根据健康危害对物质的分类,使用深度神经网络和分子电子密度进行分类
Satnam Singh1,2, Gina Zeh1, Jessica Freiherr1,2
1Department of Sensory Analytics and Technologies, Fraunhofer Institute for Process Engineering and Packaging IVV, Giggenhauser Str. 35, 85354, Freising, Germany.
Journal of cheminformatics
|April 16, 2024
概括
这项研究引入了一种新的深度学习方法,使用3D电子密度来对化品和食品安全的化学物质进行分类. 该方法准确地预测危险材料,帮助监管决策和安全产品开发.
科学领域:
- 计算化学是一种计算化学.
- 机器学习是机器学习.
- 毒理学 毒理学 毒理学
背景情况:
- 评估化品和食品的化学安全至关重要.
- 目前的方法可能无法充分利用分子空间信息.
- 需要新的计算方法来准确地分类危险.
研究的目的:
- 开发和验证使用3D电子密度进行危险物质分类的深度学习模型.
- 探索3D电子密度图在预测化品和食品应用中的化学安全方面的实用性.
- 为化学安全领域的机器学习提供新的3D分子表示.
主要方法:
- 使用半经验分子计算生成3D电子密度立方体.
- 在电子密度数据上训练了一种修改的3D-UNet深度学习模型.
- 使用ECHA (化品) 和EFSA/FDA (食品) 的定制数据集.
主要成果:
- 在将化品物质分类为危险或非危险时,获得了78.1%的准确性.
- 在分类食品物质方面获得了64.1%的准确性.
- 证明掩盖电子密度可以提高分类准确性.
结论:
- 3D电子密度是训练化学安全的深度学习模型的可行表示.
- 这种方法可以指导安全设计产品的开发,并支持监管决策.
- 该方法提供了一种利用分子空间域用于属性预测的新方法.
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