对基于深度学习的方法进行全面的审查,以预测药物相互作用
Yan Xia1, An Xiong1, Zilong Zhang1
1School of Computer Science and Technology, Hainan University, No. 58, Renmin Avenue, Haidian Island, Haikou, Hainan Province, 570228, China.
Briefings in functional genomics
|February 23, 2025
概括
深度学习通过预测药物相互作用 (DDI) 来推进生物医学研究. 本综述指导研究人员通过各种计算方法和分子表示来准确预测DDI.
科学领域:
- 生物医学信息学 生物医学信息学
- 计算化学的计算化学
- 药理学 药理学是指药理学的学科.
背景情况:
- 药物相互作用 (DDI) 具有重大风险,影响药物开发和临床结果.
- 传统的DDI预测方法昂贵且耗时.
- 人工智能和深度学习提供了有希望的替代方案,但也带来了数据和方法方面的挑战.
研究的目的:
- 审查和综合当前的DDI预测方法.
- 为该领域的研究人员提供全面指南.
- 分析分子表示和基于图形的特征提取模型.
主要方法:
- 基于相似性,基于网络和基于集成的DDI预测方法的审查.
- 对分子表示技术的分析.
- 图形数据的系统曝光功能提取模型.
主要成果:
- 多种DDI预测策略的结构化概述.
- 洞察有效的分子编码和从图形数据中提取特征.
- 在人工智能驱动的DDI预测中识别挑战和进步.
结论:
- 深度学习显著提高了DDI预测的准确性和效率.
- 了解分子表示和图形模型对于开发强大的DDI预测系统至关重要.
- 这篇评论是研究人员导航基于AI的DDI预测的宝贵资源.
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