预测气味化合物的空气 - 鼻分离系数的预测
Junwei Shen1, Yuki Harada1, Shinichiro Nakamura1
1Laboratory for Data Sciences, Priority Organization for Innovation and Excellence, Kumamoto University, 2-39-1, Kurokami, Chuo-ku, Kumamoto 860-8555, Japan.
ACS omega
|July 29, 2024
概括
通过一种新的深层神经网络模型,现在可以预测对气味至关重要的空气粘液分区系数. 该模型使用分子表面电荷密度来理解气味特性,并帮助设计新气味.
科学领域:
- 嗅觉科学是一种嗅觉科学.
- 计算化学是一种计算化学.
- 化学感应是一种化学感应.
背景情况:
- 空气-粘液分割系数是气味与嗅觉受体相互作用的关键.
- 由于粘液的复杂性,这种系数的体内测量具有挑战性.
- 预测模型可以提供对嗅觉感知和气味设计的见解.
研究的目的:
- 开发一个新的空气粘液分区系数的预测模型.
- 利用深层神经网络 (DNN) 来预测气味特性.
- 确定影响嗅觉感知的关键分子特征.
主要方法:
- 使用了深层神经网络 (DNN) 算法.
- 来自COSMO-RS的分子表面电荷密度 (σ-profile) 被用作描述符.
- 该模型在一系列气味化合物上进行了训练和验证.
主要成果:
- 在空气-粘液分区系数和σ-形状之间发现了强烈的相关性.
- DNN模型成功预测了分区系数.
- 可解释的结果确定了调节系数的分子特征.
结论:
- 开发的DNN模型为预测空气-粘液分区系数提供了有价值的工具.
- 分子表面电荷密度是嗅觉感知的一个重要因素.
- 这种方法有助于合理设计具有特定特性的气味剂.
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