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利用机器学习预测了提高试验提交和决策效率的信心

Davide Bassani¹, Michael Reutlinger¹, Holger Fischer¹

¹Pharmaceutical Research & Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., 4070, Basel, Switzerland.

European journal of medicinal chemistry

|July 10, 2025

概括

机器学习 (ML) 不确定性量化有助于通过确定可靠的预测来帮助制药研究. 这种方法可以将高达25%的化合物排除在药理动力学测定中,从而节省了大量的时间和成本.

科学领域:

药物的发现和开发.
计算化学是一种计算化学.
药理动力学药理动力学

背景情况:

机器学习 (ML) 越来越多地被用于科学研究,因为它能够分析大数据集.
药学研究中的ML应用包括分子性质预测和化合物生成.
将不确定性量化 (UQ) 与ML模型相结合,可以提高预测可靠性.

研究的目的:

描述罗氏使用ML UQ的经验,以改善药理动力学 (PK) 试验提交的决策.
在药物发现中为ML模型建立最佳不确定性值.
通过知情的测试选择来证明节省成本和时间的潜力.

主要方法:

开发ML模型来预测与PK测试相关的分子性质.
实施非附加性分析以设定初始错误接受值.
机器学习和实验科学家之间的合作努力,以定义一个最佳的UQ值.
排除基于ML预测的化合物在定义的置信水平内.

主要成果:

确定了需要提交PK测定的化合物的显著减少.
高达25%的化合物可能会被排除在正常测试提交率之外.
开发的UQ值有效地区分了可靠的预测和不太可靠的预测.

结论:

ML UQ是优化制药研究决策的宝贵工具.
实施ML UQ可以在药物开发中节省大量的时间和成本.
该研究强调了ML UQ在简化PK测试工作流程中的实际应用.

关键词:

药物发现药物发现效率效率是指效率是指效率.在体外测试 (in vitro assays) 中进行测试.机器学习是机器学习.不确定性量化不确定性的量化.

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