基于深度学习的自动动作从结构化化学合成过程中提取出来
Mantas Vaškevičius1,2, Jurgita Kapočiūtė-Dzikienė1, Arnas Vaškevičius3
1Department of Applied Informatics, Vytautas Magnus University, Kaunas, Lithuania.
PeerJ. Computer science
|September 14, 2023
概括
本研究介绍了一种机器学习管道,可以从专利中提取化学合成动作,为AI应用程序创建结构化数据. 这促进了化学中的自然语言处理,简化了反应分析和优化.
科学领域:
- 化学 化学 化学
- 自然语言处理自然语言处理.
- 机器学习 机器学习
背景情况:
- 化学合成程序通常在专利中以非结构化文本进行描述.
- 从这些程序中提取可操作的信息是具有挑战性的,但对于推进化学研究至关重要.
研究的目的:
- 开发和验证一种机器学习方法,用于从化学合成过程中提取结构化的动作.
- 通过将实验过程转化为可用的格式,弥合化学和自然语言处理 (NLP) 之间的差距.
主要方法:
- 开发了一种结合机器学习算法和脚本的管道,用于处理USPTO和EPO专利.
- 关键任务包括对化学程序的专利段落进行分类,并将句子转换为结构化格式.
- 他们使用了人工神经网络,包括长短期记忆 (LSTM),双向LSTM,变压器和微调的T5.
主要成果:
- 双向的LSTM在分类化学过程段落中实现了0.939的高准确性.
- 变压器模型在第二个任务中表现出卓越的表现,在结构化输出方面获得0.951的BLEU分数.
- 该管道成功地将非结构化合成程序转化为结构化,可操作的数据集.
结论:
- 开发的方法有效地从化学合成程序中提取结构化操作,创建一个有价值的数据集.
- 这促进了人工智能驱动的方法来简化合成途径,预测结果和优化反应条件.
- 结构化数据集提高了研究人员在化学合成程序中的信息的可访问性和实用性.
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