大型语言模型能理解分子吗?
Shaghayegh Sadeghi1, Alan Bui2, Ali Forooghi2
1School of Computer Science, Univeristy of Windsor, Sunset Ave, Windsor, ON, N9B 3P4, Canada. sadeghi3@uwindsor.ca.
BMC bioinformatics
|June 26, 2024
概括
像LLaMA这样的大型语言模型 (LLM) 在从简化分子输入线输入系统 (SMILES) 字符串中生成分子嵌入时表现强. 基于LLaMA的嵌入性能优于GPT,并在药物相互作用预测任务中表现出色.
科学领域:
- 化学信息学 化学信息学
- 人工智能的人工智能
- 计算化学计算化学
背景情况:
- 大型语言模型 (LLM) 正在成为化学信息学的强大工具.
- 简化分子输入线输入系统 (SMILES) 是代表化学结构的关键格式.
- 在下游任务中,LLM可以将SMILES字符串解码为有意义的矢量表示.
研究的目的:
- 评估生成预训练变压器 (GPT) 和大型语言模型超人工智能 (LLaMA) 在生成 SMILES 嵌入中的性能.
- 将基于LLM的嵌入与SMILES上的传统预训练模型进行比较.
- 评估这些嵌入在分子性质和药物相互作用 (DDI) 预测中的实用性.
主要方法:
- 研究了GPT和LLaMA模型的性能.
- 使用两种LLM生成SMILES嵌入.
- 评估了对分子性质预测和DDI预测任务的嵌入.
- 基于LLM的嵌入与预先训练的SMILES模型进行了比较.
主要成果:
- 在分子性质和DDI预测方面,LLaMA生成的SMILES嵌入优于GPT生成的嵌入.
- 在分子预测中,LLaMA嵌入实现了与预先训练的模型可比的性能.
- 在DDI预测任务中,LLaMA嵌入超越了预先训练的模型.
结论:
- 实际上,LLM显示出产生有效分子嵌入的巨大潜力.
- 对于分子表示任务,LLaMA特别有前途.
- 对分子嵌入的LLM进行进一步的研究是有必要的,以弥合人工智能和化学信息学之间的差距.
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