蝙蝠GPT-Chem:化学工程的一个基础大型模型
Yifei Yang1,2,3, Runhan Shi1,2, Zuchao Li4
1School of Computer Science, Shanghai Jiao Tong University, Shanghai 200240, China.
Research (Washington, D.C.)
|September 12, 2025
概括
BatGPT-Chem是一个15亿参数的大型语言模型 (LLM),通过预测化学反应和条件来增强化学中的AI. 这种双语模型推进了逆合成,分子设计和药物发现.
科学领域:
- 化学中的人工智能.
- 大型语言模型 (LLM)
- 化学工程是化学工程的重要组成部分.
背景情况:
- 大型语言模型 (LLM) 显示出人工智能在科学,特别是化学中的潜力,因为它们能够处理顺序数据.
- 目前LLM在化学中的应用有限,很少有专门为化学数据和任务设计的模型.
- 需要先进的AI工具来模拟化学序列和自然语言,用于各种化学应用.
研究的目的:
- 利用LLMs进行化学和自然语言序列的全面建模,以解决各种化学工程任务.
- 引入 BatGPT-Chem,这是一个针对化学应用量身定制的大规模,双语基础模型.
- 通过模拟化学和自然语言之间的信息流来实现跨化学任务的全谱预测.
主要方法:
- 开发了BatGPT-Chem,这是一个15亿参数基础模型,在超过1亿个化学实例上进行了训练.
- 专门的BatGPT-Chem用于5个核心任务:逆合成预测,分子设计,分子描述,产品推断和产量预测.
- 启用双语 (英语/中文) 输入/输出,并集成对反合成反应条件的明确预测.
主要成果:
- 在零射击评估中,BatGPT-Chem展示了最先进的性能,超过了现有的化学LLM和一般模型.
- 在各种任务中实现了卓越的准确性和有效性,特别是在预测反应剂和反应条件方面.
- 展示了强大的概括能力,即使在低数据设置中,也突出了其实际实用性.
结论:
- BatGPT-Chem代表化学专业的LLM取得了重大进展,为现实世界的应用提供了实际的解决方案.
- 该模型预测反应条件的能力明确地解决了自动化逆合成中的关键差距.
- BatGPT-Chem具有强大的潜力,可以支持和加速合成规划,药物发现和材料设计.
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