新闻是可以预测的吗?
Clara Fannjiang1, Jennifer Listgarten1
1Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California 94720, USA clarafy@berkeley.edu jennl@berkeley.edu.
Cold Spring Harbor perspectives in biology
|December 5, 2023
概括
机器学习 (ML) 有助于科学设计,但平衡新发现与失败风险是关键. 本研究探讨了基于ML的蛋白质设计策略,以实现新特性,同时管理不确定性.
科学领域:
- 计算化学和结构生物学
- 人工智能在科学发现中的作用
- 分子设计和工程学
背景情况:
- 机器学习 (ML) 加快了科学设计,特别是分子,材料和蛋白质的科学设计.
- ML的应用包括药物开发,环境修复和碳捕获.
- 一个核心挑战是平衡ML模型中的新性探索与风险管理.
研究的目的:
- 为了应对在基于机器学习的设计中实现新型属性价值的挑战.
- 探索在ML模型中平衡外推和风险控制的策略.
- 专注于蛋白质设计,同时为基于ML的设计提供更广泛的适用性.
主要方法:
- 对ML模型推断和风险的概念分析.
- 在生成模型中进行受控勘探的框架开发.
- 专注于蛋白质特性优化的案例研究.
主要成果:
- 确定了模型推断和设计失败之间的关键权衡.
- 提出了一个平衡的方法来ML驱动的设计新性和可靠性.
- 对蛋白质设计挑战的证明适用性.
结论:
- 在信任ML模型和控制外推之间取得平衡对于成功的科学设计至关重要.
- 这种方法可以发现具有所需性质的新型蛋白质.
- 讨论的原则广泛适用于各种基于机器学习的设计领域.
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