探索苦味和甜味:大型语言模型在分子味道预测中的应用
Renxiu Song1, Kaifeng Liu1, Qizheng He1
1Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, School of Life Science, Jilin University, 2699 Qianjin Street, Changchun 130012, China.
Journal of chemical information and modeling
|May 7, 2024
概括
大型语言模型 (LLM) 可以预测分子味道,有助于开发新的甜味剂. 在识别苦味和甜味化合物方面,GPT-4的准确度达到了86%,为更安全的替代品铺平了道路.
科学领域:
- 计算化学是一种计算化学.
- 人工智能在药物发现中的作用
- 感官科学是一种感官科学.
背景情况:
- 味觉对人类体验至关重要.
- 由于对阿斯巴甜的安全性存在担忧,因此需要开发新的甜味剂.
- 了解苦甜味调节对于食品和制药行业至关重要.
研究的目的:
- 利用大型语言模型 (LLM) 来预测分子味道特征,专注于苦甜二分法.
- 开发具有针对性的味道特征的新型分子化合物.
- 评估新产生的味道修饰剂的可行性和安全性.
主要方法:
- 使用GPT-3.5和GPT-4进行味道预测,采用随机和支架数据分割.
- 采用ChatGPT来提取与味道有关的关键分子特征.
- 使用分子对接,分子动力学,ADMET毒性测试和DeepSA合成可行性验证生成的化合物.
主要成果:
- 在脚手架分区下,GPT-4显示出更高的预测准确度 (86%).
- 成功地产生了具有明显苦和甜的特征的新型分子化合物.
- 生成的化合物在验证和可行性测试中显示出有希望的结果.
结论:
- 在预测分子味道特性方面,LLM显示出显著的潜力.
- 这种方法可以加速发现安全有效的味道修饰剂.
- 这项研究强调了人工智能在推进健康和化学科学方面的实用性.
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