在计算生物学中,可学习的蛋白质表示用于预测药物标亲和力
Rachit Kumar1,2,3, Joseph Romano4,5, Marylyn Ritchie6
1Medical Scientist Training Program, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA. rachit.kumar@pennmedicine.upenn.edu.
Journal of cheminformatics
|January 10, 2026
概括
本综述探讨了可学习的蛋白质表示,用于预测药物向亲和力,检查数据源,训练方法和深度学习技术. 它提供了增强药物开发和评估策略的见解.
科学领域:
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
- 药物发现 药物发现 药物发现
背景情况:
- 药物标亲和力预测对于药物开发至关重要.
- 有效的蛋白质表示是准确预测的关键.
- 现有的方法在数据源,培训和编码技术方面各不相同.
研究的目的:
- 审查可学习的蛋白质表示,用于药物向 afinity 预测.
- 分析不同的视角,包括数据源,培训范式和深度学习方法.
- 为改进药物向 afinity 预测方法提供见解.
主要方法:
- 对计算生物学研究的文献综述.
- 分析蛋白质表示类型及其应用.
- 专注于基于深度学习的编码和嵌入方法.
主要成果:
- 在计算生物学中使用了各种可学习的蛋白质表示.
- 深度学习方法在生成蛋白质嵌入时非常突出.
- 数据来源和训练范式的选择显著影响了预测准确性.
结论:
- 蛋白质表示对于推进药物向亲和力预测至关重要.
- 对新型表征和培训策略的进一步研究可以改善药物发现管道.
- 本综述可以作为开发增强预测模型的资源.
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