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Standardizing chemical compounds with language models
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DOI：10.1088/2632-2153/ace878.png)
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With the growing amount of chemical data stored digitally, it has become crucial to represent chemical compounds accurately and consistently. Harmonized representations facilitate the extraction of insightful information from datasets, and are advantageous for machine learning applications. To achieve consistent representations throughout datasets, one relies on molecule standardization, which is typically accomplished using rule-based algorithms that modify descriptions of functional groups. Here, we present the first deep-learning model for molecular standardization. We enable custom standardization schemes based solely on data, which, as additional benefit, support standardization options that are difficult to encode into rules. Our model achieves over 98%
Keyword:
chemoinformatics
molecule standardization
molecular transformer
compound representation
natural language processing
chemical datasets
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被引数:
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