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ADMET-AI:一个机器学习的ADMET平台,用于评估大型化学库

Kyle Swanson^1,2, Parker Walther³, Jeremy Leitz²

¹Department of Computer Science, Stanford University, 353 Jane Stanford Way, Stanford, CA 94305, USA.

Bioinformatics (Oxford, England)

|June 24, 2024

概括

ADMET-AI是一个机器学习平台,为吸收,分布,新陈代谢,分泌和毒性 (ADMET) 属性提供快速和准确的预测,加速药物发现化合物选择.

科学领域:

计算化学和化学信息学
机器学习在药物发现中的作用
药理动力学和毒理学预测预测.

背景情况:

药物发现依赖于庞大的化学图书馆和人工智能来产生新型分子.
实验验证需要根据吸收,分布,新陈代谢,分泌和毒性 (ADMET) 属性对化合物进行过.

研究的目的:

开发一个机器学习平台,ADMET-AI,用于快速准确的ADMET预测.
为过候选药物提供可访问的工具.

主要方法:

开发一个名为ADMET-AI的机器学习平台.
作为一个用户友好的网站和开源Python包的实现.
与现有的ADMET预测工具进行比较.

主要成果:

在TDC ADMET排行榜上,ADMET-AI获得了最高的平均排名.
它是基于网络的最快的ADMET预测器,性能比其他预测器高45%.
对于100万个分子的局部预测可以在3.1小时内实现.

结论:

ADMET-AI为ADMET属性预测提供了显著的速度和准确性改进.
该平台提高了选择有前途的候选药物的效率.
免费可用的Web服务器和Python软件包有助于在药物发现中更广泛地采用.

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