现实世界的分子分布外:规范和调查
Prudencio Tossou1,2, Cas Wognum1, Michael Craig1
1Valence Labs, Montréal, Québec H2S3G9, Canada.
Journal of chemical information and modeling
|February 1, 2024
概括
在药物发现中,分子分布外 (MOOD) 概括至关重要. 本研究介绍了一个框架和协议,以改善模型性能和不确定性校准,发现强大的表示和不确定性估计是关键.
科学领域:
- 药物发现 药物发现
- 计算化学是一种计算化学.
- 机器学习 机器学习
背景情况:
- 分子分布外 (MOOD) 概括对于药物发现中可靠的AI至关重要.
- 现实世界部署将模型暴露在数据转移中,影响性能和不确定性校准.
- 现有的方法往往无法解释这些与部署相关的数据分布转移.
研究的目的:
- 建立一个严格的框架来研究分子评分中的MOOD概括.
- 量化共变量转移对模型性能和不确定性的影响.
- 提出和验证一个分割协议,以实现现实的评估.
主要方法:
- 通过基于样本距离分布的问题规范来定义MOOD概括.
- 开发了一种新的分离协议,以弥合培训/部署和测试之间的差距.
- 对模型设计,选择和数据集特征进行了全面的调查.
主要成果:
- 交变位的变化可以使性能降低高达60%,不确定性校准降低高达40%.
- 适当的分子表示显著提高MOOD性能.
- 具有内置不确定性估计的算法对于可靠的预测至关重要.
结论:
- 拟议的框架和协议允许对MOOD概括进行有意义的基准测试.
- 模型设计和不确定性估计对于药物发现中可靠的分子评分至关重要.
- 这项工作为开发用于现实世界的应用 robust AI 模型开辟了新的途径.
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