Learning Multi-view Molecular Representations with Structured and Unstructured Knowledge
MV-Mol employs multi-modal fusion with Q-Former, using two-stage pretraining to enhance molecular property prediction accuracy by 1.24%.
Key Findings
Methodology
MV-Mol utilizes a Q-Former-based architecture to fuse molecular structures encoded by Uni-Mol with textual prompts representing different views. The model undergoes a two-stage pretraining: first aligning noisy large-scale texts with molecular structures via contrastive learning to extract consensus features; second, incorporating high-quality structured knowledge from knowledge graphs through relation-based embeddings and contrastive objectives. The framework employs cross-modal contrastive and matching losses, enabling explicit view modeling and heterogenous knowledge integration, resulting in robust multi-view molecular representations.
Key Results
- MV-Mol achieves an average of 1.24% absolute improvement over SOTA Uni-Mol in molecular property prediction, demonstrating superior accuracy. In cross-modal retrieval tasks, it improves Top-1 accuracy by 12.9%, indicating enhanced multimodal understanding. The multi-view representations significantly benefit downstream tasks such as drug discovery and structure-activity relationship modeling, with stable performance across diverse datasets. Ablation studies confirm the importance of relation modeling and two-stage pretraining, validating the framework’s effectiveness.
Significance
This work pioneers explicit multi-view molecular representation learning by integrating structural, textual, and relational knowledge sources. It addresses longstanding challenges in heterogenous data fusion, providing a scalable, interpretable, and highly effective approach. The model’s ability to leverage multi-view information enhances the understanding of complex molecular functions, accelerating drug discovery, material design, and biomedical research. Its explicit view modeling sets a new standard for multi-modal AI in life sciences, bridging the gap between raw data and high-level molecular insights.
Technical Contribution
The core innovations include the adaptation of Q-Former for molecular multi-view fusion, a novel two-stage pretraining pipeline for heterogenous data, and the explicit modeling of views via relation-based prompts. The framework combines contrastive and generative objectives, enabling the model to learn rich, multi-faceted representations. It also introduces relation as a textual prompt, allowing the model to incorporate structured knowledge seamlessly. These contributions significantly advance the state-of-the-art in multi-modal molecular AI, offering new avenues for research and application.
Novelty
This is the first work to explicitly model multi-view molecular knowledge through view prompts and relation-based embeddings within a unified pretraining framework. Unlike prior methods that treat heterogeneous data sources implicitly or convert knowledge graphs into text, MV-Mol directly incorporates structured relations as views, enabling more precise and interpretable multi-modal fusion. Its two-stage pretraining strategy effectively handles data heterogeneity and quality imbalance, setting a new benchmark for multi-view molecular representation learning.
Limitations
- The model's reliance on large-scale multimodal datasets increases computational costs, limiting accessibility for smaller labs. The quality of knowledge graph relations directly impacts performance, and noisy or biased relations may introduce errors. In extremely noisy or imbalanced data environments, the model's robustness requires further validation. Future work should focus on reducing training costs, improving relation quality, and enhancing generalization to unseen molecular spaces.
Future Work
Future directions include optimizing the pretraining efficiency, exploring more sophisticated relation modeling techniques, and extending the framework to include additional modalities such as protein interactions. Enhancing robustness against noisy data and scaling to larger, more diverse molecular datasets are also key goals. Applying the model to real-world drug discovery pipelines and integrating experimental feedback will further validate its practical utility and accelerate biomedical breakthroughs.
AI Executive Summary
Understanding the properties and functions of molecules is fundamental to advancements in chemistry, biology, and materials science. Traditional approaches rely heavily on experimental assays, which are costly and time-consuming. Recent progress in molecular representation learning (MRL) leverages deep neural networks to extract knowledge from large unlabeled datasets, significantly accelerating discovery processes. However, existing models often focus on single modalities, such as structural data or text, and lack the capacity to explicitly model the multiple perspectives inherent in molecular knowledge.
