Leveraging Biomolecule and Natural Language through Multi-Modal Learning: A Survey

2024-03-03

arXiv.org

AI Summary

This work addresses the fragmentation between biomolecular multimodal representation learning and natural language modeling. We propose the first systematic, unified multimodal learning framework integrating molecular sequences, 2D graphs, 3D structures, and textual descriptions. Methodologically, the framework synergistically combines molecular graph neural networks, 3D geometric deep learning, cross-modal alignment, and retrieval techniques, establishing an end-to-end paradigm spanning representation learning, modality alignment, fusion modeling, and downstream applications. Key contributions include: (1) a taxonomy of five core research directions; (2) integration of over ten critical benchmark datasets spanning diverse biomolecular tasks; and (3) a continuously updated, open-source knowledge repository (hosted on GitHub) providing reusable methodological guidelines, implementation resources, and community-maintained tools. By unifying heterogeneous biomolecular modalities with language-based reasoning, this work establishes a foundational methodological foundation for molecular intelligence in AI for Science.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

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 url{https://github.com/QizhiPei/Awesome-Biomolecule-Language-Cross-Modeling}.

Problem

• Surveying biomolecule and natural language integration via multi-modal learning
• Enhancing biomolecule understanding and property prediction through language data
• Analyzing advancements in cross-modeling biomolecules with natural language

Innovation

• Integrates biomolecular modeling with natural language processing
• Uses multi-modal learning to combine molecular and textual data
• Applies cross-modeling for biomolecule property prediction tasks