通过偏好机器学习提取药物化学直觉
Oh-Hyeon Choung1, Riccardo Vianello1, Marwin Segler2
1Novartis Institutes for Biomedical Research, 4002, Basel, Switzerland.
Nature communications
|November 1, 2023
概括
人工智能学习到等级模型被训练在药物化学家反上,以加快药物发现的领先优化. 这些人工智能工具有助于化合物优先级和新药设计,大大缩短了开发时间.
科学领域:
- 药用化学 医学化学
- 人工智能的人工智能
- 药物发现 药物发现 药物发现
背景情况:
- 药物发现中的优化是复杂的,需要药物化学家的广泛专业知识和时间.
- 对分子性质配置文件的合作决策是一个漫长的,专业知识驱动的过程.
研究的目的:
- 使用AI复制协作领先优化流程.
- 开发人工智能模型,从专家药物化学家反中学习.
- 通过智能自动化加速药物发现时间表.
主要方法:
- 应用人工智能学习到等级的技术.
- 在几个月内利用了诺华公司35名药品化学家的反.
- 在注释的响应数据上训练模型.
主要成果:
- 开发了模仿专家决策在优化中的AI代理.
- 证明了在复合优先排序中学习代理的实用性.
- 展示了动机合理化和偏见的新药设计中的应用.
结论:
- 人工智能驱动的方法可以有效地复制和加速专家驱动的优化.
- 开发的模型和代码是开源的,这有助于更广泛的采用.
- 这项工作为药物发现的耗时方面提供了一个可扩展的解决方案.
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