数据库,预测和抗菌研究的性基于大型语言模型
Yi He1, Yilin Gao1, Kaifeng Liu1
1Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, School of Life Sciences, Jilin University, 2699 Qianjin Street, Changchun, 130012, China.
Computers in biology and medicine
|November 12, 2024
概括
一种新的计算方法预测了分子和抗菌活性. 这项研究揭示了许多性分子也具有抗菌性质,为食品科学和医学打开了大门.
科学领域:
- 食品科学 食品科学 食品科学
- 计算化学计算化学
- 感官科学 感官科学
背景情况:
- 度,一个关键的味觉,影响食品的质量 (例如,葡萄酒,茶),但传统的预测方法是昂贵的.
- 收缩性和抗菌性质之间的联系在很大程度上未经研究,尽管有潜在的应用.
研究的目的:
- 开发一个计算框架来预测分子和抗菌活性.
- 建立了第一个关于凝剂分子的全面数据库.
- 为了探索收缩性和抗菌性质之间的关系.
主要方法:
- 创建了一个238个分子的数据库来预测收缩性.
- 使用机器学习,深度学习和大型语言模型开发了一个基于连接体的预测 (LBP) 框架.
- 使用电子舌头测量验证了预测,并开发了分子和的特定预测器.
主要成果:
- 实现了0.95准确度和0.90 AUC用于收缩性预测.
- 开发了抗菌预测剂,分子准确度为0.92,类0.88.
- 发现51%的凝剂分子表现出抗菌特性.
结论:
- 开发的计算方法准确地预测了凝聚力和抗菌活性.
- 性分子和具有抗菌性质的分子之间存在显著的重叠.
- AstringentPD和ABPD网络服务器提供了可访问的工具,用于进一步研究食品科学和药物化学.
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