机器学习用于序列和基于结构的蛋白质-连接物相互作用预测
Yunjiang Zhang1, Shuyuan Li1, Kong Meng1
1Beijing Key Laboratory for Green Catalysis and Separation, The Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, P. R. China.
Journal of chemical information and modeling
|February 22, 2024
概括
机器学习模型准确地预测药物向相互作用,加速药物发现. 本综述考察了用于蛋白质 - 连接体相互作用预测的计算方法,数据集和模型,突出了应用和未来方向.
科学领域:
- 计算化学和生物信息学
- 药物的发现和开发.
- 在药理学中的机器学习.
背景情况:
- 药物开发是昂贵和耗时的.
- 准确预测药物向相互作用对于有效的药物发现至关重要.
- 机器学习 (ML) 方法在预测蛋白质-连接体相互作用方面表现有前途.
研究的目的:
- 审查用于蛋白质 - 连接体相互作用预测的计算方法.
- 根据序列和结构数据对ML模型进行分类和总结.
- 讨论该领域的应用,评估,可解释性,挑战和未来方向.
主要方法:
- 在蛋白质 - 配体相互作用研究中使用的数据集的概述.
- 检查各种蛋白质和连接体表示方法.
- 使用基于序列和基于结构的标准对ML模型 (经典和深度学习) 的分类.
主要成果:
- 用于蛋白质 - 配体相互作用预测的各种ML模型的总结.
- 讨论评估指标和模型可解释性技术.
- 探索这些模型在药物研究中的应用.
结论:
- 计算方法,特别是ML,提供了一个强大的方法来预测药物向相互作用.
- 需要进一步的研究来应对当前的挑战,并推进该领域的未来方向.
- 本综述为药物发现和计算生物学研究人员提供了全面的概述.
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