This paper introduces MV-Mol, a novel multi-view molecular representation framework that explicitly models diverse perspectives—structural, textual, and relational—using a multi-modal fusion architecture based on Q-Former. The key innovation lies in the two-stage pretraining strategy: first, aligning noisy large-scale biomedical texts with molecular structures to extract consensus features; second, integrating high-quality structured knowledge from knowledge graphs via relation prompts. This approach ensures that the model captures both shared and complementary information across views, resulting in richer, more accurate molecular representations.
Experimental results demonstrate that MV-Mol surpasses state-of-the-art methods like Uni-Mol by 1.24% in molecular property prediction accuracy and improves cross-modal retrieval Top-1 accuracy by 12.9%. These improvements highlight its superior understanding of complex molecular data, which is crucial for applications such as drug discovery, target identification, and materials design. The model's ability to explicitly incorporate view-specific knowledge addresses a longstanding challenge in multi-modal AI, offering a scalable and interpretable solution.
Looking ahead, the authors plan to optimize training efficiency, improve the quality of structured knowledge integration, and expand the framework to include additional modalities like protein interactions. By bridging structural, textual, and relational information, MV-Mol sets a new standard for multi-view molecular AI, promising to accelerate innovation across scientific disciplines and industry sectors.
Deep Dive
Glossary
Q-Former (查询变换器)
一种多模态融合架构，通过交叉注意力机制提取不同模态的关键特征。技术上基于Transformer，能同时理解分子结构和文本提示。
用于MV-Mol中的视角建模和多模态特征融合。
Contrastive Learning (对比学习)
一种训练策略，通过最大化正样本对的相似度和最小化负样本对的相似度，增强模型对不同模态或视角的判别能力。
用于对齐分子结构与文本，提取共识特征。
Knowledge Graph Embedding (知识图谱嵌入)
将知识图谱中的实体和关系映射到连续向量空间，便于模型进行关系推理和知识融合。
在第二阶段预训练中引入结构化知识。
Multi-modal Fusion (多模态融合)
结合来自不同模态（如结构、文本、图谱）的信息，形成统一的表示。
MV-Mol的核心技术之一。
Pretraining (预训练)
在大量无标注数据上训练模型，学习通用特征，为下游任务提供基础。
MV-Mol采用两阶段预训练策略。
Open Questions Unanswered questions from this research
- 1 如何进一步提升模型在极端噪声环境下的鲁棒性，尤其是在知识图谱关系质量不佳时的表现仍需验证。未来需探索更高效的预训练方法以降低成本，同时增强模型对新颖或未见数据的泛化能力。
Applications
Immediate Applications
药物筛选
利用MV-Mol的多视角表示快速筛选潜在候选药物，结合结构和文本信息，提高筛选效率和准确性。
结构-功能关系分析
帮助科研人员理解分子结构与生物活性之间的关系，指导药物优化和新材料设计。
Long-term Vision
个性化医疗
结合多模态数据实现个体化药物方案定制，加快临床转化。
智能药物设计平台
构建全流程自动化药物设计系统，利用多模态模型实现从候选筛选到优化的全链路智能化。
Abstract
Capturing molecular knowledge with representation learning approaches holds significant potential in vast scientific fields such as chemistry and life science. An effective and generalizable molecular representation is expected to capture the consensus and complementary molecular expertise from diverse views and perspectives. However, existing works fall short in learning multi-view molecular representations, due to challenges in explicitly incorporating view information and handling molecular knowledge from heterogeneous sources. To address these issues, we present MV-Mol, a molecular representation learning model that harvests multi-view molecular expertise from chemical structures, unstructured knowledge from biomedical texts, and structured knowledge from knowledge graphs. We utilize text prompts to model view information and design a fusion architecture to extract view-based molecular representations. We develop a two-stage pre-training procedure, exploiting heterogeneous data of varying quality and quantity. Through extensive experiments, we show that MV-Mol provides improved representations that substantially benefit molecular property prediction. Additionally, MV-Mol exhibits state-of-the-art performance in multi-modal comprehension of molecular structures and texts. Code and data are available at https://github.com/PharMolix/OpenBioMed.