基准测试用于现实世界药物发现应用的化合物活性预测
Tingzhong Tian1, Shuya Li1, Ziting Zhang2,3
1Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China.
Communications chemistry
|June 4, 2024
概括
一个新的基准,CARA,评估计算药物发现模型. 它解决了现实世界的数据偏差,以更准确地评估跨多种测试的复合活性预测性能.
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:
- 准确预测化合物活性对于早期药物发现至关重要.
- 数据驱动的计算方法显示出希望,但缺乏强大的评估基准.
- 现有的基准可能不反映现实世界的数据复杂性,导致业绩高估.
研究的目的:
- 引入现实应用的复合活动基准 (CARA).
- 为计算复合活性预测模型提供全面的评估框架.
- 为了解决现实世界活动数据中的偏差,并改进模型评估.
主要方法:
- 通过区分测试类型和设计特定的火车测试分割,开发了CARA.
- 选择适当的评估指标来处理有偏见的数据分布.
- 在CARA基准上评估了当前的预测模型和少量学习策略.
主要成果:
- 当前的化合物活性预测模型在不同的测试中显示了可变的性能.
- 卡拉基准有助于识别模型性能中的局限性和高估值.
- 短暂训练策略表现出取决于任务的性能变化.
结论:
- 卡拉为开发和验证化合物活动预测模型提供了高质量的数据集.
- 该基准有助于对药物发现中的计算方法进行更现实的评估.
- 调查结果表明,将数据驱动模型应用于现实世界药物发现挑战的改进.
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