从NLP视角梳理化学分子的数字表示方法，助力跨领域AI研究。
Molecular Representations for AI in Chemistry and Materials Science: An NLP Perspective
- 借鉴NLP思想设计可机器读取的分子表示
- 总结主流分子表示法及其在化学/材料中的应用
- 适合初涉化学与AI交叉领域的研究人员参考
近年来，深度学习在多个领域迅速发展，逐渐进入自然科学。这一趋势催生了对既适用于机器处理又便于科学理解的分子表示方法的需求。多年来，已构建出多种化学分子表示形式，随着计算机技术进步和对分子复杂性认知加深，新表示方法仍在持续涌现。本文综述了受自然语言处理（NLP）启发、广泛应用于化学信息学的典型数字分子表示方法，并讨论了基于这些表示的若干重要人工智能应用。本文旨在从NLP研究者视角，为人工智能在化学与材料科学中的应用提供关键结构表示的参考指南，是面向缺乏化学表示经验的研究人员开展交叉领域研究的实用工具。
原文摘要 · Abstract (English)
Deep learning, a subfield of machine learning, has gained importance in various application areas in recent years. Its growing popularity has led it to enter the natural sciences as well. This has created the need for molecular representations that are both machine-readable and understandable to scientists from different fields. Over the years, many chemical molecular representations have been constructed, and new ones continue to be developed as computer technology advances and knowledge of molecular complexity increases. This paper presents some of the most popular digital molecular representations inspired by natural language processing (NLP) and used in chemical informatics. In addition, the paper discusses some notable AI-based applications that use these representations. This paper aims to provide a guide to structural representations that are important for the application of AI in chemistry and materials science from the perspective of an NLP researcher. This review is a reference tool for researchers with little experience working with chemical representations who wish to work on projects at the interface of these fields.
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