一个数据驱动的生成策略,以避免奖励黑客在多目标分子设计的奖励
Tatsuya Yoshizawa1,2, Shoichi Ishida1, Tomohiro Sato2
1Graduate School of Medical Life Science, Yokohama City University, 1-7-29, Suehiro-cho, Tsurumi-ku, Yokohama, 230-0045, Kanagawa, Japan.
Nature communications
|March 12, 2025
概括
这项研究引入了使用生成模型进行分子设计的新框架,防止因奖励黑客造成的优化失败. 该方法确保了药物发现和材料科学的可靠的多目标优化.
科学领域:
- 计算化学是一种计算化学.
- 数据驱动的分子设计
- 生成式的人工智能 (GAI) 是一种人工智能.
背景情况:
- 数据驱动的生成模型正在在药物发现和材料科学中彻底改变分子设计.
- 由预测模型外推失败引起的奖励黑客行为构成了重大挑战.
- 由于复杂的可靠性评估,现有的适用性领域 (AD) 方法在多目标优化方面遇到了困难.
研究的目的:
- 为生成模型提出可靠的分子设计框架.
- 在多目标优化过程中防止奖励黑客攻击.
- 为了实现基于用户定义的属性优先级的可靠性级别的自动调整.
主要方法:
- 开发一个新的框架,将生成模型与可靠性估计相结合.
- 在多目标优化中实施战略来管理重叠的适用性领域.
- 使用抗癌药物候选设计作为案例研究的演示.
主要成果:
- 成功设计具有高预测性质和可靠性的分子.
- 通过多目标优化识别潜在的候选药物,包括已批准的药物.
- 验证框架在复杂的分子设计任务中防止奖励黑客的能力.
结论:
- 拟议的框架有效地解决了生成分子设计中的奖励黑客问题.
- 可靠的多目标优化甚至可以通过复杂的属性预测来实现.
- 该框架为各种应用提供可适应的可靠性调整,以药物发现为例.
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