利用生物分子和自然语言通过多模态学习：一篇综述
Leveraging Biomolecule and Natural Language through Multi-Modal Learning: A Survey
AI总结：
本文综述了生物分子与自然语言多模态学习的最新进展，探讨了技术表示、多模态整合方法、应用实例及未来研究方向。
摘要：
生物分子建模与自然语言（BL）的整合在人工智能、化学和生物学的交汇处已成为一个有前途的跨学科领域。这种方法利用文本数据源中生物分子丰富的、多方面的描述来增强我们对基本原理的理解，并启用下游计算任务，如生物分子属性预测。将自然语言中细腻的叙述与通过各种分子建模技术描述的生物分子的结构和功能细节相结合，为全面表示和分析生物分子开辟了新途径。通过将围绕生物分子的上下文语言数据纳入其建模中，BL旨在捕捉涵盖语言传达的象征性特征以及定量结构特征的完整视图。在本综述中，我们对生物分子和自然语言交叉建模最近取得的进步进行了详尽分析。(1) 我们首先概述了生物分子所使用的技术表示，包括序列、2D图和3D结构。(2) 然后我们深入探讨了有效整合语言和分子数据源的原理和关键目标。(3) 接着我们调查了该发展领域迄今为止实现的实用应用。(4) 我们还汇总并总结了可用的资源和数据集，以促进未来的工作。(5) 展望未来，我们确定了一些值得进一步探索和投资的研究方向，以继续推动该领域的发展。相关资源和内容正在https://github.com/QizhiPei/Awesome-Biomolecule-Language-Cross-Modeling上更新。
英文摘要：
The integration of biomolecular modeling with natural language (BL) has emerged as a promising interdisciplinary area at the intersection of artificial intelligence, chemistry and biology. This approach leverages the rich, multifaceted descriptions of biomolecules contained within textual data sources to enhance our fundamental understanding and enable downstream computational tasks such as biomolecule property prediction. The fusion of the nuanced narratives expressed through natural language with the structural and functional specifics of biomolecules described via various molecular modeling techniques opens new avenues for comprehensively representing and analyzing biomolecules. By incorporating the contextual language data that surrounds biomolecules into their modeling, BL aims to capture a holistic view encompassing both the symbolic qualities conveyed through language as well as quantitative structural characteristics. In this review, we provide an extensive analysis of recent advancements achieved through cross modeling of biomolecules and natural language. (1) We begin by outlining the technical representations of biomolecules employed, including sequences, 2D graphs, and 3D structures. (2) We then examine in depth the rationale and key objectives underlying effective multi-modal integration of language and molecular data sources. (3) We subsequently survey the practical applications enabled to date in this developing research area. (4) We also compile and summarize the available resources and datasets to facilitate future work. (5) Looking ahead, we identify several promising research directions worthy of further exploration and investment to continue advancing the field. The related resources and contents are updating in https://github.com/QizhiPei/Awesome-Biomolecule-Language-Cross-Modeling.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
