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一种基于NLP的技术,可以从药物SMILES中提取有意义的特征

Rahul Sharma¹, Ehsan Saghapour¹, Jake Y Chen¹

¹Informatics Institute, School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, USA.

|March 8, 2024

概括

这项研究引入了一种新的自然语言处理 (NLP) 方法,用于从药物分子结构中提取特征,其表示为SMILES符号. 这种方法增强了机器学习模型,用于个性化药物查,并加速了药物发现.

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.
机器学习机器学习

背景情况:

自然语言处理 (NLP) 模型的序列数据,类似于药物分子的SMILES符号.
在SMILES中的特殊字符具有特定的含义,对于药物结构分析至关重要.
现有的方法可能无法充分利用SMILES的顺序性质来提取特征.

研究的目的:

开发一种基于NLP的新方法,从药物SMILES标记中提取可解释的序列和特征.
将NLP衍生特征与传统的摩根指纹位向量进行比较.
在个性化药物查 (PSD) 案例研究中验证该方法的有效性.

主要方法:

利用NLP中的N-gram来提取药物SMILES中的序列特征.
采用基于UMAP的嵌入来比较NLP特征与摩根指纹.
集成基于NLP的功能,用于机器学习模型的基因表达和疾病表型数据.

主要成果:

基于NLP的功能对于PSD来说是稀疏而有效的.
结合的功能导致了改进的机器学习模型,用于个性化药物查.
通过两个成功的个性化药物查案例研究证明了它的实用性.

结论:

这种基于NLP的新方法提供了一种新的方法来分析SMILES标记中的药物分子结构.
这种方法可以加速药物发现和开发工作.
开发的方法可以作为一个可访问的Python库.

关键词:

化学化学化学计算机科学计算机科学制药科学制药科学制药科学

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