Text-based representations of chemicals and proteins can be thought of as unstructured languages codified by humans to describe domain-specific knowledge. Advances in natural language processing (NLP) methodologies in the processing of spoken languages accelerated the application of NLP to elucidate hidden knowledge in textual representations of these biochemical entities and then use it to construct models to predict molecular properties or to design novel molecules. This review outlines the impact made by these advances on drug discovery and aims to further the dialogue between medicinal chemists and computer scientists.

中文翻译：

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使用自然语言处理方法探索化学空间进行药物发现。

化学物质和蛋白质的基于文本的表示形式可以被认为是人类编码的非结构化语言，用于描述特定领域的知识。口语处理中自然语言处理（NLP）方法学的进步加速了NLP的应用，以阐明这些生化实体的文本表示形式中的隐藏知识，然后将其用于构建模型以预测分子特性或设计新分子。这篇综述概述了这些进展对药物发现的影响，并旨在促进药物化学家与计算机科学家之间的对话。