在分子科学中对拓数据分析和拓深度学习的综述
JunJie Wee1, Jian Jiang1,2
1Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States.
Journal of chemical information and modeling
|November 14, 2025
概括
拓数据分析 (TDA) 为人工智能和拓深度学习提取分子特征. 这份审查详细介绍了TDA的细节.
科学领域:
- 分子科学 分子科学
- 计算化学是一种计算化学.
- 数据科学是数据科学.
背景情况:
- 拓数据分析 (TDA) 为复杂的分子数据提供了强大的特征提取.
- 它的应用在人工智能 (AI) 和拓深度学习 (TDL) 中越来越多.
研究的目的:
- 为TDA的发展,方法和分子科学中的应用提供全面的审查.
- 要突出TDA与AI和机器学习的整合,用于预测建模.
主要方法:
- 审查TDA演变,包括持久的同类学和拓机器学习.
- 在生物分子稳定性,药物发现和材料科学中探索TDA应用.
- 对TDA与人工智能的集成进行分析,用于蛋白质工程和毒性预测.
主要成果:
- 对于分子数据,TDA提供了可解释的多尺度特征.
- 在多种分子科学领域中,TDA 已经显示出显著的影响.
- 与人工智能的整合使分子研究中的先进预测能力成为可能.
结论:
- TDA是分子科学的一个强大的框架,增强AI和TDL.
- 未来的方向包括与先进的人工智能和新的拓不变体的TDA集成.
- 本综述是应用拓学在分子研究中的基础参考.
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