodsvirtual screening |
| spellingShingle | Hyundo Lee Jisan Kim Ji-Woon Kim Yoonji Lee Yoonji Lee Recent advances in AI-based toxicity prediction for drug discovery Frontiers in Chemistry artificial intelligence drug discovery toxicity in silico methods virtual screening |
| title | Recent advances in AI-based toxicity prediction for drug discovery |
| title_full | Recent advances in AI-based toxicity prediction for drug discovery |
| title_fullStr | Recent advances in AI-based toxicity prediction for drug discovery |
| title_full_unstemmed | Recent advances in AI-based toxicity prediction for drug discovery |
| title_short | Recent advances in AI-based toxicity prediction for drug discovery |
| title_sort | recent advances in ai based toxicity prediction for drug discovery |
| topic | artificial intelligence drug discovery toxicity in silico methods virtual screening |
| url | https://www.frontiersin.org/articles/10.3389/fchem.2025.1632046/full |
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