基于变压器的化学SMILES表示模型:全面的文献综述
Medard Edmund Mswahili1, Young-Seob Jeong1
1Chungbuk National University, Department of Computer Engineering, Cheongju, 28644, South Korea.
Heliyon
|December 6, 2024
概括
预训练的化学语言模型 (CLMs) 利用变压器架构来分析化学数据,使分子性质预测和新药设计的进步成为可能. 这些模型擅长解释复杂的SMILES字符串,解锁化学信息学的新可能性.
科学领域:
- 化学信息学和生物信息学
- 自然语言处理 (NLP) 是一种自然语言处理.
- 机器学习 (ML) 是指机器学习.
- 深度学习 (DL) 是指深度学习.
背景情况:
- 化学语言模型 (CLM) 正在获得吸引力,反映了NLP的成功.
- 庞大的未标记的化学数据需要具有先进推理的CLM.
- 分子图和描述符是传统的ML表示; 变压器提供了一个新的范式.
研究的目的:
- 在化学信息学中审查基于变压器的最先进的CLM.
- 分析CLM在新设计和物业预测等任务中的功能.
- 确定这些模型的当前局限性,挑战和优势.
主要方法:
- 对化学数据应用的基于变压器的语言模型 (例如BERT,GPT变体) 的审查.
- 分析处理化学物质SMILES字符串的模型,以获取上下文信息.
- 在化学信息学下游任务中评估CLM.
主要成果:
- 基于变压器的CLM在NLP启发的化学任务中表现出显著的表现.
- 这些模型有效地从SMILES序列中学习上下文信息.
- 现有的CLM显示出分子性质预测和de novo设计的前景.
结论:
- 基于变压器的CLM代表了化学信息学的强大进步.
- 需要进一步的研究来解决局限性和探索未来的机会.
- 在药物发现和分子设计中,CLM的潜力是巨大的.
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