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Updated: Jun 17, 2025

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否定药物发现数据以改善吸收,分布,新陈代谢,分泌和毒性属性预测

Matthew Adrian1, Yunsie Chung1, Alan C Cheng1

  • 1Modeling and Informatics, Merck & Co., Inc., South San Francisco, California 94080, United States.

Journal of chemical information and modeling
|August 7, 2024
PubMed
概括

这项研究引入了一种深度学习的方法,通过解决吸收,分布,新陈代谢,分泌和毒性 (ADMET) 数据中的实验错误来改进药物发现模型. 这种新的方法有效地减少了回归任务中的噪音,提高了预测模型的性能.

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