特性工程嵌入用于分类分子数据的嵌入
Claudio Jardim1, Alta de Waal1, Inger Fabris-Rotelli1
1University of Pretoria, Pretoria, South Africa.
Computational biology and chemistry
|May 25, 2024
概括
本研究介绍了一种使用化学文本数据的自然语言处理 (NLP) 进行分子分类的更快,更高效的计算方法. 这种方法避免了漫长的深度学习培训,为药物发现和生物信息学提供了强大的替代方案.
科学领域:
- 计算化学是一种计算化学.
- 生物信息学是一种生物信息学.
- 机器学习是机器学习.
背景情况:
- 分子分类对于药物发现至关重要.
- 目前使用结构数据的深度学习方法是计算密集的,训练很慢.
- 分子数据存在于结构和序列/文本信息.
研究的目的:
- 开发一种计算效率高,可重复的分子分类方法.
- 探索自然语言处理 (NLP) 技术用于特征工程分子文本数据的使用.
- 为机器学习模型创建快速,化学文本数据依赖的分子嵌入.
主要方法:
- 使用NLP技术的特征工程:计数矢量化,术语频率逆向文档频率 (TF-IDF),word2vec和隐藏的迪里克莱特分配 (LDA).
- 这些技术应用于FASTA序列数据和简化分子输入线输入规范 (SMILES) 数据.
- 在机器学习分类任务中评估生成的嵌入式的性能.
主要成果:
- 基于NLP的特征工程创造了强大的和可重复的分子嵌入.
- 开发的嵌入式很快实现,并且完全依赖于化学文本数据.
- 这些嵌入式在应用到FASTA和SMILES数据时,在分子分类任务中表现出色.
结论:
- 自然语言处理技术为分子分类提供了传统深度学习的有效替代方案.
- 拟议的方法提供了一种快速,计算上便宜,有效的方法,用于从文本数据中生成分子嵌入.
- 这种方法对加速药物发现和其他需要分子分类的应用具有重大意义.
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