使用人工智能识别基于天然产品的药物组合 (NPDC)
Tianle Niu1, Yimiao Zhu2, Minjie Mou3
1School of Pharmacy, Hebei Medical University, Shijiazhuang 050017, China; College of Pharmaceutical Sciences, State Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China.
人工智能 (AI) 加快了针对复杂疾病的基于天然产品的药物组合 (NPDC) 发现. 人工智能整合了多样化的数据,提高了预测准确度,并指导了新型协同疗法的实验验证.
科学领域:
- 药理学和药物发现
- 计算生物学 计算生物学
- 人工智能在医学中的应用
背景情况:
- 基于天然产品的药物组合 (NPDCs) 为复杂疾病提供了独特的治疗潜力.
- 传统的方法,如高通量选 (HTS) 和计算方法,由于数据碎片化,成本和庞大的组合空间而面临局限性.
- 需要有效和准确的方法来发现协同作用的NPDC是关键的.
研究的目的:
- 审查近期人工智能 (AI) 驱动的NPDC预测方面的进展.
- 介绍人工智能驱动的NPDC发现中使用的关键数据资源和算法框架.
- 评估AI在加速NPDC识别方面的当前局限性和未来前景.
主要方法:
- 整合多源异质数据用于NPDC预测.
- 机器学习和深度学习算法的应用用于自主特征提取.
- 对应用到NPDC发现的AI方法的综合文献综述.
主要成果:
- 与传统方法相比,人工智能显著提高了协同NPDC的预测准确性.
- 人工智能可以从复杂的数据集中自主提取特征,提高发现效率.
- 人工智能方法提供了一个强大的技术方法来识别新的NPDC.
结论:
- 人工智能是一个强大的工具,可以大大加快发现基于天然产品的药物组合.
- 人工智能驱动的方法提高了识别协同作用药物组合的准确性和效率.
- 预计未来的人工智能应用将进一步完善NPDC发现,并为实验验证策略提供信息.
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