利用人工智能来融合用于药物标识的表型特征:计算建模方面的进展
Fengming Chen1, Ranran Zhao2, Xingxing Han3
1State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, No. 16, South Xiaojie, Dongzhimen Inner, Dongcheng District, Beijing 100700 PR China.
Briefings in bioinformatics
|February 9, 2026
概括
使用基因表达数据的计算模型有效地预测多目标药物相互作用 (DTI). 这种方法绕过了结构数据的需求,加速了药物发现,并推进了精准医学.
科学领域:
- 计算生物学是一种计算生物学.
- 药理学 药理学是指药理学的学科.
- 生物信息学是一种生物信息学.
背景情况:
- 传统的药物向相互作用 (DTI) 预测方法通常需要广泛的结构性或向特定数据.
- 基因表达特征提供了一个数据驱动的替代方案,利用转录特征进行功能推断.
- 现型信息整合桥梁选和目标预测,创造了一个新的目标识别范式.
研究的目的:
- 审查和比较目前用于目标识别的计算方法,重点是基于基因表达的方法来预测DTI.
- 突出基因表达分析在药物发现中的优势,特别是在数据有限的情况下.
- 为多目标DTI预测提供有关数据库,方法和未来方向的指导.
主要方法:
- 使用大规模基因表达数据对三种主要计算模型类型的分析:基于生物网络,基于协会和多式联结.
- 每种型号的优势和局限性的比较.
- 讨论包括数据集成,算法优化和多omics融合在内的挑战.
主要成果:
- 基于基因表达的模型能够在没有明确结构数据的情况下推断DTI的功能性推断,在数据有限的场景中证明有价值.
- 计算DTI模型作为有效的假设生成工具,降低实验成本和加速药物发现.
- 将表型信息与计算分析的整合促进了精准医学的发展,并有助于理解复杂的疾病机制.
结论:
- 基因表达造型提供了一种强大而通用的方法来预测多目标药物相互作用.
- 计算DTI模型对于产生假设,降低成本和加速药物发现管道至关重要.
- 未来的研究应该专注于数据集成,算法优化和多omics融合,以充分利用基因表达数据来进行先进的药物预测和精准医学.
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