探索数据驱动的化学 SMILES 代币化方法,以确定关键的蛋白质-连接体结合部分
Asu Busra Temizer1,2, Gökçe Uludoğan3, Rıza Özçelik3
1Department of Pharmaceutical Chemistry, Faculty of Pharmacy, İstanbul University, İstanbul, Turkey.
Molecular informatics
|January 10, 2024
概括
这项研究探讨了用于药物发现的分子序列中的"化学单词". 使用自然语言处理技术识别的关键化学词是特定于蛋白质标的,并代表重要的药理.
科学领域:
- 计算机化药物发现.
- 化学信息学 化学信息学
- 生物信息学是一种生物信息学.
背景情况:
- 机器学习模型经常代表分子作为药物发现任务的序列.
- 基于序列的模型将分子分割成用于自然语言处理 (NLP) 应用的"化学单词".
- 这些"化学词"的化学含义仍然未被充分探索.
研究的目的:
- 研究从分子序列中衍生的"化学词"的化学特征和意义.
- 为了比较不同的数据驱动的标记化技术来识别化学单词.
- 阐明这些化学单词在蛋白质 - 配体结合中的作用.
主要方法:
- 在分子序列 (SMILES) 上使用字节对编码,WordPiece和Unigram标记化.
- 开发了一种语言灵感的管道,使用tf-idf权重来识别高亲缘关系联体中的关键化学单词.
- 分析了多个蛋白质 - 配体亲和力数据集,并进行了针对特定目标的案例研究.
主要成果:
- 在不同的子词令牌化算法中识别了类似的关键化学词,尽管词汇的变化.
- 证明了关键化学词是特定于蛋白质标的.
- 与已知的药理和功能组相关的关键化学词相关联.
结论:
- 这项研究阐明了机器学习识别的"化学词"的化学特性.
- 识别的化学关键词为分子识别和结合提供了洞察力.
- 这种方法可以帮助识别重要的化学成分,用于计算药物发现.
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