使用基于变压器的模型预测NADES的形成
Lucas B Ayres1, Federico J V Gomez2, Maria Fernanda Silva2
1Department of Chemistry, Clemson University, 211 S. Palmetto Blvd, Clemson, SC, 29634, USA.
Scientific reports
|February 22, 2024
概括
机器学习预测了用于更绿色化学的新型天然深度环保溶剂 (NADES). 这种方法加速了新型,稳定的NADES混合物的发现,促进了制药和农业应用.
科学领域:
- 绿色化学 绿色化学
- 计算化学计算化学
- 材料科学 材料科学 材料科学
背景情况:
- 在各种行业中,天然深层欧特溶剂 (NADES) 为传统有机溶剂提供了可持续的替代品.
- 目前的NADES开发严重依赖实证方法,限制了新混合物的发现.
- 需要采用数据驱动的方法来克服传统NADES发现的局限性.
研究的目的:
- 开发一种机器学习模型,用于预测稳定的NADES的形成.
- 加速发现和设计具有所需性质的新NADES.
- 在NADES开发中减少对经验方法的依赖.
主要方法:
- 使用基于变压器的神经网络,利用自然语言处理技术.
- 该模型在一般化学数据 (SMILES) 上进行了预训练,并为稳定的NADES形成的二元分类进行了微调.
- 该方法适应了语言学习策略,以在有限的数据和计算资源的情况下有效识别模式.
主要成果:
- 该算法成功地从自然化合物数据库中预测了337种新的稳定菌混合物.
- 该模型表明,它能够预测纳德斯的成分和摩尔比率,其中包含了新型分子.
- 验证包括之前报告的NADES和开发新的含伊布洛芬溶剂.
结论:
- 拟议的机器学习策略显著提高了NADES的预测和发现.
- 这种方法有可能彻底改变NADES查,并简化生物活性化合物在液体配方中的使用.
- 该方法为推进绿色化学和制药行业提供了一个强大的工具.
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