通过SMILES中的原子代币化,提高化学语言模型结果的质量
Umit V Ucak1, Islambek Ashyrmamatov2, Juyong Lee3
1Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea.
Journal of cheminformatics
|May 29, 2023
概括
传统的分子表示具有局限性. 我们的新原子-in-SMILES代币化提高了AI模型在化学任务中的准确性,提供了更好的预测质量和化学准确的代币.
科学领域:
- 计算化学是一种计算化学.
- 化学领域的人工智能
- 化学数据的自然语言处理.
背景情况:
- 代币化对于自然语言处理 (NLP) 和化学数据分析至关重要.
- 传统的SMILES代币化方法存在局限性,无法准确地表示分子结构.
- 这可能会对AI模型在化学任务中的性能产生负面影响.
研究的目的:
- 为了解决传统的SMILES代币化的局限性.
- 引入和验证新的原子在SMILES代币化计划.
- 为了提高AI驱动化学应用的分子表示的质量.
主要方法:
- 开发了SMILES中的原子代币化方案.
- 在多个化学翻译任务中进行评估.
- 评估分子属性预测准确度的评估.
- 在生成的 SMILES 序列中分析令牌退化和重复.
主要成果:
- 原子在SMILES计划有效地消除了通用SMILES令牌中的模两可.
- 在化学任务的预测准确性方面取得了显著的改进.
- 在生成更高质量的SMILES序列方面,超过了现有的代币化和表示方案.
- 量化和分析了代币退化,显示了对预测质量的不利影响减少.
结论:
- 适当的代币化,如原子在SMILES中,显著影响化学中的AI模型性能.
- Atom-in-SMILES为分子性质预测和生成提供了化学精确和定制的令牌.
- 这种方法在基于AI的化学研究和开发中具有很大的应用潜力.
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