大型语言模型在药物诱导骨毒性预测中的应用
Yi-Qi Chen1, Tao Yu1, Zheng-Qi Song1
1Department of Orthopaedics, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou 32500, China.
Journal of chemical information and modeling
|March 21, 2025
概括
机器学习模型,包括DeepSeek和ChatGPT,可以预测药物诱导的骨质毒性. 这有助于在药物开发过程中评估骨副作用,提高患者的安全性和治疗结果.
科学领域:
- 药理学和毒理学 药理学和毒理学
- 计算化学计算化学
- 药品安全 药品安全
背景情况:
- 药物诱导的骨质毒性带来了重大的临床和发育挑战.
- 目前的毒性评估模型缺乏专门的数据集和算法来预测骨毒性.
- 识别和减轻骨的副作用对于患者的安全至关重要.
研究的目的:
- 开发和评估用于预测药物诱导骨质毒性的机器学习模型.
- 评估大型语言模型 (LLM) 在识别骨毒分子方面的有效性.
- 通过准确的毒性预测,改善药物开发中的安全协议.
主要方法:
- 收集一组骨质毒性分子数据集.
- 应用各种机器学习算法,包括DeepSeek和ChatGPT.
- 对LLM和用于毒性预测的传统机器学习方法进行比较分析.
主要成果:
- 深度搜索R1和ChatGPT o3模型实现了高精度 (ACC值分别为0.87和0.88).
- 机器学习方法在预测分子骨毒性方面显示出显著的潜力.
- 在识别对骨健康有害影响方面,LLM显示出有前途.
结论:
- 机器学习,特别是LLM,可以有效地预测药物诱导的骨质毒性.
- 这些模型为增强开发期间药物安全性评估提供了有价值的工具.
- 这些发现支持改善骨健康监测和化学和健康科学中的公共安全.
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