[论文解读] Leveraging Biomolecule and Natural Language through Multi-Modal Learning: A Survey

Qizhi Pei, Zhou, Zhimeng+7 more|arXiv (Cornell University)|Mar 3, 2024

Machine Learning in Bioinformatics被引用 5

一句话总结

对跨生物分子-语言建模的全面综述，详细介绍表示、学习框架、任务、数据集和未来方向。

ABSTRACT

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.

研究动机与目标

• 考察生物分子表示（1D序列、2D图结构、3D结构）及其在BL建模中的作用。
• 考察将语言与生物分子整合的理论基础、目标及核心学习框架。
• 整理当前在性质预测、生成和检索中的应用。
• 总结可用资源、数据集和基准，以加速未来工作。
• 识别尚存挑战与有前景的方向，以推动BL研究。

提出的方法

• 对生物分子表示进行分类与分析，包括序列、图和结构。
• 综述机器学习框架，如基于GPT的预训练和多流架构。
• 讨论BL的表示学习策略、训练任务及学习目标。
• 回顾在预测、生成和信息检索方面的实际应用。
• 整理数据集、模型和基准，并概述未来研究方向。

实验结果

研究问题

• RQ1 在跨模态生物分子-语言（BL）建模中，常见的生物分子表示有哪些？
• RQ2 哪些学习框架与表示策略能够有效地将语言与生物分子数据整合？
• RQ3 通过BL模型已经展示了哪些应用，及其性能趋势如何？
• RQ4 哪些资源、数据集和基准当前支持BL研究？
• RQ5 未来BL工作面临的主要挑战和有前景的方向是什么？

主要发现

• 跨模态BL建模结合文本、分子和蛋白质数据，为下游任务创建更丰富的表示。
• 基础模型如MolT5和BioT5在分子与文本之间展示出强大的检索与生成能力。
• 架构涵盖编码器-仅、解码器-仅、编码器-解码器，以及双流/多流设计，包括PaLM-E风格框架。
• 存在一组不断增长的数据集、模型和基准资源以加速BL研究（例如公开可用的资源及所引述GitHub仓库中内容的更新）。
• 指令跟随和智能体/助手范式使零-shot任务和大语言模型的交互式生物分子知识检索成为可能。
• 该综述强调可解释性和泛化等开放挑战，并勾勒BL研究的未来方向。