自动派对:对分子对接结果的机器学习引导视觉检查
Laura Shub1,2, Magdalena Korczynska3, Duncan F Muir1,2
1Department of Pharmaceutical Chemistry, Institute for Neurodegenerative Diseases, University of California, San Francisco, San Francisco, California 94158, United States.
Journal of chemical information and modeling
|July 29, 2025
概括
自动派对通过使用主动学习来训练人类直觉模型来加速虚拟药物查. 这种工具提高了成功率,使药物发现速度更快,更高效.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 生物信息学是一种生物信息学.
背景情况:
- 人类检查至关重要,但在虚拟药物查中却很慢.
- 目前的方法缺乏标准化和一致性.
- 由于大量的分子,可扩展性是一个主要的挑战.
研究的目的:
- 介绍Autoparty,一个容器化工具,以加速人类循环中药物发现.
- 为了使模型能够有效地训练,这些模型从人类的专业知识中学习.
- 为了标准化注释记录,并创建一个持久的数据库.
主要方法:
- 利用本地主动学习来进行药物发现.
- 在信息化用户查询中使用不确定性量化指标.
- 开发一个容器化工具,以简化工作流程.
主要成果:
- 自动党方便了人类在循环中的模型培训.
- 不确定性量化减少了对广泛的人类标签的需求.
- 在一个案例研究中观察到,命中率增加了40%.
结论:
- 自动派对有效地加速了虚拟药物查.
- 该工具通过推断人类直觉来增强模型训练.
- 标准化的注释和本地数据库支持下游应用程序.
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