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Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
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In an NMR sample, precise measurement of the absolute absorption frequencies of nuclei is difficult. A standard internal reference compound is added, and the frequency difference between the reference signal and sample signals is measured.
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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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微调化学文本挖掘的大型语言模型.

Wei Zhang1,2, Qinggong Wang3, Xiangtai Kong1,2

  • 1Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences 555 Zuchongzhi Road Shanghai 201203 China myzheng@simm.ac.cn fuzunyun@simm.ac.cn.

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精心调整的大型语言模型 (LLM) 在五个复杂任务中显著提高了化学文本挖掘的准确性. 这些先进的LLM减少了对广泛的快速工程的需求,为化学自动化数据采集提供了一个强大的新工具.

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科学领域:

  • 计算化学是一种计算化学.
  • 自然语言处理自然语言处理.
  • 化学信息学 化学信息学

背景情况:

  • 从化学文献中提取知识是具有挑战性的,因为复杂的语言.
  • 对于实验和计算化学家来说,自动数据采集至关重要.
  • 大型语言模型 (LLM) 显示了化学文本挖掘的潜力.

研究的目的:

  • 探索微调的LLM在复杂的化学文本挖掘任务中的有效性.
  • 将微调的LLM与快速工程模型 (ChatGPT,GPT-4) 和其他开源LLM进行比较.
  • 用最少的注释数据来评估LLM的性能.

主要方法:

  • 精细调整各种LLM,包括ChatGPT (GPT-3.5-turbo),GPT-4,Mistral,Llama3,Llama2,T5和BART. 这三种类型的LLM.
  • 评估了五项化学文本挖掘任务:化合物实体识别,反应角色标记,MOF合成提取,NMR数据提取和反应到作用序列转换.
  • 微调模型与使用有限的注释数据的提示工程模型进行比较.

主要成果:

  • 微调的ChatGPT模型在所有评估任务中实现了高精度 (69%-95%).
  • 精心调整的LLM超越了使用任务适应性预训练和更大的域内数据集的模型.
  • 微调的Mistral和Llama3表现出了竞争力的表现.
  • 观察到快速工程工作的显著减少.

结论:

  • 精心调整的LLM在化学知识提取方面非常有效,即使数据最小.
  • 这些模型为自动数据采集提供了多功能,强大和低代码的解决方案.
  • 利用精心调整的LLM可以彻底改变化学信息学领域.