Yi-Qi Chen
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Yi-Qi Chen has a total of 10 co-authors. Here are the top co-authors by number of shared publications:
Chen-Yu Wang
Department of Orthopaedics
1 shared publication- Haiming Jin
Department of Orthopaedics, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, China
1 shared publication Heng Qiu
Department of Chemistry
1 shared publicationJiang-Tao Luo
Department of Orthopaedics
1 shared publicationQing-Qing Wang
Department of Orthopaedic Surgery, Sir Run Run Shaw Hospital
1 shared publicationTao Yu
Department of Orthopaedics
1 shared publication
Latest Publications
- 21 April 2026
- Published by Wolters Kluwer Health in Circulation
- Vol. 153 (16) , e1122-e1123
Letter by Chen et al Regarding Article, “Prognostic Role of Myocarditis-Like Episodes and Their Treatment in Patients With Pathogenic Desmoplakin Variants”
- 21 April 2026
- Published by Wolters Kluwer Health in Circulation
- Vol. 153 (16) , e1122-e1123
Letter by Chen et al Regarding Article, “Prognostic Role of Myocarditis-Like Episodes and Their Treatment in Patients With Pathogenic Desmoplakin Variants”
- 21 March 2025
- Published by American Chemical Society (ACS) in Journal of Chemical Information and Modeling
- Vol. 65 (7) , 3370-3379
Application of Large Language Models in Drug-Induced Osteotoxicity Prediction
Drug-induced osteotoxicity refers to the harmful effects certain drugs have on the skeletal system, posing significant safety risks. These toxic effects are a key concern in clinical practice, drug development, and environmental management. However, existing toxicity assessment models lack specialized data sets and algorithms for predicting osteotoxicity. In our study, we collected osteotoxic molecules and employed various large language models, including DeepSeek and ChatGPT, alongside traditional machine learning methods to predict their properties. Among these, the DeepSeek R1 and ChatGPT o3 models achieved ACC values of 0.87 and 0.88, respectively. Our results indicate that machine learning methods can assist in evaluating the impact of harmful substances on bone health during drug development, improving safety protocols, mitigating skeletal side effects, and enhancing treatment outcomes and public safety. Furthermore, it highlights the potential of large language models in predicting molecular toxicity and their significance in the fields of health and chemical sciences.
- 21 March 2025
- Published by American Chemical Society (ACS) in Journal of Chemical Information and Modeling
- Vol. 65 (7) , 3370-3379
Application of Large Language Models in Drug-Induced Osteotoxicity Prediction
Drug-induced osteotoxicity refers to the harmful effects certain drugs have on the skeletal system, posing significant safety risks. These toxic effects are a key concern in clinical practice, drug development, and environmental management. However, existing toxicity assessment models lack specialized data sets and algorithms for predicting osteotoxicity. In our study, we collected osteotoxic molecules and employed various large language models, including DeepSeek and ChatGPT, alongside traditional machine learning methods to predict their properties. Among these, the DeepSeek R1 and ChatGPT o3 models achieved ACC values of 0.87 and 0.88, respectively. Our results indicate that machine learning methods can assist in evaluating the impact of harmful substances on bone health during drug development, improving safety protocols, mitigating skeletal side effects, and enhancing treatment outcomes and public safety. Furthermore, it highlights the potential of large language models in predicting molecular toxicity and their significance in the fields of health and chemical sciences.