DRPMKB1.0:以人工智能为导向的药物重新定位预测模型的全面知识库
Xin Zheng1, Cheng Bi1, Weichen Bo2
1Department of Respiratory and Critical Care Medicine, Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China.
Journal of chemical information and modeling
|December 31, 2025
概括
我们开发了DRPMKB 1.0,这是一个集成AI模型用于药物重新定位 (DR) 的知识库. 该平台通过个性化模型建议和标准化模型选择来提高预测准确性,以提高药物发现效率.
科学领域:
- 计算生物学是一种计算生物学.
- 人工智能在药物发现中的作用
- 药理学 药理学是指药理学的学科.
背景情况:
- 药物重新定位 (DR) 通过为现有药物找到新的用途来加速药物开发.
- 人工智能 (AI) 模型在DR中的扩散需要有效的集成和资源管理.
- 大型语言模型 (LLM) 具有广泛的适用性,但受益于个性化的知识库,以提高准确性.
研究的目的:
- 开发一个全面的,以人工智能为导向的知识库,用于药物重新定位的预测 (DRPMKB 1.0).
- 为DR创建一个标准化的框架,用于评估和整合各种AI模型和数据集.
- 通过量身定制的模型建议,提高个性化药物重新定位的准确性和效率.
主要方法:
- 从PubMed收集到2024年3月的数据,涵盖了45个类别,193个模型和693个数据输入.
- 开发了DRPMKB 1.0,具有跨数据,模型,应用程序和参考维度的显示和交互接口.
- 建立了双重评估框架,以评估固有的模型质量和预测证据.
主要成果:
- DRPMKB 1.0为DR提供了一个集中式的数据共享平台.
- 双重评估框架标准化了模型选择和评估.
- 基于用户数据的个性化建议显著提高了DR预测准确度.
结论:
- DRPMKB 1.0为人工智能驱动的药物重新定位提供了一个强大的,集成的平台.
- 该知识库促进了各种数据和模型的无集成,支持持续的AI增强.
- 该资源使研究人员能够提供量身定制的模型建议,推进个性化药物发现工作.
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