机器学习的近期进展预测了蛋白质-连接体结合亲缘关系
Jian Jiang1, Daixin Li2, Guilin Wang2
1Research Center of Nonlinear Science, School of Mathematics and Statistics, Wuhan Textile University, Wuhan, 430200, PR China; Department of Mathematics, Michigan State University, East Lansing, MI 48824, USA.
Current opinion in structural biology
|November 29, 2025
概括
机器学习 (ML) 模型正在彻底改变药物设计的蛋白质 - 配体结合亲和力预测. 本综述探讨了基于结构,基于连接体和混合ML的方法,强调了挑战和未来的方向.
科学领域:
- 计算化学计算化学
- 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:
- 精确预测蛋白质 - 配体结合亲缘关系对于合理的药物设计至关重要.
- 传统方法在准确性,成本和依赖评分函数方面的局限性.
- 机器学习 (ML) 为结合亲和力预测提供了新的范式.
研究的目的:
- 审查基于ML的蛋白质-配体结合亲和力预测的最新进展.
- 涵盖基于结构的,基于连接体的和混合ML方法.
- 讨论当前的挑战和该领域的未来趋势.
主要方法:
- 对有关ML应用在约束亲缘关系预测中的最新文献的调查.
- 将方法分类为基于结构的,基于连接体的和混合框架.
- 突出从监督学习到深度学习架构的算法.
主要成果:
- 机器学习模型在提高结合亲和度预测准确度方面显示出显著的前景.
- 不同的ML方法满足各种预测场景.
- 深度学习架构越来越突出.
结论:
- 机器学习正在通过增强成功识别和客优化来改变药物发现.
- 应对当前的挑战将进一步完善ML模型的性能.
- 未来的趋势指向更准确和更广泛的约束亲和力预测工具.
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