论文标题
使用自然语言处理方法探索化学空间进行药物发现
Exploring Chemical Space using Natural Language Processing Methodologies for Drug Discovery
论文作者
论文摘要
基于文本的化学物质和蛋白质表示形式可以被视为人类编码的非结构化语言，以描述特定于领域的知识。自然语言处理的进步（NLP）方法学在处理语言的处理中加速了NLP在这些生化实体的文本表示中阐明隐藏知识的应用，然后使用它来构建模型来预测分子特性或设计新颖的分子。这篇综述概述了这些进步对药物发现的影响，并旨在进一步进一步进行药物化学家与计算机科学家之间的对话。
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.
