概述了预测化合物-蛋白相互作用 (CPI) 的最新进展和挑战
Yanbei Li1,2,3, Zhehuan Fan2,3, Jingxin Rao2,3
1School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, Zhejiang Province, China.
Medical review (2021)
|January 29, 2024
概括
机器学习 (ML) 模型正在推进药物发现的化合物-蛋白相互作用 (CPI) 预测. 本综述涵盖了ML方法,数据集和CPI预测的未来趋势.
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.
- 人工智能的人工智能是人工智能.
背景情况:
- 化合物-蛋白相互作用 (CPI) 对药物发现,标识和预测副作用至关重要.
- 机器学习 (ML) 与传统方法相比,为CPI预测提供了高效和成本效益的解决方案.
研究的目的:
- 提供基于ML的CPI预测近期进展的全面概述.
- 突出基于结构和非结构的ML模型的性能和成就.
- 讨论CPI预测数据集,评估基准,挑战和未来趋势.
主要方法:
- 对用于CPI预测的ML模型最近文献的综述.
- 将模型分为基于结构的和非基于结构的方法.
- 在CPI预测研究中使用的数据集和评估指标的分析.
主要成果:
- 机器学习模型在预测CPI方面取得了重大进展.
- 基于结构和非结构的ML方法都显示出有前途.
- 确定了评估CPI预测模型的关键数据集和基准.
结论:
- 机器学习是通过准确的CPI预测加速药物发现的强大工具.
- 应对当前的挑战和探索新兴趋势将进一步提高CPI预测能力.
- 对CPI的ML的持续研究对于推进治疗开发至关重要.
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