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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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Synthesis of new DNA molecules is carried out by the enzyme DNA polymerase, which adds nucleotides on the daughter strand complementary to the template DNA strand. DNA polymerase has a higher affinity to add the correct base and ensures fidelity during DNA replication. Furthermore,  it exhibits proofreading activity during replication, using an exonuclease domain that cuts off incorrect nucleotides from the nascent DNA strand.
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以语法为导向的SMILES标准化使用了TokenSMILES.

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  • 1Departamento de Física Aplicada, Centro de Investigación y de Estudios Avanzados Unidad Mérida, km 6 Antigua Carretera a Progreso, Apdo. Postal 73, Cordemex 97310 Mérida Yucatán Mexico.

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概括

标记SMILES通过创建简化分子输入线输入系统 (SMILES) 字符串的语法框架来标准化化学符号. 这种方法显著减少了冗余,并提高了化学信息学应用的机器解释性.

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科学领域:

  • 计算化学计算化学
  • 化学信息学 化学信息学
  • 语言学原则 语言学原则

背景情况:

  • 简化分子输入线输入系统 (SMILES) 符号提出了冗余性挑战,其中多个字符串可以表示相同的分子.
  • 这种冗余性使计算化学和化学信息学任务复杂化,阻碍了高效的数据处理和分析.

研究的目的:

  • 引入TokenSMILES,这是一个新的语法框架,旨在标准化SMILES标记.
  • 通过将字符串转换为结构化,无上下文的句子来减轻SMILES冗余.
  • 为了实现可控生成和操作有效的SMILES字符串,增强语法和语义一致性.

主要方法:

  • 开发了TokenSMILES,一个框架,将五个语法约束 (例如,分支限制,平衡括号) 应用于SMILES字符串.
  • 利用语义解析规则来确保价值和八位合规性.
  • 在开源的 SmilX 工具中实现了 TokenSMILES,用于生成标准化的 SMILES.

主要成果:

  • 在保持化学有效性的同时,TokenSMILES 显著减少了 SMILES 对基的冗余.
  • 结合TokenSMILES的SmilX工具生成有效的SMILES,其准确度与现有的低缺水分子 (HDI ≤ 4) 的方法相美.
  • 该框架通过诸如键插入,循环化和异构原子替代等修改,证明了超出基的适用性.

结论:

  • 标记SMILES将SMILES语法正式化为标准化,机器可解释的形式,解决化学信息学中的一个关键挑战.
  • 该框架为药物发现,材料设计和机器学习中的应用提供了系统的化学空间探索.
  • 对于高度不和的系统,需要进一步开发,强调动态可行性检查的重要性.