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Updated: Feb 8, 2026
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盲目的挑战让我们看看预测模型的前进道路
John D Chodera1, W Patrick Walters2, Sriram Kosuri3
1Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States.
Journal of chemical information and modeling
|February 6, 2026
概括
人工智能和机器学习模型有望加速药物发现. 盲目的挑战对于准确评估预测性能和克服计算药物设计中的准确性障碍至关重要.
科学领域:
- 计算化学是一种计算化学.
- 药物发现信息学 药物发现信息学
- 医学中的人工智能
背景情况:
- 人工智能/ML模型正在快速推进药物发现,但它们的预测准确性往往被夸大.
- 不同的分子表示用于目标上 (结构) 和目标外/ADMET (隐含) 预测.
- 现有的回顾性基准可能不准确地反映现实世界的预测成功.
研究的目的:
- 解决需要对AI/ML模型在药物发现中的性能进行现实的评估的需求.
- 突出目前用于分子性质预测的基准测试方法的局限性.
- 强调前性,标准化的比较的重要性.
主要方法:
- 讨论了回顾性基准的作用及其局限性.
- 突出了盲目的挑战的重要性 (例如,OpenADMET × ASAP × PolarisHub挑战).
- 强调需要对预测模型进行标准化,前性比较.
主要成果:
- 追溯基准可以对模型性能产生误导性.
- 盲目的挑战为预测能力提供了更现实的评估.
- 社区主导的倡议和开放数据对于进步至关重要.
结论:
- 标准化,前性评估对于验证药物发现中的AI/ML模型至关重要.
- 盲目的挑战是识别和克服准确性障碍的关键.
- 对数据和社区挑战的持续投资将加速人工智能驱动的药物发现.
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