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通过预测数据的多任务训练改善ADME预测:来自ASAP-Polaris-OpenADMET盲人挑战的见解

Long-Hung Dinh Pham¹, Minh-Tri Le^2,3,4, Khac-Minh Thai^2,3,4

¹Department of Chemistry, Imperial College London, W12 0BZ London, U.K.

Journal of chemical information and modeling

|December 22, 2025

概括

利用从行业模型中预测的标签,通过图形神经网络 (GNN) 进行转移学习,可以提高吸收,分布,新陈代谢和排泄 (ADME) 的预测. 这种方法通过使用有限的实验数据来提高模型性能.

科学领域:

计算化学是一种计算化学.
机器学习在药物发现中的作用
生物信息学是一种生物信息学.

背景情况:

吸收,分布,新陈代谢和分泌 (ADME) 特性对于药物开发的成功至关重要.
对ADME预测的有限的公共数据阻碍了机器学习模型的开发.
行业发布的预测标签为培训数据提供了新的机会.

研究的目的:

开发一种改进的机器学习方法,用于预测分子ADME配置文件.
为了利用转移学习和图形神经网络 (GNN) 进行ADME预测.
使用公开可用的预测标签用于模型预训练和微调.

主要方法:

使用多任务图形神经网络 (GNN) 采用转移学习.
丰富的表示学习和专注于对实验数据进行微调.
探索一种预训练策略,整合实验和预测标签.

主要成果:

在ASAP-Polaris-OpenADMET抗病毒ADME挑战2025中取得了竞争性结果.
在聚合平均绝对误差 (MAE) 上排名第四,在聚合Pearson R.上排名第二.
竞赛后的优化超越了在没有专有数据的MAE中排名第三的参赛者.

结论:

使用GNN进行转移学习对于ADME预测是有效的,尤其是在有限的实验数据的情况下.
整合预测和实验标签显示了预训策略的前景.
该研究表明,在现实世界药物发现任务中使用预测标签的可行方法.

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