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相关概念视频

Molecular Models02:00

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Social Foundations of Self II: The Generalized Other01:20

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对于分子基础模型的代币化

Alexius Wadell1,2, Anoushka Bhutani1,2, Venkatasubramanian Viswanathan1,2

  • 1Department of Mechanical Engineering, University of Michigan, Ann Arbor, Michigan 48109, United States.

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概括
此摘要是机器生成的。

分子基础模型推进了科学,但有限的代币化器阻碍了进步. 新的Smirk和Smirk-GPE代币提供了完整的覆盖范围,使化学及其他领域的应用更广泛.

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科学领域:

  • 化学信息学和计算化学
  • 人工智能在科学发现中的作用
  • 分子表示和建模.

背景情况:

  • 基于文本的基础模型对于科学发现至关重要,特别是在分子设计中.
  • 现有的分子基础模型受到封闭词汇标记化器的限制,限制了它们代表全部分子空间的能力.
  • 对35个tokenizers的系统评估揭示了SMILES代表性覆盖面的重大差距.

研究的目的:

  • 系统地评估现有的分子标记化器的覆盖范围.
  • 评估标记器选择对分子性质预测的影响.
  • 开发具有分子表示的全面覆盖的新型代码化器.

主要方法:

  • 评估了35个tokenizers,包括20个化学特异性,用于SMILES的代表覆盖范围.
  • 引入了n-gram语言模型作为评估tokenizer影响的代理.
  • 预先训练和微调的18个罗伯塔式编码器用于分子性质预测.
  • 开发了两个新的标记器,Smirk和Smirk-GPE,完全覆盖了OpenSMILES.

主要成果:

  • 确定了SMILES现有代币化器的覆盖范围中的重大差距.
  • 证明了n-gram语言模型作为tokenizer评估的代理的有效性.
  • 展示了预训练的罗伯塔式编码器用于分子性质预测的能力.
  • 推出了Smirk和Smirk-GPE令牌化器,这些令牌集成了核,电子和几何自由度.

结论:

  • 现有的令牌化器不足以覆盖分子空间,需要开放的词汇方法.
  • 拟议的Smirk和Smirk-GPE令牌提供了完整的OpenSMILES覆盖范围,并允许更广泛的应用.
  • 强调需要化学多样性的基准和化学信息学中的开放词汇建模.